System

Abstract

An object of a system according to an embodiment is to overcome differences in languages and systems and to facilitate cross-border product transactions.SOLUTION: A system includes a commodity description translation part, a communication translation part, and a boundary crossing procedure automation part. The product description translation unit automatically translates the product description. The communication translation part automatically translates communication between the exhibitor and the purchaser. The boundary crossing procedure automating section automates a boundary crossing procedure.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, differences in language and systems made cross-border commodity trading difficult.

[0005] The system according to the embodiment aims to overcome differences in language and systems and facilitate cross-border commodity trading. [Means for solving the problem]

[0006] The system according to the embodiment includes a product description translation unit, a communication translation unit, and a cross-border procedure automation unit. The product description translation unit automatically translates product descriptions. The communication translation unit automatically translates communication between sellers and buyers. The cross-border procedure automation unit automates cross-border procedures. [Effects of the Invention]

[0007] The system according to the embodiment can overcome differences in language and systems and facilitate cross-border commodity trade. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The cross-border commodity trading system according to an embodiment of the present invention uses AI to realize the desire to buy and sell regardless of country. This system uses AI to overcome language and institutional barriers in product descriptions, communication, cross-border procedures, etc. As a result, the cross-border commodity trading system can facilitate commodity trading around the world and open up new shopping possibilities.

[0029] A cross-border commodity trading system according to an embodiment includes a product description translation unit, a communication translation unit, and a cross-border procedure automation unit. The product description translation unit automatically translates product descriptions. For example, a generation AI automatically translates product descriptions entered by a seller into multiple languages. The generation AI translates product descriptions using a text generation AI (e.g., LLM). The generation AI can also translate product descriptions using a multimodal generation AI. The generation AI can also extract and translate important parts of product descriptions. For example, the text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to extract particularly important information from product descriptions and performs translation based on that information. The communication translation unit automatically translates communications between sellers and buyers. For example, the generation AI automatically translates communications between sellers and buyers. The generation AI translates communications using a text generation AI (e.g., LLM). The generation AI can also translate communications using a multimodal generation AI. Generative AI can also extract and translate important parts of communications. For example, text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. Generative AI uses keyword extraction technology to identify particularly important information in communications and translates based on that information. The cross-border procedure automation unit automates cross-border procedures. For example, generative AI automatically analyzes each country's import and export procedures, tariffs, and regulations, and suggests the necessary procedures. Generative AI automates cross-border procedures using text generation AI (e.g., LLM). Generative AI can also automate cross-border procedures using multimodal generation AI. Generative AI can also extract and automate important parts of cross-border procedures. For example, text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities.The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to identify particularly important information in cross-border procedures and automates the process based on that information. This allows the cross-border commodity trading system according to the embodiment to overcome language and institutional barriers by utilizing AI, facilitating commodity trading around the world. For example, sellers can translate product descriptions into multiple languages, allowing buyers to understand the product descriptions in their own language. Furthermore, communication between sellers and buyers is automatically translated, allowing for smooth transactions without the need for language barriers. Furthermore, cross-border procedures are also automated, simplifying complex procedures and enabling smoother transactions.

[0030] The product description translation unit can take into account cultural background and regional expressions when translating. For example, when the generation AI translates a Japanese product description into English, it takes into account Japan's cultural background and regional expressions to convert it into expressions that are natural for English-speaking consumers. For example, the generation AI appropriately expresses the concept of "omotenashi" (hospitality) included in traditional Japanese product descriptions as "hospitality" in English. The generation AI also takes into account regional expressions when translating. For example, when translating a product description in Kansai dialect into standard Japanese, the generation AI takes into account Kansai-specific expressions and converts it into standard Japanese. This enables natural translation that takes into account cultural background and regional expressions.

[0031] The product description translation unit can also translate the captions and tags of product images. For example, when the generation AI translates a product description, the product description translation unit simultaneously translates the captions and tags of the product images. For example, the generation AI translates the product image caption "Beautiful Scenery" into English as "Beautiful Scenery" along with the Japanese product description. The generation AI also translates the product image tags. For example, the generation AI translates the Japanese tag "Fukei" into English as "Scenery." This makes the captions and tags of product images multilingual, making it easier to understand visual information.

[0032] The product description translation unit can also translate product reviews or ratings. For example, when the generation AI translates a product description, the product description translation unit simultaneously translates product reviews and ratings. For example, the generation AI translates a Japanese review such as "This product is great" into English as "This product is excellent." The generation AI also translates product reviews and ratings. For example, the generation AI translates a Japanese rating such as "5 stars" into English as "5 stars." This makes it easier for buyers to understand the opinions of other users.

[0033] The product description translation unit can provide the translation results audibly using speech synthesis technology. For example, when the generation AI translates a product description, the product description translation unit provides the translation results audibly using speech synthesis technology. For example, the generation AI translates a Japanese product description into English and plays back the translation results audibly. The generation AI also provides the translation results audibly using speech synthesis technology. For example, the generation AI provides the translation results audibly using text-to-speech technology. This makes it possible to accommodate visually impaired people and the elderly.

