system

Abstract

The system according to the embodiment aims to simplify the process of making effective use of garbage to generate revenue. [Solution] A system according to an embodiment includes an analysis unit, a setting unit, a listing unit, and a display unit. The analysis unit analyzes photos. The setting unit sets prices based on information analyzed by the analysis unit. The listing unit lists items based on the prices set by the setting unit. The display unit displays the amount of waste reduction based on information about the items listed by the listing 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] With conventional technology, the process of effectively utilizing waste to generate revenue was complicated and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to simplify the process of making effective use of garbage to generate revenue. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a setting unit, a listing unit, and a display unit. The analysis unit analyzes photos. The setting unit sets prices based on information analyzed by the analysis unit. The listing unit lists items based on the prices set by the setting unit. The display unit displays the amount of waste reduction based on information about the items listed by the listing unit. [Effects of the Invention]

[0007] The system according to the embodiment can simplify the process of making effective use of garbage to generate revenue. [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) A system according to an embodiment of the present invention allows poor office workers to use their trash to open an e-commerce store. In this system, users take photos of their trash, and a generation AI analyzes the photos, automatically sets an appropriate price, and automatically lists the trash on Yahoo! Auctions or a flea market site. This allows users to sell their trash without spending time and contribute to reducing waste. For example, a user takes photos of unwanted home appliances or clothing, and a generation AI analyzes the photos and sets an appropriate price. The items are automatically listed on Yahoo! Auctions or a flea market site based on the set price. This allows users to list items without any effort. The generation AI also calculates the amount of waste reduction based on the information about the listed items and provides this to the user. This increases users' awareness of waste reduction. This system allows poor office workers to sell their trash without spending time and contributes to reducing waste.

[0029] An e-commerce store opening system according to an embodiment includes an analysis unit, a setting unit, a listing unit, and a display unit. The analysis unit analyzes a photo using a generation AI. For example, the analysis unit determines the type and condition of an item in the photo. The analysis unit can also use the generation AI to set a price based on the market value of the item in the photo. The setting unit sets a price based on information analyzed by the analysis unit. For example, the setting unit sets a price based on the market value of the item in the photo. The setting unit can also use the generation AI to adjust the pricing criteria. The listing unit lists an item based on the price set by the setting unit. For example, the listing unit collaborates with Yahoo! Auctions or a flea market site to automate the listing process. The listing unit can also use the generation AI to adjust the listing process flow. The display unit displays the amount of waste reduction based on information about the item listed by the listing unit. For example, the display unit calculates the amount of waste reduction based on information about the listed item and provides it to the user. The display unit can also adjust the display content using the generation AI. As a result, the EC shop opening system according to the embodiment can analyze the photo, set an appropriate price, list the item, and display the amount of waste reduction.

[0030] The EC store opening system includes a photography unit that takes photos. The photography unit allows users to take photos. For example, the photography unit can easily take photos using a smartphone or camera. The photography unit can also adjust the timing of taking photos using a generation AI. This allows users to take photos.

[0031] The listing unit can link with Yahoo! Auctions or a flea market site. The listing unit links with Yahoo! Auctions or a flea market site, for example, using an API. The listing unit can also automatically update listing information using a data synchronization method. In this way, by linking the listing unit with Yahoo! Auctions or a flea market site, listing is automated.

[0032] The display unit can calculate the amount of waste reduction based on information about the auctioned items and provide it to the user. The display unit, for example, calculates the amount of waste reduction based on information about the auctioned items. The display unit can also display the amount of waste reduction numerically using a reduction amount standard. In this way, the display unit can calculate the amount of waste reduction and provide it to the user, thereby raising environmental awareness.

[0033] The analysis unit can determine the type or condition of the item shown in the photo. For example, the analysis unit can use image recognition technology to determine the type of item shown in the photo. The analysis unit can also use a determination algorithm to determine the condition of the item. This allows the analysis unit to determine the type and condition of the item, making it possible to set an appropriate price.

[0034] The setting unit can set the price based on the market value of the item shown in the photograph. The setting unit can evaluate the market value of the item, for example, using past transaction data. The setting unit can also set the price taking into consideration the balance between supply and demand. This allows the setting unit to set the price based on market value, making it possible to set an appropriate price.

[0035] During analysis, the analysis unit can more accurately determine the condition based on the usage history of the item. The analysis unit determines the condition, for example, taking into account the frequency of use of the item. The analysis unit can also determine the condition taking into account the period of use of the item. In this way, by taking into account the usage history of the item, the condition can be determined more accurately.

[0036] During analysis, the analysis unit can automatically extract the year of manufacture and model information of the item and reflect this in the analysis. The analysis unit can automatically extract the year of manufacture of the item using, for example, image recognition technology. The analysis unit can also automatically extract the model information of the item by referencing a database. This automatically extracts the year of manufacture and model information of the item, improving the accuracy of the analysis.

[0037] The analysis unit can introduce technology for analyzing not only the appearance of an item but also its internal structure during analysis. For example, the analysis unit uses X-ray analysis to analyze the internal structure of an item. The analysis unit can also use CT scans to analyze the internal structure of an item in detail. This allows for more detailed analysis results to be provided by analyzing both the appearance and internal structure of an item.

