System

Abstract

An object of a system according to an embodiment is to translate commodity transaction information into a plurality of languages and smoothly perform a transaction.SOLUTION: A system according to an embodiment includes an input unit, an analysis unit, a translation unit, and a display unit. The input unit inputs commodity transaction information. The analysis unit analyzes the information input by the input unit. The translation unit translates the information analyzed by the analysis unit. The display unit displays the information translated by the translation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a language barrier when it came to import and sales services for individuals, which made it difficult for transactions to be carried out smoothly.

[0005] The system according to the embodiment aims to translate product transaction information into multiple languages ​​and facilitate transactions. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a translation unit, and a display unit. The input unit inputs commodity trading information. The analysis unit analyzes the information input by the input unit. The translation unit translates the information analyzed by the analysis unit. The display unit displays the information translated by the translation unit. [Effects of the Invention]

[0007] The system according to the embodiment can translate product transaction information into multiple languages, enabling smooth transactions. [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 flea market system according to an embodiment of the present invention allows users to enter product transaction information into a single form, translating it into languages ​​around the world and listing the items for sale. The flea market system includes an input unit for entering product transaction information, an analysis unit for analyzing the entered information, a translation unit for translating the analyzed information, and a display unit for displaying the translated information. For example, in the flea market system, a user enters information such as the product name, description, price, and shipping method. A generation AI then analyzes this information and translates it into multiple languages. For example, the information is translated into major languages ​​such as English, Chinese, and Spanish. The translated information is displayed in a format that is easy for users in each country to understand. Furthermore, the flea market system provides an in-app translation function for users living in Japan who have difficulty speaking Japanese. For example, the generation AI translates information entered by a user in Japanese and converts it into other languages ​​in real time. This allows foreign users to easily conduct transactions. This makes it easier for individuals to buy and sell products, promoting international transactions. It also lowers transaction barriers for foreign users living in Japan, potentially increasing transaction volumes.

[0029] The flea market system according to the embodiment includes an input unit, an analysis unit, a translation unit, and a display unit. The input unit allows a user to input product transaction information. The product transaction information includes, but is not limited to, product names, descriptions, prices, and delivery methods. For example, the input unit allows a user to input product names and descriptions in text format. The input unit can also input product transaction information using voice input or image recognition. For example, a user can simply input a product name and description by voice, which is converted into text. Furthermore, when a user uploads a photo of a product, product information is automatically input using image recognition technology. The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, a data analysis method or an analysis algorithm, but is not limited to, examples. For example, the analysis unit analyzes the input product names and descriptions and classifies them into appropriate categories. The analysis unit can also analyze the input price and delivery method and propose optimal transaction terms. The translation unit translates the information analyzed by the analysis unit. The translation is performed based on, for example, the translation engine used and the translation accuracy, but is not limited to, examples. For example, the translation unit uses a generation AI to translate input information into multiple languages. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI translates into major languages ​​such as English, Chinese, and Spanish. The translation unit may also improve the accuracy of the translation by taking into account technical terms and industry jargon. The display unit displays the information translated by the translation unit. The display may be performed based on, for example, a display format or a display device, but is not limited to, for example. For example, the display unit displays the translated information in a format that is easy for users in each country to understand. The display unit may also provide an optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, it may provide a display method tailored to the screen size. As a result, the flea market system according to the embodiment can translate product transaction information into multiple languages ​​and promote international transactions.

[0030] The flea market system includes a real-time translation unit that translates information entered by a user in Japanese in real time and converts it into another language. The real-time translation unit translates information entered by a user in Japanese in real time and converts it into another language. Real-time translation is performed based on, for example, the translation delay time and the technology used, but is not limited to these examples. For example, the real-time translation unit uses a generation AI to translate information entered by a user in Japanese in real time. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the generation AI translates product names and descriptions entered by a user in Japanese into English, Chinese, Spanish, etc. in real time. The real-time translation unit can also estimate the user's emotions and adjust the translation expression based on the estimated user's emotions. For example, if the user is relaxed, it provides a careful and detailed translation. This allows real-time translation to be performed, making it easy for foreign users to conduct transactions.

[0031] The input unit can provide an auto-completion function by referring to the user's past input history when entering product transaction information. For example, the input unit can automatically display product names and descriptions previously entered by the user as candidates. The input unit can also automatically display prices and delivery methods previously entered by the user as candidates. The input unit can also predict and automatically complete product information in a specific category from the user's past input history. This improves input efficiency by referring to the past input history. The auto-completion function is realized, for example, based on a completion algorithm and completion accuracy. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history data into a generation AI and have the generation AI perform auto-completion.

[0032] The input unit can provide a variety of input methods when inputting product transaction information using voice input or image recognition. For example, the input unit converts product names and descriptions into text simply by a user's voice. Alternatively, the input unit can automatically input product information using image recognition technology when a user uploads a photo of the product. Alternatively, the input unit can combine voice input and image recognition to input product transaction information more intuitively. This provides a variety of input methods using voice input and image recognition. Voice input is achieved, for example, based on voice recognition technology or the accuracy of voice input. Image recognition is achieved, for example, based on image analysis technology or the accuracy of image recognition. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input voice data or image data into a generation AI and have the generation AI convert the data into text data.

[0033] The input unit can automatically suggest delivery method options taking into account the user's current location information when inputting product transaction information. For example, when the user inputs their current location, the input unit automatically suggests the optimal delivery method. Furthermore, when the user inputs a destination, the input unit can also suggest the optimal delivery method taking into account the distance from the current location. Furthermore, when the user uses the app while on the move, the input unit can update the current location in real time and suggest the optimal delivery method. This allows the optimal delivery method to be suggested by taking into account the current location information. The current location information is obtained, for example, based on GPS data or the accuracy of the location information. The delivery method options are suggested based, for example, on the selection criteria for the delivery company and the delivery cost. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input current location data to a generation AI and cause the generation AI to suggest the optimal delivery method.

[0034] The input unit can analyze the user's social media activity when inputting product transaction information and automatically suggest related product information. The input unit can suggest related product information based on, for example, products that the user has "liked" on social media. The input unit can also analyze the content of the user's social media posts and suggest related product information. The input unit can also suggest related product information based on the activity of the user's friends on social media. In this way, related product information is suggested by analyzing social media activity. The analysis of social media activity is performed based on, for example, the analytical tools and analytical criteria used. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input social media data to a generation AI and cause the generation AI to suggest related product information.

[0035] The input unit can customize the input interface by reflecting the user's past feedback when inputting product transaction information. The input unit customizes the input interface, for example, based on feedback provided by the user in the past. The input unit can also add or delete specific functions based on the user's past feedback. The input unit can also adjust the design of the input interface based on the user's feedback. In this way, the input interface is customized by reflecting the past feedback. The reflection of the past feedback is performed based on, for example, the feedback collection method and the reflection criteria. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input past feedback data to a generation AI and have the generation AI customize the input interface.