[0034] The communication translation unit can understand the context of the conversation and perform translation. For example, when the generation AI translates a conversation between a seller and a buyer, the communication translation unit understands the context of the conversation and provides a more appropriate translation. For example, if the buyer asks, "How long will this product take to arrive?", the generation AI will understand the context and translate it as, "It usually arrives in 2-3 days." The generation AI also understands the context of the conversation and performs translation. For example, if the seller explains, "This product is handmade," the generation AI will understand the context and translate it as, "This product is handmade." This enables appropriate translations that understand the context of the conversation.

[0035] The communication translation unit can also translate the tone or nuance of the conversation. For example, when the generation AI translates a conversation between a seller and a buyer, the communication translation unit also translates the tone and nuance of the conversation. For example, if the buyer exclaims, "This product is really amazing!", the generation AI will preserve that tone and translate it as, "This product is really amazing!" The generation AI also translates the nuance of the conversation. For example, if the seller explains, "This product is a bit expensive, but I guarantee the quality," the generation AI will preserve that nuance and translate it as, "This product is a bit expensive, but I guarantee the quality." This makes it possible to take the tone and nuance of the conversation into account when translating.

[0036] The communication translation unit can automatically save the conversation history so that it can be referenced later. For example, when the generation AI translates a conversation between a seller and a buyer, the communication translation unit automatically saves the conversation history so that it can be referenced later. For example, the generation AI searches past conversation history to check previous interactions. The generation AI also automatically saves the conversation history so that it can be referenced later. For example, the generation AI saves the conversation history as a text log so that it can be searched as needed. This allows the conversation history to be saved and referenced later.

[0037] The communication translation unit can integrate the translation results into a chatbot or virtual assistant, allowing users to have natural conversations. For example, when the generation AI translates a conversation between a seller and a buyer, the communication translation unit integrates the translation results into a chatbot or virtual assistant, allowing users to have natural conversations. For example, when a buyer asks a question to the chatbot, the generation AI translates the question and passes it on to the seller. The generation AI also translates the seller's response and passes it on to the buyer. This allows users to have natural conversations.

[0038] The cross-border procedure automation unit updates the latest regulations and laws of each country in real time and can provide the latest information. For example, the generation AI in the cross-border procedure automation unit updates the import and export regulations and laws of each country in real time and provides the latest information to sellers. For example, the generation AI automatically presents the latest documents and procedures required when exporting a specific product. The generation AI also updates the latest regulations and laws of each country in real time and provides the latest information. For example, the generation AI automatically updates information on legal amendments and the scope of regulatory application and provides this to sellers. This enables cross-border procedures to be carried out in accordance with the latest regulations and laws.

[0039] The cross-border procedure automation unit can automatically generate the necessary documents and provide them to the seller. In the cross-border procedure automation unit, for example, the generation AI automatically generates the documents necessary for import and export procedures in each country and provides them to the seller. For example, the generation AI automatically creates invoices and packing lists required when exporting specific products. The generation AI also automatically generates the necessary documents and provides them to the seller. For example, the generation AI automatically creates export licenses and customs declarations. This allows the necessary documents to be automatically generated and provided to the seller.

[0040] The cross-border procedure automation unit automatically collaborates with logistics companies and can propose the optimal delivery method. For example, when the generation AI automates cross-border procedures, the cross-border procedure automation unit automatically collaborates with logistics companies and proposes the optimal delivery method. For example, the generation AI automatically calculates the most efficient route for delivering a specific product. The generation AI also automatically collaborates with logistics companies and proposes the optimal delivery method. For example, the generation AI considers delivery time and cost to propose the optimal delivery method. This makes it possible to propose the optimal delivery method.

[0041] The cross-border procedure automation unit can display the progress of the procedure on a visual dashboard, allowing the user to intuitively understand. In the cross-border procedure automation unit, for example, the generation AI displays the results of automating the cross-border procedure on a visual dashboard, allowing the user to intuitively understand the progress of the procedure. For example, the generation AI visually displays each step of the procedure. The generation AI also displays the progress of the procedure on a visual dashboard, allowing the user to intuitively understand. For example, the generation AI displays the progress of the procedure using a progress bar or graph. This allows the user to intuitively understand the progress of the procedure.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The cross-border product trading system may further include a proposal unit that analyzes a user's purchase history and makes individually customized product proposals. For example, the proposal unit may propose highly relevant products based on products the user has previously purchased or viewed. The proposal unit may also analyze a user's purchase history and propose products that are in line with the season or trends. Furthermore, the proposal unit may preferentially propose products from specific brands or categories based on the user's purchase history. This allows for more attractive product proposals to be made to users.

[0044] The cross-border commodity trading system can also be equipped with an inventory management unit that predicts user purchasing behavior and optimizes inventory management based on the prediction. For example, the inventory management unit analyzes past purchase data and predicts when a particular product will sell well. The inventory management unit can also appropriately replenish inventory based on the prediction to prevent stockouts. Furthermore, the inventory management unit can optimize inventory allocation based on the prediction to achieve efficient logistics. This makes inventory management more efficient and reduces costs.