[0038] The analysis unit can provide analysis results by taking into account the environmental load information of the product during analysis. For example, the analysis unit can provide analysis results by taking into account the environmental load during the manufacturing process of the product. The analysis unit can also provide analysis results by taking into account the environmental load when the product is used. In this way, by taking into account the environmental load information of the product, it is possible to provide analysis results that are environmentally friendly.

[0039] During analysis, the analysis unit can determine the condition of the item by taking into account the repair history of the item. For example, the analysis unit determines the condition by taking into account the number of repairs the item has undergone. The analysis unit can also determine the condition by taking into account the details of the repairs made to the item. In this way, by taking into account the repair history of the item, the condition can be determined more accurately.

[0040] The analysis unit can provide analysis results taking into account the brand value of the product during analysis. For example, the analysis unit provides analysis results taking into account the brand value of the product. The analysis unit can also provide analysis results taking into account the market evaluation of the brand of the product. In this way, by taking into account the brand value of the product, the analysis results become more accurate.

[0041] The price setting unit can set prices by reflecting market trends of the product in real time when setting the price. For example, the price setting unit obtains the market price of the product in real time and reflects this in the price setting. The price setting unit can also set prices by reflecting the balance between supply and demand of the product in real time. This allows appropriate price setting by reflecting market trends in real time.

[0042] The price setting unit can set the price based on the balance between supply and demand for the product when setting the price. For example, if the demand for the product is high, the price setting unit can raise the price. Also, if the supply of the product is high, the price setting unit can lower the price. This makes it possible to set an appropriate price by taking into account the balance between supply and demand.

[0043] The setting unit can set the price by referring to competing product information of the item when setting the price. For example, the setting unit sets the price by referring to the prices of competing products. The setting unit can also set the price by referring to the sales status of competing products. This makes it possible to set an appropriate price by referring to competing product information.

[0044] The setting unit can set the price taking into consideration the regional market value of the product when setting the price. For example, the setting unit sets the price taking into consideration the regional market value of the product. The setting unit can also set the price taking into consideration regional demand. This allows for appropriate price setting by taking into consideration the regional market value.

[0045] The setting unit can set the price taking into consideration the seasonality of the product when setting the price. For example, the setting unit sets the price taking into consideration the seasonality of the product. The setting unit can also set the price taking into consideration seasonal demand. This allows for appropriate price setting by taking into consideration seasonality.

[0046] When setting a price, the setting unit can set the price by referring to the sales history of the item. For example, the setting unit sets the price by referring to the past sales price of the item. The setting unit can also set the price by referring to the past sales quantity of the item. This makes it possible to set an appropriate price by referring to the sales history.

[0047] The listing section can enhance the function of automatically inputting detailed information about an item when it is listed. For example, the listing section can automatically extract and input detailed information from a photo of the item. The listing section can also automatically input detailed information by scanning the barcode of the item. This makes the listing process more efficient by automatically inputting detailed information about the item.

[0048] The listing unit can add a function to automatically optimize and post photos of items when listing them. For example, the listing unit automatically corrects and optimizes photos of items. The listing unit can also automatically remove and optimize backgrounds from photos of items. This allows for optimizing photos of items to make them more attractive for listing.

[0049] The listing department can implement a function to automatically generate a description of an item when the item is listed. For example, the listing department can automatically generate a description from a photo of the item. The listing department can also automatically generate a description based on detailed information about the item. This automatically generates a description of the item, making the listing process more efficient.

[0050] The listing unit can add a function to automatically suggest shipping options for an item when it is listed. The listing unit can suggest the optimal shipping option based on, for example, the size and weight of the item. The listing unit can also suggest the optimal shipping option based on the destination of the item. This automatically suggesting shipping options for the item streamlines the listing process.

[0051] The listing department can add a function to automatically suggest insurance options for an item when it is listed. For example, the listing department can suggest the optimal insurance option based on the value of the item. The listing department can also suggest the optimal insurance option based on the delivery risk of the item. This automatically suggesting insurance options for an item streamlines the listing process.

[0052] The listing unit can add a function to automatically suggest a sales promotion campaign for an item when the item is listed. For example, the listing unit can suggest an optimal sales promotion campaign based on the category of the item. The listing unit can also suggest an optimal sales promotion campaign based on the time of sale of the item. This automatically suggests a sales promotion campaign for the item, thereby streamlining the listing process.

[0053] The display unit can add a function to visually display the amount of waste reduction in an easy-to-understand manner. For example, the display unit displays the amount of waste reduction in a graph. The display unit can also display the amount of waste reduction as an icon. This visual display of the amount of waste reduction deepens the user's understanding.

[0054] The display unit can add a function that allows the amount of waste reduction to be compared with other users when displaying the amount. For example, the display unit displays the amount of waste reduction in comparison with other users in a ranking format. The display unit can also display the amount of waste reduction in comparison with other users in a graph. This increases the sense of competition by comparing the amount of waste reduction with other users.

[0055] The display unit can add a function to evaluate the environmental contribution based on the amount of waste reduction when displaying the information. For example, the display unit evaluates the environmental contribution numerically based on the amount of waste reduction. The display unit can also evaluate the environmental contribution graphically based on the amount of waste reduction. This increases the user's environmental awareness by evaluating the environmental contribution.

[0056] The display unit can add a function to display the amount of waste reduction by region when displaying. For example, the display unit displays the amount of waste reduction by region in a graph. The display unit can also display the amount of waste reduction by region using an icon. By displaying the amount of waste reduction by region, the contribution of each region can be seen.