[0036] When inputting product transaction information, the input unit can provide the optimal input method by taking into account the user's device information. For example, if the user is using a smartphone, the input unit prioritizes touch input. Furthermore, if the user is using a tablet, the input unit can also provide an input method optimized for a large screen. Furthermore, if the user is using a desktop, the input unit can also prioritize keyboard input. In this way, the optimal input method is provided by taking the device information into consideration. The device information is acquired based on, for example, the type of device and the device characteristics. The optimal input method is provided based on, for example, input method selection criteria and a method of providing the input method. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the device information to a generation AI and cause the generation AI to provide the optimal input method.

[0037] When analyzing commodity transaction information, the analysis unit can improve the accuracy of the analysis by referring to past transaction data. The analysis unit, for example, predicts demand for commodities based on past transaction data. The analysis unit can also analyze price fluctuations based on past transaction data. The analysis unit can also propose an optimal delivery method based on past transaction data. In this way, the accuracy of the analysis is improved by referring to past transaction data. The reference to past transaction data is performed based on, for example, the data acquisition method and usage criteria. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past transaction data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0038] When analyzing product transaction information, the analysis unit can apply different analysis methods depending on the product category. For example, in the case of electronic devices, the analysis unit may perform an analysis that emphasizes technical specifications. In addition, in the case of fashion items, the analysis unit may perform an analysis that emphasizes trends. In addition, in the case of food, the analysis unit may perform an analysis that emphasizes expiration dates and storage methods. In this way, by applying an analysis method depending on the product category, the accuracy of the analysis is improved. Product categories are classified based on, for example, the type of category and classification criteria. Different analysis methods are applied based on, for example, the type of analysis method and application criteria. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input product category data into a generation AI and cause the generation AI to apply an appropriate analysis method.

[0039] The analysis unit can automatically detect input errors made by the user and suggest corrections when analyzing product transaction information. For example, the analysis unit can detect errors in the number of digits when the user enters a price and suggest corrections. The analysis unit can also detect spelling errors when the user enters a product name and suggest corrections. The analysis unit can also detect inappropriate selections when the user enters a shipping method and suggest corrections. This automatically detects input errors and suggests corrections, thereby improving the accuracy of input. The detection of input errors is performed, for example, based on a detection algorithm or detection criteria. The correction suggestions are performed, for example, based on a suggestion method or suggestion criteria. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input input data to a generation AI and have the generation AI detect input errors and suggest corrections.

[0040] When analyzing commodity trading information, the analysis unit can improve the accuracy of the analysis by referring to related market data. The analysis unit, for example, forecasts demand for a commodity based on current market trends. The analysis unit can also propose optimal pricing based on the prices of competing commodities. The analysis unit can also propose optimal sales times taking into account seasonal fluctuations in the market. In this way, the accuracy of the analysis is improved by referring to related market data. The market data is referenced based on, for example, the method of acquiring data and the criteria for use. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input market data into a generation AI and have the generation AI improve the accuracy of the analysis.

[0041] When analyzing product transaction information, the analysis unit can customize the analysis results by taking into account the user's past transaction history. The analysis unit, for example, makes optimal product suggestions based on the user's past transaction history. The analysis unit can also suggest optimal pricing based on the user's past transaction history. The analysis unit can also suggest optimal delivery methods based on the user's past transaction history. In this way, the analysis results are customized by taking into account the past transaction history. The past transaction history is taken into account based on, for example, the method of acquiring the history and the criteria for use. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past transaction history data into a generation AI and have the generation AI customize the analysis results.

[0042] When analyzing product transaction information, the analysis unit can integrate information from different data sources to improve the accuracy of the analysis. For example, the analysis unit can integrate social media data to forecast product demand. The analysis unit can also integrate competitor data to propose optimal pricing. The analysis unit can also integrate market research data to propose optimal sales strategies. In this way, the accuracy of the analysis is improved by integrating information from different data sources. The integration of information from different data sources is performed based on, for example, a data acquisition method and integration criteria. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information from different data sources into a generation AI and have the generation AI integrate the information and improve the accuracy of the analysis.

[0043] The translation unit can improve the accuracy of the translation by taking into account technical terms and industry jargon when translating product transaction information. For example, in the case of electronic devices, the translation unit accurately translates technical terminology. The translation unit can also accurately translate industry-specific terminology in the case of fashion items. The translation unit can also accurately translate terms related to cooking methods and ingredients in the case of food. This improves the accuracy of the translation by taking into account technical terms and industry jargon. The consideration of technical terms and industry jargon is performed, for example, based on a list of terms and consideration criteria. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input data on technical terms and industry jargon into the generation AI and have the generation AI improve the accuracy of the translation.

[0044] When translating product transaction information, the translation unit can maintain consistency in the translation by referring to past translation history. The translation unit provides consistent translations, for example, based on product names and descriptions previously translated by the user. The translation unit can also consistently use the same terminology based on the user's past translation history. The translation unit can also provide translations in the same style based on the user's past translation history. In this way, consistency in the translation is maintained by referring to the past translation history. The reference to the past translation history is performed based on, for example, the method of acquiring the history and the criteria for use. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input past translation history data into a generation AI and have the generation AI perform processing to maintain consistency in the translation.

[0045] When translating product transaction information, the translation unit can adjust the translation content by taking cultural background into consideration. For example, the translation unit uses appropriate expressions to suit the culture of the translation destination. The translation unit can also use appropriate examples and metaphors to suit the culture of the translation destination. The translation unit can also use appropriate honorifics and polite language to suit the culture of the translation destination. In this way, appropriate translation content is provided by taking cultural background into consideration. The cultural background is taken into consideration based on, for example, a list of cultural elements and consideration criteria. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input cultural background data into the generation AI and have the generation AI adjust the translation content.

[0046] When translating commodity trading information, the translation unit can improve the accuracy of the translation by referring to related literature. For example, the translation unit can refer to related technical literature to use accurate technical terminology. The translation unit can also refer to related industry literature to use accurate industry terminology. The translation unit can also refer to related cultural literature to use appropriate cultural expressions. In this way, the accuracy of the translation is improved by referring to related literature. The reference to related literature is performed, for example, based on the method of obtaining the literature and the criteria for use. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the translation.

[0047] When translating product transaction information, the translation unit can adjust the use of technical terminology in the translation depending on the user's level of expertise. For example, if the user is an expert, the translation unit can provide a translation that uses a lot of technical terminology. Furthermore, if the user is a general consumer, the translation unit can also provide a translation that avoids technical terminology. The translation unit can also adjust the use of appropriate technical terminology depending on the user's level of expertise. This improves the quality of the translation by providing a translation that is appropriate for the user's level of expertise. The level of expertise is evaluated based on, for example, an evaluation method or usage criteria. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without AI. For example, the translation unit can input the user's level of expertise data into the generation AI and cause the generation AI to perform a process of adjusting the use of technical terminology.

[0048] When translating product transaction information, the translation unit can adjust the translation content by taking into account nuances between different languages. For example, the translation unit uses appropriate expressions to match the nuances of the target language. The translation unit can also use appropriate examples and metaphors to match the nuances of the target language. The translation unit can also use appropriate honorifics and polite language to match the nuances of the target language. This allows appropriate translation content to be provided by taking into account nuances between different languages. The consideration of nuances between different languages ​​is performed, for example, based on a list of nuances or consideration criteria. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input nuance data between different languages ​​to a generation AI and have the generation AI adjust the translation content.

[0049] When displaying translated product transaction information, the display unit can customize the display content by referring to the user's past browsing history. For example, the display unit displays related product information based on product information previously viewed by the user. The display unit can also provide an optimal display method based on the user's past browsing history. The display unit can also display related product categories based on the user's past browsing history. In this way, the display content is customized by referring to the past browsing history. The past browsing history is referenced based on, for example, a history acquisition method or usage criteria. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input past browsing history data to a generation AI and have the generation AI customize the display content.

[0050] When displaying translated product transaction information, the display unit can provide an optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the display unit can provide a display method optimized for a wide screen. In this way, the optimal display method is provided by taking the device information into consideration. The device information is acquired based on, for example, the type of device and the device characteristics. The optimal display method is provided based on, for example, display method selection criteria and a display method provision method. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the device information to a generation AI and cause the generation AI to provide the optimal display method.

[0051] The display unit can display related information taking into account the user's current location information when displaying translated product transaction information. For example, when the user inputs their current location, the display unit can display related product information. Furthermore, when the user inputs a destination, the display unit can display related product information taking into account the distance from the current location. Furthermore, when the user uses the app while on the move, the display unit can update the current location in real time and display related product information. This provides related information by taking into account the current location information. The current location information is obtained, for example, based on GPS data or the accuracy of the location information. The display of related information is performed, for example, based on the type of information and the display method. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input current location data to a generation AI and cause the generation AI to display related information.

[0052] When displaying the translated product transaction information, the display unit can analyze the user's social media activity and display related information. For example, the display unit can display related product information based on products that the user has "liked" on social media. The display unit can also analyze the content of the user's social media posts and display related product information. The display unit can also display related product information based on the activity of the user's friends on social media. In this way, related information is provided by analyzing social media activity. The analysis of social media activity is performed based on, for example, the analysis tool and analysis criteria used. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input social media data into a generation AI and cause the generation AI to display related information.

[0053] When displaying translated product transaction information, the display unit can customize the display interface by reflecting the user's past feedback. The display unit customizes the display interface, for example, based on feedback provided by the user in the past. The display unit can also add or delete specific functions based on the user's past feedback. The display unit can also adjust the design of the display interface based on the user's feedback. In this way, the display interface is customized by reflecting the past feedback. The reflection of the past feedback is performed based on, for example, a feedback collection method and reflection criteria. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input past feedback data into a generation AI and have the generation AI customize the display interface.

[0054] When displaying translated product transaction information, the display unit can make the display content multilingual according to the user's language setting. The display unit, for example, automatically sets the display content based on the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide display content in a specific language when the user selects that language. This achieves multilingual support by providing display content according to the language setting. The language setting is acquired, for example, based on a setting acquisition method or usage criteria. Multilingual support is achieved, for example, based on the type of supported language and the support method. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input language setting data to a generation AI and have the generation AI execute multilingual display content.

[0055] The real-time translation unit can improve the accuracy of translation by taking into account technical terms and industry jargon during real-time translation. For example, in the case of electronic devices, the real-time translation unit accurately translates technical terminology. In addition, in the case of fashion items, the real-time translation unit can also accurately translate industry-specific terminology. In addition, in the case of food, the real-time translation unit can accurately translate terms related to cooking methods and ingredients. In this way, the accuracy of real-time translation is improved by taking into account technical terms and industry jargon. The consideration of technical terms and industry jargon is performed, for example, based on a list of terms and consideration criteria. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input data on technical terms and industry jargon into a generation AI and have the generation AI improve the accuracy of the translation.

[0056] The real-time translation unit can maintain consistency in translation by referring to past translation history during real-time translation. The real-time translation unit provides consistent translations based on, for example, product names and descriptions previously translated by the user. The real-time translation unit can also consistently use the same terminology based on the user's past translation history. The real-time translation unit can also provide translations in the same style based on the user's past translation history. In this way, consistency in translation is maintained by referring to past translation history. The reference to past translation history is performed based on, for example, a method for obtaining the history or criteria for use. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input past translation history data into a generation AI and have the generation AI perform processing to maintain consistency in the translation.

[0057] The real-time translation unit can adjust the translation content taking cultural background into account during real-time translation. For example, the real-time translation unit uses appropriate expressions to match the culture of the translation destination. The real-time translation unit can also use appropriate examples and metaphors to match the culture of the translation destination. The real-time translation unit can also use appropriate honorifics and polite language to match the culture of the translation destination. In this way, appropriate real-time translation content is provided by taking cultural background into account. The cultural background is taken into account based on, for example, a list of cultural elements or consideration criteria. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input cultural background data into a generation AI and have the generation AI adjust the translation content.

[0058] The real-time translation unit can improve the accuracy of the translation by referring to related literature during real-time translation. The real-time translation unit, for example, refers to related technical literature to use accurate technical terminology. The real-time translation unit can also refer to related industry literature to use accurate industry terminology. The real-time translation unit can also refer to related cultural literature to use appropriate cultural expressions. In this way, the accuracy of the real-time translation is improved by referring to related literature. The reference to related literature is performed, for example, based on the method of obtaining the literature and the criteria for use. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the translation.

[0059] The real-time translation unit can adjust the use of technical terminology in the translation during real-time translation according to the user's level of expertise. For example, if the user is an expert, the real-time translation unit can provide a translation that uses a lot of technical terminology. Furthermore, if the user is a general consumer, the real-time translation unit can also provide a translation that avoids technical terminology. The real-time translation unit can also adjust the use of appropriate technical terminology according to the user's level of expertise. This improves the quality of the translation by providing real-time translation that is appropriate for the user's level of expertise. The level of expertise is evaluated based on, for example, an evaluation method or usage criteria. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input the user's level of expertise data into the generation AI and cause the generation AI to perform a process of adjusting the use of technical terminology.

[0060] The real-time translation unit can adjust the translation content during real-time translation by taking into account nuances between different languages. For example, the real-time translation unit uses appropriate expressions to match the nuances of the target language. The real-time translation unit can also use appropriate examples and metaphors to match the nuances of the target language. The real-time translation unit can also use appropriate honorifics and polite language to match the nuances of the target language. In this way, appropriate real-time translation content is provided by taking into account nuances between different languages. The consideration of nuances between different languages ​​is performed based on, for example, a list of nuances or consideration criteria. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input nuance data between different languages ​​to a generation AI and have the generation AI adjust the translation content.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The input unit can refer to the user's past purchase history and automatically suggest related product information. For example, it can suggest products similar to products the user has previously purchased. The input unit can also suggest complementary products or related accessories to products the user has previously purchased. The input unit can also preferentially suggest products of a specific brand or category based on the user's past purchase history. In this way, by referring to the user's past purchase history, it is possible to provide product information that is highly relevant to the user.