[0057] The display unit can add a function to display the amount of waste reduction by time when displaying. For example, the display unit displays the amount of waste reduction by time in a graph. The display unit can also display the amount of waste reduction by time using icons. By displaying the amount of waste reduction by time, the degree of contribution by time can be understood.

[0058] The display unit can add a function to display the amount of waste reduction by item when displaying. For example, the display unit displays the amount of waste reduction by item in a graph. The display unit can also display the amount of waste reduction by item as an icon. By displaying the amount of waste reduction by item, the contribution of each item can be seen.

[0059] The photographing unit can be added with a function to photograph not only the entire image of an item but also the detailed parts when photographing. For example, the photographing unit photographs the entire image and the detailed parts of the item at the same time. The photographing unit can also zoom in and photograph the detailed parts of the item. This allows more detailed information to be provided by photographing the entire image and the detailed parts of the item.

[0060] The photographing unit can be added with a function to automatically measure the size and weight of an item when photographing. For example, the photographing unit can automatically measure and display the size of the item. The photographing unit can also automatically measure and display the weight of the item. This makes it possible to provide more detailed information by automatically measuring the size and weight of the item.

[0061] The photographing unit may be added with a function to automatically remove the background of an object when photographing. For example, the photographing unit may automatically remove the background of an object before photographing. The photographing unit may also remove the background of an object and photograph only the object while emphasizing it. In this way, by removing the background of the object, it is possible to photograph the object while emphasizing its details.

[0062] The photographing unit can be added with a function to automatically correct the color and shape of an object when photographing. For example, the photographing unit automatically corrects the color of the object before photographing it. The photographing unit can also automatically correct the shape of the object before photographing it. This allows for more accurate information to be provided by correcting the color and shape of the object.

[0063] The photographing unit can be added with a function to automatically adjust the photographing angle of the item when photographing. For example, the photographing unit automatically adjusts the optimum photographing angle of the item and photographs it. The photographing unit can also adjust the photographing angle of the item multiple times and photograph it. This allows for more appropriate photographing by adjusting the photographing angle of the item.

[0064] The photographing unit can be added with a function to automatically optimize the photographing environment of an item when photographing. For example, the photographing unit automatically optimizes the photographing environment of the item and photographs it. The photographing unit can also adjust the photographing environment of the item and photograph it in the optimal condition. This allows for more appropriate photographing by optimizing the photographing environment of the item.

[0065] The linking unit can add a function to automatically obtain the latest information from Yahoo! Auctions and flea market sites when linking. For example, the linking unit automatically obtains the latest information from Yahoo! Auctions and flea market sites and links. The linking unit can also obtain the latest information in real time and link. This allows for more appropriate linking by automatically obtaining the latest information.

[0066] The linking unit can add a function to automatically update the listing information when linking. For example, the linking unit automatically updates the listing information to link. The linking unit can also update the listing information in real time to link. This allows for more appropriate linking by automatically updating the listing information.

[0067] The linking unit can add a function of synchronizing the listing information in real time during linking. For example, the linking unit synchronizes the listing information in real time. The linking unit can also periodically synchronize the listing information. This allows for more appropriate linking by synchronizing the listing information in real time.

[0068] The linking unit can add a function to link with other EC sites when linking. For example, the linking unit can automatically link with other EC sites. The linking unit can also link with other EC sites in real time. This allows for a wider range of listings to be made by linking with other EC sites.

[0069] The linking unit can add a function to make the listing information multilingual during linking. For example, the linking unit automatically translates the listing information into multiple languages ​​and links it. The linking unit can also translate the listing information into multiple languages ​​based on the user's language setting and link it. This makes the listing information multilingual, making it possible to accommodate a wider range of users.

[0070] The linking unit can add a function to automatically share listing information on SNS when linking. For example, the linking unit automatically shares listing information on SNS. The linking unit can also automatically share listing information based on the user's SNS account. This allows information to reach more users by sharing listing information on SNS.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] During analysis, the analysis unit can more accurately determine the condition based on the usage history of the item. For example, the condition can be determined taking into account the frequency of use of the item. The condition can also be determined taking into account the period of use of the item. Furthermore, the condition can be determined more accurately by taking into account the environment in which the item is used. As a result, the condition can be determined more accurately by taking into account the usage history of the item.

[0073] During analysis, the analysis unit can automatically extract the year of manufacture and model information of the item and reflect this in the analysis. For example, the year of manufacture of the item can be automatically extracted using image recognition technology. Also, the model information of the item can be automatically extracted using database reference. Furthermore, the accuracy of the analysis can be improved by taking into account the place and method of manufacture of the item. This improves the accuracy of the analysis by automatically extracting the year of manufacture and model information of the item.

[0074] During analysis, the analysis unit can introduce technology to analyze not only the appearance of an item but also its internal structure. For example, X-ray analysis can be used to analyze the internal structure of an item. CT scans can also be used to analyze the internal structure in detail. Furthermore, ultrasonic analysis can be used to analyze the internal structure of an item non-destructively. This allows for more detailed analysis results to be provided by analyzing both the appearance and internal structure of an item.

[0075] The analysis unit can provide analysis results by taking into account the environmental load information of the item during analysis. For example, the analysis results can be provided by taking into account the environmental load during the manufacturing process of the item. The analysis results can also be provided by taking into account the environmental load when the item is used. Furthermore, by taking into account the environmental load when the item is disposed of, it is possible to provide analysis results that take into account the environment. In this way, by taking into account the environmental load information of the item, it is possible to provide analysis results that take into account the environment.

[0076] During analysis, the analysis unit can determine the condition by taking into account the repair history of the item. For example, the condition can be determined by taking into account the number of times the item has been repaired. The condition can also be determined by taking into account the details of the repairs to the item. Furthermore, by taking into account the quality of the parts used to repair the item, the condition can be determined more accurately. Thus, by taking into account the repair history of the item, the condition can be determined more accurately.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The analysis unit uses generative AI to analyze the photo. Specifically, it determines the type and condition of the item in the photo and can even set a price based on the item's market value. Step 2: The setting unit sets the price based on the information analyzed by the analysis unit. Specifically, the price is set based on the market value of the items in the photo, and the generation AI can also be used to adjust the pricing criteria. Step 3: The listing department lists the item based on the price set by the setting department. Specifically, it can link with Yahoo! Auctions and flea market sites to automate the listing process and use generation AI to adjust the flow of the listing process. Step 4: The display unit displays the amount of waste reduction based on the information of the product listed by the listing unit. Specifically, the display unit calculates the amount of waste reduction based on the information of the listed product and provides it to the user, and can also adjust the display content using generation AI.

[0079] (Example 2) A system according to an embodiment of the present invention allows poor office workers to use their trash to open an e-commerce store. In this system, users take photos of their trash, and a generation AI analyzes the photos, automatically sets an appropriate price, and automatically lists the trash on Yahoo! Auctions or a flea market site. This allows users to sell their trash without spending time and contribute to reducing waste. For example, a user takes photos of unwanted home appliances or clothing, and a generation AI analyzes the photos and sets an appropriate price. The items are automatically listed on Yahoo! Auctions or a flea market site based on the set price. This allows users to list items without any effort. The generation AI also calculates the amount of waste reduction based on the information about the listed items and provides this to the user. This increases users' awareness of waste reduction. This system allows poor office workers to sell their trash without spending time and contributes to reducing waste.

[0080] An e-commerce store opening system according to an embodiment includes an analysis unit, a setting unit, a listing unit, and a display unit. The analysis unit analyzes a photo using a generation AI. For example, the analysis unit determines the type and condition of an item in the photo. The analysis unit can also use the generation AI to set a price based on the market value of the item in the photo. The setting unit sets a price based on information analyzed by the analysis unit. For example, the setting unit sets a price based on the market value of the item in the photo. The setting unit can also use the generation AI to adjust the pricing criteria. The listing unit lists an item based on the price set by the setting unit. For example, the listing unit collaborates with Yahoo! Auctions or a flea market site to automate the listing process. The listing unit can also use the generation AI to adjust the listing process flow. The display unit displays the amount of waste reduction based on information about the item listed by the listing unit. For example, the display unit calculates the amount of waste reduction based on information about the listed item and provides it to the user. The display unit can also adjust the display content using the generation AI. As a result, the EC shop opening system according to the embodiment can analyze the photo, set an appropriate price, list the item, and display the amount of waste reduction.

[0081] The EC store opening system includes a photography unit that takes photos. The photography unit allows users to take photos. For example, the photography unit can easily take photos using a smartphone or camera. The photography unit can also adjust the timing of taking photos using a generation AI. This allows users to take photos.

[0082] The listing unit can link with Yahoo! Auctions or a flea market site. The listing unit links with Yahoo! Auctions or a flea market site, for example, using an API. The listing unit can also automatically update listing information using a data synchronization method. In this way, by linking the listing unit with Yahoo! Auctions or a flea market site, listing is automated.

[0083] The display unit can calculate the amount of waste reduction based on information about the auctioned items and provide it to the user. The display unit, for example, calculates the amount of waste reduction based on information about the auctioned items. The display unit can also display the amount of waste reduction numerically using a reduction amount standard. In this way, the display unit can calculate the amount of waste reduction and provide it to the user, thereby raising environmental awareness.

[0084] The analysis unit can determine the type or condition of the item shown in the photo. For example, the analysis unit can use image recognition technology to determine the type of item shown in the photo. The analysis unit can also use a determination algorithm to determine the condition of the item. This allows the analysis unit to determine the type and condition of the item, making it possible to set an appropriate price.

[0085] The setting unit can set the price based on the market value of the item shown in the photograph. The setting unit can evaluate the market value of the item, for example, using past transaction data. The setting unit can also set the price taking into consideration the balance between supply and demand. This allows the setting unit to set the price based on market value, making it possible to set an appropriate price.

[0086] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, emotion recognition technology. The analysis unit can also adjust the accuracy of the analysis based on the user's emotion using an estimation algorithm. This allows the analysis accuracy to be adjusted according to the user's emotion, making it possible to provide more appropriate analysis results.

[0087] During analysis, the analysis unit can more accurately determine the condition based on the usage history of the item. The analysis unit determines the condition, for example, taking into account the frequency of use of the item. The analysis unit can also determine the condition taking into account the period of use of the item. In this way, by taking into account the usage history of the item, the condition can be determined more accurately.