[0063] The input unit can suggest optimal trading partners by taking into account the user's current location information. For example, when the user inputs their current location, nearby trading partners are automatically suggested. In addition, when the user inputs their destination, the input unit can suggest trading partners close to the destination. Furthermore, when the user uses the app while on the move, the input unit can update the user's current location in real time and suggest optimal trading partners. This improves the convenience of trading by taking into account the user's current location information.

[0064] When analyzing product transaction information, the analysis unit can improve the accuracy of the analysis by integrating information from different data sources. For example, social media data can be integrated to forecast product demand. The analysis unit can also integrate data from competitors to propose optimal pricing. The analysis unit can also integrate market research data to propose optimal sales strategies. In this way, the accuracy of the analysis is improved by integrating information from different data sources.

[0065] When translating product transaction information, the translation department can adjust the translation content taking cultural background into consideration. For example, the translation department can use appropriate expressions to suit the culture of the target translation. The translation department can also use appropriate examples and metaphors to suit the culture of the target translation. The translation department can also use appropriate honorifics and polite language to suit the culture of the target translation. In this way, the translation department can provide appropriate translation content by taking cultural background into consideration.

[0066] When displaying the translated product transaction information, the display unit can customize the display content by referring to the user's past browsing history. For example, related product information is displayed based on product information previously viewed by the user. The display unit can also provide an optimal display method based on the user's past browsing history. The display unit can also display related product categories based on the user's past browsing history. In this way, the display content is customized by referring to the past browsing history.

[0067] The real-time translation unit can improve the accuracy of translation by taking into account technical terms and industry jargon during real-time translation. For example, in the case of electronic devices, technical terminology is accurately translated. The real-time translation unit can also accurately translate industry-specific terminology in the case of fashion items. The real-time translation unit can also accurately translate terms related to cooking methods and ingredients in the case of food. This improves the accuracy of real-time translation by taking into account technical terms and industry jargon.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The input unit allows the user to input product transaction information. The product transaction information includes, for example, the product name, description, price, and delivery method. The input unit can input product transaction information not only in text format, but also using voice input and image recognition. For example, when the user inputs the product name and description by voice, it is converted into text. Also, when the user uploads a photo of the product, the product information is automatically entered using image recognition technology. Step 2: The analysis unit analyzes the information entered by the input unit. The analysis is performed using data analysis techniques and analysis algorithms. For example, the analysis unit analyzes the entered product names and descriptions and classifies them into appropriate categories. It can also analyze the entered prices and delivery methods to propose optimal transaction terms. Step 3: The translation unit translates the information analyzed by the analysis unit. The translation is performed based on the translation engine used and the accuracy of the translation. For example, the translation unit uses a generation AI to translate the input information into multiple languages. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI translates into major languages ​​such as English, Chinese, and Spanish. It can also improve the accuracy of the translation by taking into account technical terms and industry jargon. Step 4: The display unit displays the information translated by the translation unit. The display is based on the display format and display device. For example, the display unit displays the translated information in a format that is easy for users in each country to understand. The display unit also provides the optimal display method taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size.

[0070] (Example 2) A flea market system according to an embodiment of the present invention allows users to enter product transaction information into a single form, translating it into languages ​​around the world and listing the items for sale. The flea market system includes an input unit for entering product transaction information, an analysis unit for analyzing the entered information, a translation unit for translating the analyzed information, and a display unit for displaying the translated information. For example, in the flea market system, a user enters information such as the product name, description, price, and shipping method. A generation AI then analyzes this information and translates it into multiple languages. For example, the information is translated into major languages ​​such as English, Chinese, and Spanish. The translated information is displayed in a format that is easy for users in each country to understand. Furthermore, the flea market system provides an in-app translation function for users living in Japan who have difficulty speaking Japanese. For example, the generation AI translates information entered by a user in Japanese and converts it into other languages ​​in real time. This allows foreign users to easily conduct transactions. This makes it easier for individuals to buy and sell products, promoting international transactions. It also lowers transaction barriers for foreign users living in Japan, potentially increasing transaction volumes.

[0071] The flea market system according to the embodiment includes an input unit, an analysis unit, a translation unit, and a display unit. The input unit allows a user to input product transaction information. The product transaction information includes, but is not limited to, product names, descriptions, prices, and delivery methods. For example, the input unit allows a user to input product names and descriptions in text format. The input unit can also input product transaction information using voice input or image recognition. For example, a user can simply input a product name and description by voice, which is converted into text. Furthermore, when a user uploads a photo of a product, product information is automatically input using image recognition technology. The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, a data analysis method or an analysis algorithm, but is not limited to, examples. For example, the analysis unit analyzes the input product names and descriptions and classifies them into appropriate categories. The analysis unit can also analyze the input price and delivery method and propose optimal transaction terms. The translation unit translates the information analyzed by the analysis unit. The translation is performed based on, for example, the translation engine used and the translation accuracy, but is not limited to, examples. For example, the translation unit uses a generation AI to translate input information into multiple languages. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI translates into major languages ​​such as English, Chinese, and Spanish. The translation unit may also improve the accuracy of the translation by taking into account technical terms and industry jargon. The display unit displays the information translated by the translation unit. The display may be performed based on, for example, a display format or a display device, but is not limited to, for example. For example, the display unit displays the translated information in a format that is easy for users in each country to understand. The display unit may also provide an optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, it may provide a display method tailored to the screen size. As a result, the flea market system according to the embodiment can translate product transaction information into multiple languages ​​and promote international transactions.

[0072] The flea market system includes a real-time translation unit that translates information entered by a user in Japanese in real time and converts it into another language. The real-time translation unit translates information entered by a user in Japanese in real time and converts it into another language. Real-time translation is performed based on, for example, the translation delay time and the technology used, but is not limited to these examples. For example, the real-time translation unit uses a generation AI to translate information entered by a user in Japanese in real time. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the generation AI translates product names and descriptions entered by a user in Japanese into English, Chinese, Spanish, etc. in real time. The real-time translation unit can also estimate the user's emotions and adjust the translation expression based on the estimated user's emotions. For example, if the user is relaxed, it provides a careful and detailed translation. This allows real-time translation to be performed, making it easy for foreign users to conduct transactions.