[0088] During analysis, the analysis unit can automatically extract the year of manufacture and model information of the item and reflect this in the analysis. The analysis unit can automatically extract the year of manufacture of the item using, for example, image recognition technology. The analysis unit can also automatically extract the model information of the item by referencing a database. This automatically extracts the year of manufacture and model information of the item, improving the accuracy of the analysis.

[0089] The analysis unit can introduce technology for analyzing not only the appearance of an item but also its internal structure during analysis. For example, the analysis unit uses X-ray analysis to analyze the internal structure of an item. The analysis unit can also use CT scans to analyze the internal structure of an item in detail. This allows for more detailed analysis results to be provided by analyzing both the appearance and internal structure of an item.

[0090] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, emotion recognition technology. The analysis unit can also adjust the display method of the analysis results based on the user's emotion using an estimation algorithm. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotion.

[0091] The analysis unit can provide analysis results by taking into account the environmental load information of the product during analysis. For example, the analysis unit can provide analysis results by taking into account the environmental load during the manufacturing process of the product. The analysis unit can also provide analysis results by taking into account the environmental load when the product is used. In this way, by taking into account the environmental load information of the product, it is possible to provide analysis results that are environmentally friendly.

[0092] During analysis, the analysis unit can determine the condition of the item by taking into account the repair history of the item. For example, the analysis unit determines the condition by taking into account the number of repairs the item has undergone. The analysis unit can also determine the condition by taking into account the details of the repairs made to the item. In this way, by taking into account the repair history of the item, the condition can be determined more accurately.

[0093] The analysis unit can provide analysis results taking into account the brand value of the product during analysis. For example, the analysis unit provides analysis results taking into account the brand value of the product. The analysis unit can also provide analysis results taking into account the market evaluation of the brand of the product. In this way, by taking into account the brand value of the product, the analysis results become more accurate.

[0094] The setting unit can estimate the user's emotion and adjust the price setting criteria based on the estimated user's emotion. The setting unit can estimate the user's emotion using, for example, emotion recognition technology. The setting unit can also adjust the price setting criteria based on the user's emotion using an estimation algorithm. This allows for more appropriate price setting by adjusting the price setting criteria according to the user's emotion.

[0095] The price setting unit can set prices by reflecting market trends of the product in real time when setting the price. For example, the price setting unit obtains the market price of the product in real time and reflects this in the price setting. The price setting unit can also set prices by reflecting the balance between supply and demand of the product in real time. This allows appropriate price setting by reflecting market trends in real time.

[0096] The price setting unit can set the price based on the balance between supply and demand for the product when setting the price. For example, if the demand for the product is high, the price setting unit can raise the price. Also, if the supply of the product is high, the price setting unit can lower the price. This makes it possible to set an appropriate price by taking into account the balance between supply and demand.

[0097] The setting unit can set the price by referring to competing product information of the item when setting the price. For example, the setting unit sets the price by referring to the prices of competing products. The setting unit can also set the price by referring to the sales status of competing products. This makes it possible to set an appropriate price by referring to competing product information.

[0098] The setting unit can estimate the user's emotions and determine the priority of pricing based on the estimated user's emotions. The setting unit can estimate the user's emotions using, for example, emotion recognition technology. The setting unit can also determine the priority of pricing based on the user's emotions using an estimation algorithm. This enables more appropriate price setting by determining the priority of pricing according to the user's emotions.

[0099] The setting unit can set the price taking into consideration the regional market value of the product when setting the price. For example, the setting unit sets the price taking into consideration the regional market value of the product. The setting unit can also set the price taking into consideration regional demand. This allows for appropriate price setting by taking into consideration the regional market value.

[0100] The setting unit can set the price taking into consideration the seasonality of the product when setting the price. For example, the setting unit sets the price taking into consideration the seasonality of the product. The setting unit can also set the price taking into consideration seasonal demand. This allows for appropriate price setting by taking into consideration seasonality.

[0101] When setting a price, the setting unit can set the price by referring to the sales history of the item. For example, the setting unit sets the price by referring to the past sales price of the item. The setting unit can also set the price by referring to the past sales quantity of the item. This makes it possible to set an appropriate price by referring to the sales history.

[0102] The listing unit can estimate the user's emotions and adjust the flow of the listing procedure based on the estimated user's emotions. The listing unit can estimate the user's emotions using, for example, emotion recognition technology. The listing unit can also adjust the flow of the listing procedure based on the user's emotions using an estimation algorithm. This allows for more appropriate listing by adjusting the flow of the listing procedure according to the user's emotions.

[0103] The listing section can enhance the function of automatically inputting detailed information about an item when it is listed. For example, the listing section can automatically extract and input detailed information from a photo of the item. The listing section can also automatically input detailed information by scanning the barcode of the item. This makes the listing process more efficient by automatically inputting detailed information about the item.

[0104] The listing unit can add a function to automatically optimize and post photos of items when listing them. For example, the listing unit automatically corrects and optimizes photos of items. The listing unit can also automatically remove and optimize backgrounds from photos of items. This allows for optimizing photos of items to make them more attractive for listing.

[0105] The listing department can implement a function to automatically generate a description of an item when the item is listed. For example, the listing department can automatically generate a description from a photo of the item. The listing department can also automatically generate a description based on detailed information about the item. This automatically generates a description of the item, making the listing process more efficient.

[0106] The selling unit can estimate the user's emotions and determine the priority of items to be sold based on the estimated user's emotions. The selling unit can estimate the user's emotions using, for example, emotion recognition technology. The selling unit can also determine the priority of items to be sold based on the user's emotions using an estimation algorithm. This allows for more appropriate listing by determining the priority of items to be sold based on the user's emotions.

[0107] The listing unit can add a function to automatically suggest shipping options for an item when it is listed. The listing unit can suggest the optimal shipping option based on, for example, the size and weight of the item. The listing unit can also suggest the optimal shipping option based on the destination of the item. This automatically suggesting shipping options for the item streamlines the listing process.

[0108] The listing department can add a function to automatically suggest insurance options for an item when it is listed. For example, the listing department can suggest the optimal insurance option based on the value of the item. The listing department can also suggest the optimal insurance option based on the delivery risk of the item. This automatically suggesting insurance options for an item streamlines the listing process.

[0109] The listing unit can add a function to automatically suggest a sales promotion campaign for an item when the item is listed. For example, the listing unit can suggest an optimal sales promotion campaign based on the category of the item. The listing unit can also suggest an optimal sales promotion campaign based on the time of sale of the item. This automatically suggests a sales promotion campaign for the item, thereby streamlining the listing process.

[0110] The display unit can estimate the user's emotion and adjust the display content based on the estimated user's emotion. The display unit estimates the user's emotion using, for example, emotion recognition technology. The display unit can also adjust the display content based on the user's emotion using an estimation algorithm. This allows for a more appropriate display by adjusting the display content according to the user's emotion.

[0111] The display unit can add a function to visually display the amount of waste reduction in an easy-to-understand manner. For example, the display unit displays the amount of waste reduction in a graph. The display unit can also display the amount of waste reduction as an icon. This visual display of the amount of waste reduction deepens the user's understanding.

[0112] The display unit can add a function that allows the amount of waste reduction to be compared with other users when displaying the amount. For example, the display unit displays the amount of waste reduction in comparison with other users in a ranking format. The display unit can also display the amount of waste reduction in comparison with other users in a graph. This increases the sense of competition by comparing the amount of waste reduction with other users.

[0113] The display unit can add a function to evaluate the environmental contribution based on the amount of waste reduction when displaying the information. For example, the display unit evaluates the environmental contribution numerically based on the amount of waste reduction. The display unit can also evaluate the environmental contribution graphically based on the amount of waste reduction. This increases the user's environmental awareness by evaluating the environmental contribution.

[0114] The display unit can estimate the user's emotion and determine the display priority based on the estimated user's emotion. The display unit can estimate the user's emotion using, for example, emotion recognition technology. The display unit can also determine the display priority based on the user's emotion using an estimation algorithm. This allows for more appropriate display by determining the display priority according to the user's emotion.

[0115] The display unit can add a function to display the amount of waste reduction by region when displaying. For example, the display unit displays the amount of waste reduction by region in a graph. The display unit can also display the amount of waste reduction by region using an icon. By displaying the amount of waste reduction by region, the contribution of each region can be seen.

[0116] The display unit can add a function to display the amount of waste reduction by time when displaying. For example, the display unit displays the amount of waste reduction by time in a graph. The display unit can also display the amount of waste reduction by time using icons. By displaying the amount of waste reduction by time, the degree of contribution by time can be understood.

[0117] The display unit can add a function to display the amount of waste reduction by item when displaying. For example, the display unit displays the amount of waste reduction by item in a graph. The display unit can also display the amount of waste reduction by item as an icon. By displaying the amount of waste reduction by item, the contribution of each item can be seen.

[0118] The image capturing unit can estimate the user's emotion and adjust the timing of capturing images based on the estimated user's emotion. The image capturing unit can estimate the user's emotion using, for example, emotion recognition technology. The image capturing unit can also adjust the timing of capturing images based on the user's emotion using an estimation algorithm. This allows for more appropriate capturing by adjusting the timing of capturing images according to the user's emotion.

[0119] The photographing unit can be added with a function to photograph not only the entire image of an item but also the detailed parts when photographing. For example, the photographing unit photographs the entire image and the detailed parts of the item at the same time. The photographing unit can also zoom in and photograph the detailed parts of the item. This allows more detailed information to be provided by photographing the entire image and the detailed parts of the item.

[0120] The photographing unit can be added with a function to automatically measure the size and weight of an item when photographing. For example, the photographing unit can automatically measure and display the size of the item. The photographing unit can also automatically measure and display the weight of the item. This makes it possible to provide more detailed information by automatically measuring the size and weight of the item.

[0121] The photographing unit may be added with a function to automatically remove the background of an object when photographing. For example, the photographing unit may automatically remove the background of an object before photographing. The photographing unit may also remove the background of an object and photograph only the object while emphasizing it. In this way, by removing the background of the object, it is possible to photograph the object while emphasizing its details.

[0122] The image capturing unit can estimate the user's emotions and determine the priority of image capturing based on the estimated user's emotions. The image capturing unit can estimate the user's emotions using, for example, emotion recognition technology. The image capturing unit can also determine the priority of image capturing based on the user's emotions using an estimation algorithm. This allows for more appropriate image capturing by determining the priority of image capturing based on the user's emotions.