[0073] The input unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, when the user is stressed, the input unit can provide a simple interface and minimize input steps. Furthermore, when the user is relaxed, the input unit can provide detailed input options and suggest customizable input methods. Furthermore, when the user is in a hurry, the input unit can prioritize voice input and enable the user to quickly enter product transaction information. This provides an interface that corresponds to the user's emotions and improves ease of input. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit can be performed using AI, for example, or without AI. For example, the input unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0074] The input unit can provide an auto-completion function by referring to the user's past input history when entering product transaction information. For example, the input unit can automatically display product names and descriptions previously entered by the user as candidates. The input unit can also automatically display prices and delivery methods previously entered by the user as candidates. The input unit can also predict and automatically complete product information in a specific category from the user's past input history. This improves input efficiency by referring to the past input history. The auto-completion function is realized, for example, based on a completion algorithm and completion accuracy. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history data into a generation AI and have the generation AI perform auto-completion.

[0075] The input unit can provide a variety of input methods when inputting product transaction information using voice input or image recognition. For example, the input unit converts product names and descriptions into text simply by a user's voice. Alternatively, the input unit can automatically input product information using image recognition technology when a user uploads a photo of the product. Alternatively, the input unit can combine voice input and image recognition to input product transaction information more intuitively. This provides a variety of input methods using voice input and image recognition. Voice input is achieved, for example, based on voice recognition technology or the accuracy of voice input. Image recognition is achieved, for example, based on image analysis technology or the accuracy of image recognition. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input voice data or image data into a generation AI and have the generation AI convert the data into text data.

[0076] The input unit can automatically suggest delivery method options taking into account the user's current location information when inputting product transaction information. For example, when the user inputs their current location, the input unit automatically suggests the optimal delivery method. Furthermore, when the user inputs a destination, the input unit can also suggest the optimal delivery method taking into account the distance from the current location. Furthermore, when the user uses the app while on the move, the input unit can update the current location in real time and suggest the optimal delivery method. This allows the optimal delivery method to be suggested by taking into account the current location information. The current location information is obtained, for example, based on GPS data or the accuracy of the location information. The delivery method options are suggested based, for example, on the selection criteria for the delivery company and the delivery cost. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input current location data to a generation AI and cause the generation AI to suggest the optimal delivery method.

[0077] The input unit can estimate the user's emotions and display a confirmation message for the input content based on the estimated user emotions. For example, if the user feels anxious, the input unit displays a confirmation message to prompt the user to reconfirm the input content. Furthermore, if the user feels relaxed, the input unit can also display a simplified confirmation message. Furthermore, if the user is in a hurry, the input unit can omit the confirmation message and quickly proceed to the next step. This prompts the user to confirm the input content by displaying a confirmation message according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0078] The input unit can analyze the user's social media activity when inputting product transaction information and automatically suggest related product information. The input unit can suggest related product information based on, for example, products that the user has "liked" on social media. The input unit can also analyze the content of the user's social media posts and suggest related product information. The input unit can also suggest related product information based on the activity of the user's friends on social media. In this way, related product information is suggested by analyzing social media activity. The analysis of social media activity is performed based on, for example, the analytical tools and analytical criteria used. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input social media data to a generation AI and cause the generation AI to suggest related product information.

[0079] The input unit can customize the input interface by reflecting the user's past feedback when inputting product transaction information. The input unit customizes the input interface, for example, based on feedback provided by the user in the past. The input unit can also add or delete specific functions based on the user's past feedback. The input unit can also adjust the design of the input interface based on the user's feedback. In this way, the input interface is customized by reflecting the past feedback. The reflection of the past feedback is performed based on, for example, the feedback collection method and the reflection criteria. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input past feedback data to a generation AI and have the generation AI customize the input interface.

[0080] When inputting product transaction information, the input unit can provide the optimal input method by taking into account the user's device information. For example, if the user is using a smartphone, the input unit prioritizes touch input. Furthermore, if the user is using a tablet, the input unit can also provide an input method optimized for a large screen. Furthermore, if the user is using a desktop, the input unit can also prioritize keyboard input. In this way, the optimal input method is provided by taking the device information into consideration. The device information is acquired based on, for example, the type of device and the device characteristics. The optimal input method is provided based on, for example, input method selection criteria and a method of providing the input method. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the device information to a generation AI and cause the generation AI to provide the optimal input method.

[0081] The analysis unit can estimate the user's emotions and adjust the parameters of the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can adjust the parameters to perform a detailed analysis. If the user is in a hurry, the analysis unit can also adjust the parameters to perform a quick analysis. If the user is excited, the analysis unit can also adjust the parameters to provide a visually stimulating analysis result. This improves analysis accuracy by adjusting the parameters of the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0082] When analyzing commodity transaction information, the analysis unit can improve the accuracy of the analysis by referring to past transaction data. The analysis unit, for example, predicts demand for commodities based on past transaction data. The analysis unit can also analyze price fluctuations based on past transaction data. The analysis unit can also propose an optimal delivery method based on past transaction data. In this way, the accuracy of the analysis is improved by referring to past transaction data. The reference to past transaction data is performed based on, for example, the data acquisition method and usage criteria. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past transaction data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0083] When analyzing product transaction information, the analysis unit can apply different analysis methods depending on the product category. For example, in the case of electronic devices, the analysis unit may perform an analysis that emphasizes technical specifications. In addition, in the case of fashion items, the analysis unit may perform an analysis that emphasizes trends. In addition, in the case of food, the analysis unit may perform an analysis that emphasizes expiration dates and storage methods. In this way, by applying an analysis method depending on the product category, the accuracy of the analysis is improved. Product categories are classified based on, for example, the type of category and classification criteria. Different analysis methods are applied based on, for example, the type of analysis method and application criteria. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input product category data into a generation AI and cause the generation AI to apply an appropriate analysis method.

[0084] The analysis unit can automatically detect input errors made by the user and suggest corrections when analyzing product transaction information. For example, the analysis unit can detect errors in the number of digits when the user enters a price and suggest corrections. The analysis unit can also detect spelling errors when the user enters a product name and suggest corrections. The analysis unit can also detect inappropriate selections when the user enters a shipping method and suggest corrections. This automatically detects input errors and suggests corrections, thereby improving the accuracy of input. The detection of input errors is performed, for example, based on a detection algorithm or detection criteria. The correction suggestions are performed, for example, based on a suggestion method or suggestion criteria. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input input data to a generation AI and have the generation AI detect input errors and suggest corrections.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This facilitates understanding of the analysis results by providing a display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0086] When analyzing commodity trading information, the analysis unit can improve the accuracy of the analysis by referring to related market data. The analysis unit, for example, forecasts demand for a commodity based on current market trends. The analysis unit can also propose optimal pricing based on the prices of competing commodities. The analysis unit can also propose optimal sales times taking into account seasonal fluctuations in the market. In this way, the accuracy of the analysis is improved by referring to related market data. The market data is referenced based on, for example, the method of acquiring data and the criteria for use. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input market data into a generation AI and have the generation AI improve the accuracy of the analysis.