[0123] The photographing unit can be added with a function to automatically correct the color and shape of an object when photographing. For example, the photographing unit automatically corrects the color of the object before photographing it. The photographing unit can also automatically correct the shape of the object before photographing it. This allows for more accurate information to be provided by correcting the color and shape of the object.

[0124] The photographing unit can be added with a function to automatically adjust the photographing angle of the item when photographing. For example, the photographing unit automatically adjusts the optimum photographing angle of the item and photographs it. The photographing unit can also adjust the photographing angle of the item multiple times and photograph it. This allows for more appropriate photographing by adjusting the photographing angle of the item.

[0125] The photographing unit can be added with a function to automatically optimize the photographing environment of an item when photographing. For example, the photographing unit automatically optimizes the photographing environment of the item and photographs it. The photographing unit can also adjust the photographing environment of the item and photograph it in the optimal condition. This allows for more appropriate photographing by optimizing the photographing environment of the item.

[0126] The linking unit can estimate the user's emotion and adjust the timing of linking based on the estimated user's emotion. The linking unit estimates the user's emotion using, for example, emotion recognition technology. The linking unit can also adjust the timing of linking based on the user's emotion using an estimation algorithm. This allows for more appropriate linking by adjusting the timing of linking according to the user's emotion.

[0127] The linking unit can add a function to automatically obtain the latest information from Yahoo! Auctions and flea market sites when linking. For example, the linking unit automatically obtains the latest information from Yahoo! Auctions and flea market sites and links. The linking unit can also obtain the latest information in real time and link. This allows for more appropriate linking by automatically obtaining the latest information.

[0128] The linking unit can add a function to automatically update the listing information when linking. For example, the linking unit automatically updates the listing information to link. The linking unit can also update the listing information in real time to link. This allows for more appropriate linking by automatically updating the listing information.

[0129] The linking unit can add a function of synchronizing the listing information in real time during linking. For example, the linking unit synchronizes the listing information in real time. The linking unit can also periodically synchronize the listing information. This allows for more appropriate linking by synchronizing the listing information in real time.

[0130] The collaboration unit can estimate the user's emotion and determine the priority of collaboration based on the estimated user's emotion. The collaboration unit can estimate the user's emotion using, for example, emotion recognition technology. The collaboration unit can also determine the priority of collaboration based on the user's emotion using an estimation algorithm. This enables more appropriate collaboration by determining the priority of collaboration according to the user's emotion.

[0131] The linking unit can add a function to link with other EC sites when linking. For example, the linking unit can automatically link with other EC sites. The linking unit can also link with other EC sites in real time. This allows for a wider range of listings to be made by linking with other EC sites.

[0132] The linking unit can add a function to make the listing information multilingual during linking. For example, the linking unit automatically translates the listing information into multiple languages ​​and links it. The linking unit can also translate the listing information into multiple languages ​​based on the user's language setting and link it. This makes the listing information multilingual, making it possible to accommodate a wider range of users.

[0133] The linking unit can add a function to automatically share listing information on SNS when linking. For example, the linking unit automatically shares listing information on SNS. The linking unit can also automatically share listing information based on the user's SNS account. This allows information to reach more users by sharing listing information on SNS. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, setting unit, listing unit, display unit, and photographing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 analyzing a photo taken by the camera 42 of the smart device 14. The setting unit is realized by the specific processing unit 290 of the data processing device 12 setting a price. The listing unit is realized by the specific processing unit 290 of the data processing device 12 performing a listing procedure on Yahoo! Auctions or a flea market site. The display unit is realized by the display 40A of the smart device 14 displaying the amount of waste reduction. The photographing unit is realized by the camera 42 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, setting unit, listing unit, display unit, and photographing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 analyzing a photo taken by the camera 42 of the smart glasses 214. The setting unit is realized by the specific processing unit 290 of the data processing device 12 setting a price. The listing unit is realized by the specific processing unit 290 of the data processing device 12 performing a listing procedure on Yahoo! Auctions or a flea market site. The display unit is realized by the display of the smart glasses 214 displaying the amount of waste reduction. The photographing unit is realized by the camera 42 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, setting unit, listing unit, display unit, and photographing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 analyzing a photo taken by the camera 42 of the headset type terminal 314. The setting unit is realized by the specific processing unit 290 of the data processing device 12 setting a price. The listing unit is realized by the specific processing unit 290 of the data processing device 12 performing a listing procedure on Yahoo! Auctions or a flea market site. The display unit is realized by the display 343 of the headset type terminal 314 displaying the amount of waste reduction. The photographing unit is realized by the camera 42 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, setting unit, listing unit, display unit, and photographing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 analyzing a photo taken by the camera 42 of the robot 414. The setting unit is realized by the specific processing unit 290 of the data processing device 12 setting a price. The listing unit is realized by the specific processing unit 290 of the data processing device 12 performing a listing procedure on Yahoo! Auctions or a flea market site. The display unit is realized by the display of the robot 414 displaying the amount of waste reduction. The photographing unit is realized by the camera 42 of the robot 414.

[0134] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0135] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can increase the accuracy of the analysis to reduce the burden on the user. If the user is relaxed, the analysis unit can return the analysis accuracy to the normal setting. Furthermore, if the user is excited, the analysis unit can temporarily lower the accuracy of the analysis to perform analysis that takes the user's emotions into consideration.