[0087] When analyzing product transaction information, the analysis unit can customize the analysis results by taking into account the user's past transaction history. The analysis unit, for example, makes optimal product suggestions based on the user's past transaction history. The analysis unit can also suggest optimal pricing based on the user's past transaction history. The analysis unit can also suggest optimal delivery methods based on the user's past transaction history. In this way, the analysis results are customized by taking into account the past transaction history. The past transaction history is taken into account based on, for example, the method of acquiring the history and the criteria for use. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past transaction history data into a generation AI and have the generation AI customize the analysis results.

[0088] When analyzing product transaction information, the analysis unit can integrate information from different data sources to improve the accuracy of the analysis. For example, the analysis unit can integrate social media data to forecast product demand. The analysis unit can also integrate competitor data to propose optimal pricing. The analysis unit can also integrate market research data to propose optimal sales strategies. In this way, the accuracy of the analysis is improved by integrating information from different data sources. The integration of information from different data sources is performed based on, for example, a data acquisition method and integration criteria. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information from different data sources into a generation AI and have the generation AI integrate the information and improve the accuracy of the analysis.

[0089] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user emotions. For example, if the user is relaxed, the translation unit can provide a careful and detailed translation. If the user is in a hurry, the translation unit can also provide a concise and to-the-point translation. If the user is excited, the translation unit can also provide a translation including visually stimulating expressions. This improves the quality of the translation by providing a translation expression that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the translation unit can be performed using AI, for example, or without AI. For example, the translation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotions and adjust the translation expression.

[0090] The translation unit can improve the accuracy of the translation by taking into account technical terms and industry jargon when translating product transaction information. For example, in the case of electronic devices, the translation unit accurately translates technical terminology. The translation unit can also accurately translate industry-specific terminology in the case of fashion items. The translation unit can also accurately translate terms related to cooking methods and ingredients in the case of food. This improves the accuracy of the translation by taking into account technical terms and industry jargon. The consideration of technical terms and industry jargon is performed, for example, based on a list of terms and consideration criteria. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input data on technical terms and industry jargon into the generation AI and have the generation AI improve the accuracy of the translation.

[0091] When translating product transaction information, the translation unit can maintain consistency in the translation by referring to past translation history. The translation unit provides consistent translations, for example, based on product names and descriptions previously translated by the user. The translation unit can also consistently use the same terminology based on the user's past translation history. The translation unit can also provide translations in the same style based on the user's past translation history. In this way, consistency in the translation is maintained by referring to the past translation history. The reference to the past translation history is performed based on, for example, the method of acquiring the history and the criteria for use. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input past translation history data into a generation AI and have the generation AI perform processing to maintain consistency in the translation.

[0092] When translating product transaction information, the translation unit can adjust the translation content by taking cultural background into consideration. For example, the translation unit uses appropriate expressions to suit the culture of the translation destination. The translation unit can also use appropriate examples and metaphors to suit the culture of the translation destination. The translation unit can also use appropriate honorifics and polite language to suit the culture of the translation destination. In this way, appropriate translation content is provided by taking cultural background into consideration. The cultural background is taken into consideration based on, for example, a list of cultural elements and consideration criteria. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input cultural background data into the generation AI and have the generation AI adjust the translation content.

[0093] The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated user emotions. For example, if the user is in a hurry, the translation unit can provide a concise, to-the-point translation. If the user is relaxed, the translation unit can provide a longer translation with detailed explanations. If the user is excited, the translation unit can provide a translation with visually stimulating expressions. This improves the quality of the translation by providing a translation length that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the translation unit can be performed using AI, for example, or without AI. For example, the translation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion and adjust the translation length.

[0094] When translating commodity trading information, the translation unit can improve the accuracy of the translation by referring to related literature. For example, the translation unit can refer to related technical literature to use accurate technical terminology. The translation unit can also refer to related industry literature to use accurate industry terminology. The translation unit can also refer to related cultural literature to use appropriate cultural expressions. In this way, the accuracy of the translation is improved by referring to related literature. The reference to related literature is performed, for example, based on the method of obtaining the literature and the criteria for use. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the translation.

[0095] When translating product transaction information, the translation unit can adjust the use of technical terminology in the translation depending on the user's level of expertise. For example, if the user is an expert, the translation unit can provide a translation that uses a lot of technical terminology. Furthermore, if the user is a general consumer, the translation unit can also provide a translation that avoids technical terminology. The translation unit can also adjust the use of appropriate technical terminology depending on the user's level of expertise. This improves the quality of the translation by providing a translation that is appropriate for the user's level of expertise. The level of expertise is evaluated based on, for example, an evaluation method or usage criteria. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without AI. For example, the translation unit can input the user's level of expertise data into the generation AI and cause the generation AI to perform a process of adjusting the use of technical terminology.

[0096] When translating product transaction information, the translation unit can adjust the translation content by taking into account nuances between different languages. For example, the translation unit uses appropriate expressions to match the nuances of the target language. The translation unit can also use appropriate examples and metaphors to match the nuances of the target language. The translation unit can also use appropriate honorifics and polite language to match the nuances of the target language. This allows appropriate translation content to be provided by taking into account nuances between different languages. The consideration of nuances between different languages ​​is performed, for example, based on a list of nuances or consideration criteria. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input nuance data between different languages ​​to a generation AI and have the generation AI adjust the translation content.

[0097] The display unit can estimate the user's emotions and adjust the design of the display interface based on the estimated user emotions. For example, if the user is nervous, the display unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the display unit can provide an interface with bright colors to make input tasks more enjoyable. Furthermore, if the user is tired, the display unit can provide a simple, highly visible interface to make input tasks easier. This reduces visual stress by providing a display interface that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions and adjust the design of the display interface.

[0098] When displaying translated product transaction information, the display unit can customize the display content by referring to the user's past browsing history. For example, the display unit displays related product information based on product information previously viewed by the user. The display unit can also provide an optimal display method based on the user's past browsing history. The display unit can also display related product categories based on the user's past browsing history. In this way, the display content is customized by referring to the past browsing history. The past browsing history is referenced based on, for example, a history acquisition method or usage criteria. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input past browsing history data to a generation AI and have the generation AI customize the display content.

[0099] When displaying translated product transaction information, the display unit can provide an optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the display unit can provide a display method optimized for a wide screen. In this way, the optimal display method is provided by taking the device information into consideration. The device information is acquired based on, for example, the type of device and the device characteristics. The optimal display method is provided based on, for example, display method selection criteria and a display method provision method. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the device information to a generation AI and cause the generation AI to provide the optimal display method.

[0100] The display unit can display related information taking into account the user's current location information when displaying translated product transaction information. For example, when the user inputs their current location, the display unit can display related product information. Furthermore, when the user inputs a destination, the display unit can display related product information taking into account the distance from the current location. Furthermore, when the user uses the app while on the move, the display unit can update the current location in real time and display related product information. This provides related information by taking into account the current location information. The current location information is obtained, for example, based on GPS data or the accuracy of the location information. The display of related information is performed, for example, based on the type of information and the display method. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input current location data to a generation AI and cause the generation AI to display related information.

[0101] The display unit can estimate the user's emotions and prioritize display content based on the estimated user emotions. For example, when the user is nervous, the display unit can prioritize displaying important information. Furthermore, when the user is relaxed, the display unit can prioritize displaying detailed information. Furthermore, when the user is in a hurry, the display unit can prioritize displaying key points. This prioritizes display content according to the user's emotions, thereby prioritizing display of important information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions and prioritize display content.

[0102] When displaying the translated product transaction information, the display unit can analyze the user's social media activity and display related information. For example, the display unit can display related product information based on products that the user has "liked" on social media. The display unit can also analyze the content of the user's social media posts and display related product information. The display unit can also display related product information based on the activity of the user's friends on social media. In this way, related information is provided by analyzing social media activity. The analysis of social media activity is performed based on, for example, the analysis tool and analysis criteria used. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input social media data into a generation AI and cause the generation AI to display related information.

[0103] When displaying translated product transaction information, the display unit can customize the display interface by reflecting the user's past feedback. The display unit customizes the display interface, for example, based on feedback provided by the user in the past. The display unit can also add or delete specific functions based on the user's past feedback. The display unit can also adjust the design of the display interface based on the user's feedback. In this way, the display interface is customized by reflecting the past feedback. The reflection of the past feedback is performed based on, for example, a feedback collection method and reflection criteria. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input past feedback data into a generation AI and have the generation AI customize the display interface.

[0104] When displaying translated product transaction information, the display unit can make the display content multilingual according to the user's language setting. The display unit, for example, automatically sets the display content based on the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide display content in a specific language when the user selects that language. This achieves multilingual support by providing display content according to the language setting. The language setting is acquired, for example, based on a setting acquisition method or usage criteria. Multilingual support is achieved, for example, based on the type of supported language and the support method. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input language setting data to a generation AI and have the generation AI execute multilingual display content.

[0105] The real-time translation unit can estimate the user's emotions and adjust the expression method of the real-time translation based on the estimated user's emotions. For example, if the user is relaxed, the real-time translation unit can provide a careful and detailed translation. If the user is in a hurry, the real-time translation unit can also provide a concise and to-the-point translation. If the user is excited, the real-time translation unit can also provide a translation including visually stimulating expressions. This improves the quality of the translation by providing real-time translation expressions that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the real-time translation unit can be performed using, for example, AI, or without AI. For example, the real-time translation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion and adjust the expression method of the real-time translation.

[0106] The real-time translation unit can improve the accuracy of translation by taking into account technical terms and industry jargon during real-time translation. For example, in the case of electronic devices, the real-time translation unit accurately translates technical terminology. In addition, in the case of fashion items, the real-time translation unit can also accurately translate industry-specific terminology. In addition, in the case of food, the real-time translation unit can accurately translate terms related to cooking methods and ingredients. In this way, the accuracy of real-time translation is improved by taking into account technical terms and industry jargon. The consideration of technical terms and industry jargon is performed, for example, based on a list of terms and consideration criteria. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input data on technical terms and industry jargon into a generation AI and have the generation AI improve the accuracy of the translation.

[0107] The real-time translation unit can maintain consistency in translation by referring to past translation history during real-time translation. The real-time translation unit provides consistent translations based on, for example, product names and descriptions previously translated by the user. The real-time translation unit can also consistently use the same terminology based on the user's past translation history. The real-time translation unit can also provide translations in the same style based on the user's past translation history. In this way, consistency in translation is maintained by referring to past translation history. The reference to past translation history is performed based on, for example, a method for obtaining the history or criteria for use. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input past translation history data into a generation AI and have the generation AI perform processing to maintain consistency in the translation.

[0108] The real-time translation unit can adjust the translation content taking cultural background into account during real-time translation. For example, the real-time translation unit uses appropriate expressions to match the culture of the translation destination. The real-time translation unit can also use appropriate examples and metaphors to match the culture of the translation destination. The real-time translation unit can also use appropriate honorifics and polite language to match the culture of the translation destination. In this way, appropriate real-time translation content is provided by taking cultural background into account. The cultural background is taken into account based on, for example, a list of cultural elements or consideration criteria. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input cultural background data into a generation AI and have the generation AI adjust the translation content.

[0109] The real-time translation unit can estimate the user's emotions and adjust the length of the real-time translation based on the estimated user emotions. For example, if the user is in a hurry, the real-time translation unit can provide a concise, to-the-point translation. If the user is relaxed, the real-time translation unit can also provide a longer translation with detailed explanations. If the user is excited, the real-time translation unit can also provide a translation with visually stimulating expressions. This improves the quality of the translation by providing a real-time translation length that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the real-time translation unit can be performed using AI, for example, or without AI. For example, the real-time translation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion and adjust the length of the real-time translation.

[0110] The real-time translation unit can improve the accuracy of the translation by referring to related literature during real-time translation. The real-time translation unit, for example, refers to related technical literature to use accurate technical terminology. The real-time translation unit can also refer to related industry literature to use accurate industry terminology. The real-time translation unit can also refer to related cultural literature to use appropriate cultural expressions. In this way, the accuracy of the real-time translation is improved by referring to related literature. The reference to related literature is performed, for example, based on the method of obtaining the literature and the criteria for use. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the translation.

[0111] The real-time translation unit can adjust the use of technical terminology in the translation during real-time translation according to the user's level of expertise. For example, if the user is an expert, the real-time translation unit can provide a translation that uses a lot of technical terminology. Furthermore, if the user is a general consumer, the real-time translation unit can also provide a translation that avoids technical terminology. The real-time translation unit can also adjust the use of appropriate technical terminology according to the user's level of expertise. This improves the quality of the translation by providing real-time translation that is appropriate for the user's level of expertise. The level of expertise is evaluated based on, for example, an evaluation method or usage criteria. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input the user's level of expertise data into the generation AI and cause the generation AI to perform a process of adjusting the use of technical terminology.