[0136] The setting unit can estimate the user's emotions and adjust the price setting criteria based on the estimated user emotions. For example, if the user feels anxious, the setting unit can set the price lower to increase the user's sense of security. If the user feels confident, the setting unit can meet the user's expectations by setting the price higher. Furthermore, if the user is unsure, the setting unit can support the user's decision by setting the price in the middle.

[0137] The listing unit can estimate the user's emotions and adjust the flow of the listing procedure based on the estimated user's emotions. For example, if the user is in a hurry, the listing unit can simplify the procedure to reduce the user's burden. If the user is calm, the listing unit can return the procedure to the normal flow. Furthermore, if the user is excited, the listing unit can temporarily delay the procedure to list the item in consideration of the user's emotions.

[0138] The display unit can estimate the user's emotions and adjust the display content based on the estimated user's emotions. For example, if the user feels anxious, the display unit can display information in a concise manner to increase the user's sense of security. If the user feels excited, the display unit can display information in detail to keep the user interested. If the user feels relaxed, the display unit can display information in a normal format.

[0139] The image capturing unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated user emotions. For example, if the user is nervous, the image capturing unit can delay the timing of capturing images to encourage the user to relax. Also, if the user is relaxed, the image capturing unit can return the timing of capturing images to the normal setting. Furthermore, if the user is excited, the image capturing unit can temporarily advance the timing of capturing images to take the user's emotions into consideration.

[0140] During analysis, the analysis unit can more accurately determine the condition based on the usage history of the item. For example, the condition can be determined taking into account the frequency of use of the item. The condition can also be determined taking into account the period of use of the item. Furthermore, the condition can be determined more accurately by taking into account the environment in which the item is used. As a result, the condition can be determined more accurately by taking into account the usage history of the item.

[0141] During analysis, the analysis unit can automatically extract the year of manufacture and model information of the item and reflect this in the analysis. For example, the year of manufacture of the item can be automatically extracted using image recognition technology. Also, the model information of the item can be automatically extracted using database reference. Furthermore, the accuracy of the analysis can be improved by taking into account the place and method of manufacture of the item. This improves the accuracy of the analysis by automatically extracting the year of manufacture and model information of the item.

[0142] During analysis, the analysis unit can introduce technology to analyze not only the appearance of an item but also its internal structure. For example, X-ray analysis can be used to analyze the internal structure of an item. CT scans can also be used to analyze the internal structure in detail. Furthermore, ultrasonic analysis can be used to analyze the internal structure of an item non-destructively. This allows for more detailed analysis results to be provided by analyzing both the appearance and internal structure of an item.

[0143] The analysis unit can provide analysis results by taking into account the environmental load information of the item during analysis. For example, the analysis results can be provided by taking into account the environmental load during the manufacturing process of the item. The analysis results can also be provided by taking into account the environmental load when the item is used. Furthermore, by taking into account the environmental load when the item is disposed of, it is possible to provide analysis results that take into account the environment. In this way, by taking into account the environmental load information of the item, it is possible to provide analysis results that take into account the environment.

[0144] During analysis, the analysis unit can determine the condition by taking into account the repair history of the item. For example, the condition can be determined by taking into account the number of times the item has been repaired. The condition can also be determined by taking into account the details of the repairs to the item. Furthermore, by taking into account the quality of the parts used to repair the item, the condition can be determined more accurately. Thus, by taking into account the repair history of the item, the condition can be determined more accurately.

[0145] The processing flow of the second embodiment will be briefly explained below.

[0146] Step 1: The analysis unit uses generative AI to analyze the photo. Specifically, it determines the type and condition of the item in the photo and can even set a price based on the item's market value. Step 2: The setting unit sets the price based on the information analyzed by the analysis unit. Specifically, the price is set based on the market value of the items in the photo, and the generation AI can also be used to adjust the pricing criteria. Step 3: The listing department lists the item based on the price set by the setting department. Specifically, it can link with Yahoo! Auctions and flea market sites to automate the listing process and use generation AI to adjust the flow of the listing process. Step 4: The display unit displays the amount of waste reduction based on the information of the product listed by the listing unit. Specifically, the display unit calculates the amount of waste reduction based on the information of the listed product and provides it to the user, and can also adjust the display content using generation AI.

[0147] 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.

[0148] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0152] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0168] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0198] 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.

[0199] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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."

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Explanation of symbols]

[0219] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the photograph; a setting unit that sets a price based on the information analyzed by the analysis unit; a listing unit that lists items for sale based on the price set by the setting unit; a display unit that displays the amount of waste reduction based on information about the product exhibited by the exhibit unit; Equipped with A system characterized by:

2. Equipped with a photographing unit for taking photos 2. The system of claim 1.

3. The exhibition section Link with Yahoo! Auctions or flea market sites 2. The system of claim 1.

4. The display unit Calculate the amount of waste reduction based on the information of the listed items and provide it to the user 2. The system of claim 1.

5. The analysis unit Determine the type or condition of the item in the photograph 2. The system of claim 1.

6. The setting unit Set a price based on the market value of the items in the photo 2. The system of claim 1.

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

2. The system of claim 1.

8. The analysis unit During analysis, the condition of the item is more accurately determined based on its usage history.

2. The system of claim 1.

9. The analysis unit During analysis, information such as the manufacturing year and model of the item is automatically extracted and reflected in the analysis.

2. The system of claim 1.

Images