[0112] The real-time translation unit can adjust the translation content during real-time translation by taking into account nuances between different languages. For example, the real-time translation unit uses appropriate expressions to match the nuances of the target language. The real-time translation unit can also use appropriate examples and metaphors to match the nuances of the target language. The real-time translation unit can also use appropriate honorifics and polite language to match the nuances of the target language. In this way, appropriate real-time translation content is provided by taking into account nuances between different languages. The consideration of nuances between different languages ​​is performed based on, for example, a list of nuances or consideration criteria. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input nuance data between different languages ​​to a generation AI and have the generation AI adjust the translation content. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, translation unit, display unit, and real-time translation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14, and a user inputs product transaction information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and translates the analyzed information. The display unit is realized by the output device 40 of the smart device 14, and displays the translated information. The real-time translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and translates information input in Japanese by a user in real time. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, analysis unit, translation unit, display unit, and real-time translation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214, and a user inputs commodity transaction information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and translates the analyzed information. The display unit is realized by the speaker 240 of the smart glasses 214, and displays the translated information. The real-time translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and translates information input in Japanese by a user in real time. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, translation unit, display unit, and real-time translation unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset terminal 314, and a user inputs commodity transaction information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and translates the analyzed information. The display unit is realized by the display 343 of the headset terminal 314, and displays the translated information. The real-time translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and translates information input in Japanese by a user in real time. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, translation unit, display unit, and real-time translation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414, and a user inputs commodity transaction information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and translates the analyzed information. The display unit is realized by the speaker 240 of the robot 414, and displays the translated information. The real-time translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and translates information input in Japanese by a user in real time.

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

[0114] The input unit can refer to the user's past purchase history and automatically suggest related product information. For example, it can suggest products similar to products the user has previously purchased. The input unit can also suggest complementary products or related accessories to products the user has previously purchased. The input unit can also preferentially suggest products of a specific brand or category based on the user's past purchase history. In this way, by referring to the user's past purchase history, it is possible to provide product information that is highly relevant to the user.

[0115] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, providing a display method that corresponds to the user's emotions promotes understanding of the analysis results.

[0116] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user's emotions. For example, if the user is relaxed, the translation unit can provide a careful and detailed translation. If the user is in a hurry, the translation unit can also provide a concise and to-the-point translation. If the user is excited, the translation unit can also provide a translation that includes visually stimulating expressions. This improves the quality of the translation by providing translation expressions that correspond to the user's emotions.

[0117] The display unit can estimate the user's emotions and adjust the design of the display interface based on the estimated user emotions. For example, if the user is nervous, the display unit can provide an interface with subdued colors to reduce visual stress. If the user is having fun, the display unit can provide an interface with bright colors to make input work more enjoyable. If the user is tired, the display unit can provide a simple, highly visible interface to make input work easier. In this way, visual stress is reduced by providing a display interface that corresponds to the user's emotions.

[0118] The real-time translation unit can estimate the user's emotions and adjust the way the real-time translation is expressed based on the estimated user's emotions. For example, if the user is relaxed, the real-time translation unit can provide a careful and detailed translation. If the user is in a hurry, the real-time translation unit can also provide a concise and to-the-point translation. If the user is excited, the real-time translation unit can also provide a translation that includes visually stimulating expressions. This improves the quality of the translation by providing real-time translation expressions that correspond to the user's emotions.

[0119] The input unit can suggest optimal trading partners by taking into account the user's current location information. For example, when the user inputs their current location, nearby trading partners are automatically suggested. In addition, when the user inputs their destination, the input unit can suggest trading partners close to the destination. Furthermore, when the user uses the app while on the move, the input unit can update the user's current location in real time and suggest optimal trading partners. This improves the convenience of trading by taking into account the user's current location information.

[0120] When analyzing product transaction information, the analysis unit can improve the accuracy of the analysis by integrating information from different data sources. For example, social media data can be integrated to forecast product demand. The analysis unit can also integrate data from competitors to propose optimal pricing. The analysis unit can also integrate market research data to propose optimal sales strategies. In this way, the accuracy of the analysis is improved by integrating information from different data sources.

[0121] When translating product transaction information, the translation department can adjust the translation content taking cultural background into consideration. For example, the translation department can use appropriate expressions to suit the culture of the target translation. The translation department can also use appropriate examples and metaphors to suit the culture of the target translation. The translation department can also use appropriate honorifics and polite language to suit the culture of the target translation. In this way, the translation department can provide appropriate translation content by taking cultural background into consideration.

[0122] When displaying the translated product transaction information, the display unit can customize the display content by referring to the user's past browsing history. For example, related product information is displayed based on product information previously viewed by the user. The display unit can also provide an optimal display method based on the user's past browsing history. The display unit can also display related product categories based on the user's past browsing history. In this way, the display content is customized by referring to the past browsing history.

[0123] The real-time translation unit can improve the accuracy of translation by taking into account technical terms and industry jargon during real-time translation. For example, in the case of electronic devices, technical terminology is accurately translated. The real-time translation unit can also accurately translate industry-specific terminology in the case of fashion items. The real-time translation unit can also accurately translate terms related to cooking methods and ingredients in the case of food. This improves the accuracy of real-time translation by taking into account technical terms and industry jargon.

[0124] The processing flow of the second embodiment will be briefly explained below.

[0125] Step 1: The input unit allows the user to input product transaction information. The product transaction information includes, for example, the product name, description, price, and delivery method. The input unit can input product transaction information not only in text format, but also using voice input and image recognition. For example, when the user inputs the product name and description by voice, it is converted into text. Also, when the user uploads a photo of the product, the product information is automatically entered using image recognition technology. Step 2: The analysis unit analyzes the information entered by the input unit. The analysis is performed using data analysis techniques and analysis algorithms. For example, the analysis unit analyzes the entered product names and descriptions and classifies them into appropriate categories. It can also analyze the entered prices and delivery methods to propose optimal transaction terms. Step 3: The translation unit translates the information analyzed by the analysis unit. The translation is performed based on the translation engine used and the accuracy of the translation. For example, the translation unit uses a generation AI to translate the input information into multiple languages. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI translates into major languages ​​such as English, Chinese, and Spanish. It can also improve the accuracy of the translation by taking into account technical terms and industry jargon. Step 4: The display unit displays the information translated by the translation unit. The display is based on the display format and display device. For example, the display unit displays the translated information in a format that is easy for users in each country to understand. The display unit also provides the optimal display method taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size.

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

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

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

[0143] 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.

[0144] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0164] The data processing device 12 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0176] 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.

[0177] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] [Explanation of symbols]

[0198] 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 input section for inputting commodity transaction information; an analysis unit that analyzes the information input by the input unit; a translation unit that translates the information analyzed by the analysis unit; a display unit that displays the information translated by the translation unit; Equipped with A system characterized by:

2. It has a real-time translation unit that translates information entered by users in Japanese into other languages ​​in real time.

2. The system of claim 1.

3. The input unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

4. The input unit When entering product transaction information, the system provides an auto-complete function by referencing the user's past input history.

2. The system of claim 1.

5. The input unit When entering product transaction information, voice input and image recognition are used to provide more diverse input options.

2. The system of claim 1.

6. The input unit When entering product transaction information, automatically suggest delivery options based on the user's current location.

2. The system of claim 1.

7. The input unit Estimate the user's emotions and display a confirmation message for the input based on the estimated user emotions.

2. The system of claim 1.

8. The input unit When entering product transaction information, analyze the user's social media activity and automatically suggest related product information.

2. The system of claim 1.

9. The input unit When entering product transaction information, customize the input interface by reflecting the user's past feedback.

2. The system of claim 1.

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