[0045] The cross-border product trading system can also have a repeat customer rewards section that provides rewards to repeat customers based on the user's purchasing history. For example, the repeat customer rewards section can provide discount coupons or points to users who have made multiple purchases within a certain period of time. The repeat customer rewards section can also provide rewards for the next purchase to users who have purchased a specific product multiple times. Furthermore, the repeat customer rewards section can analyze the user's purchasing history and provide exclusive products and services for repeat customers. This can increase repeat customer satisfaction and encourage continued use.

[0046] The cross-border commodity trading system may further include a price notification unit that notifies the user of price fluctuations for specific commodities based on the user's purchasing history. For example, the price notification unit may notify the user if the price of a product that the user previously purchased or viewed drops. The price notification unit may also notify the user if a specific product becomes a sale item. Furthermore, the price notification unit may analyze the user's purchasing history, predict price fluctuations, and notify the user at the appropriate time. This allows the user to purchase products at a good price.

[0047] The cross-border product trading system may further include a follow-up unit that performs post-purchase follow-up based on the user's purchase history. For example, the follow-up unit may provide instructions on how to use and maintain a product after the user purchases it. The follow-up unit may also request a review of the product the user purchased. Furthermore, the follow-up unit may analyze the user's purchase history and offer special benefits for the next purchase. This increases user satisfaction and encourages repeat customers.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The product description translation unit automatically translates the product description. For example, the generation AI automatically translates the product description entered by the seller into multiple languages. The generation AI translates the product description using text generation AI (e.g., LLM). The generation AI can also translate the product description using multimodal generation AI. The generation AI can also extract and translate important parts of the product description. For example, the text generation AI has learned from large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from the product description and translates it based on that. Step 2: The communication translation unit automatically translates communications between sellers and buyers. For example, the generation AI automatically translates communications between sellers and buyers. The generation AI translates communications using text generation AI (e.g., LLM). The generation AI can also translate communications using multimodal generation AI. The generation AI can also extract and translate important parts of communications. For example, the text generation AI has learned from large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from the communications and translates it based on that information. Step 3: The cross-border procedure automation unit automates cross-border procedures. For example, the generation AI automatically analyzes each country's import / export procedures, tariffs, and regulations, and suggests the necessary procedures. The generation AI automates cross-border procedures using text generation AI (e.g., LLM). The generation AI can also automate cross-border procedures using multimodal generation AI. The generation AI can also extract and automate important parts of cross-border procedures. For example, the text generation AI has learned from large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information in cross-border procedures and automate them based on that information.

[0050] (Example 2) The cross-border commodity trading system according to an embodiment of the present invention uses AI to realize the desire to buy and sell regardless of country. This system uses AI to overcome language and institutional barriers in product descriptions, communication, cross-border procedures, etc. As a result, the cross-border commodity trading system can facilitate commodity trading around the world and open up new shopping possibilities.

[0051] A cross-border commodity trading system according to an embodiment includes a product description translation unit, a communication translation unit, and a cross-border procedure automation unit. The product description translation unit automatically translates product descriptions. For example, a generation AI automatically translates product descriptions entered by a seller into multiple languages. The generation AI translates product descriptions using a text generation AI (e.g., LLM). The generation AI can also translate product descriptions using a multimodal generation AI. The generation AI can also extract and translate important parts of product descriptions. For example, the text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to extract particularly important information from product descriptions and performs translation based on that information. The communication translation unit automatically translates communications between sellers and buyers. For example, the generation AI automatically translates communications between sellers and buyers. The generation AI translates communications using a text generation AI (e.g., LLM). The generation AI can also translate communications using a multimodal generation AI. Generative AI can also extract and translate important parts of communications. For example, text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. Generative AI uses keyword extraction technology to identify particularly important information in communications and translates based on that information. The cross-border procedure automation unit automates cross-border procedures. For example, generative AI automatically analyzes each country's import and export procedures, tariffs, and regulations, and suggests the necessary procedures. Generative AI automates cross-border procedures using text generation AI (e.g., LLM). Generative AI can also automate cross-border procedures using multimodal generation AI. Generative AI can also extract and automate important parts of cross-border procedures. For example, text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities.The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to identify particularly important information in cross-border procedures and automates the process based on that information. This allows the cross-border commodity trading system according to the embodiment to overcome language and institutional barriers by utilizing AI, facilitating commodity trading around the world. For example, sellers can translate product descriptions into multiple languages, allowing buyers to understand the product descriptions in their own language. Furthermore, communication between sellers and buyers is automatically translated, allowing for smooth transactions without the need for language barriers. Furthermore, cross-border procedures are also automated, simplifying complex procedures and enabling smoother transactions.

[0052] The product description translation unit can take into account cultural background and regional expressions when translating. For example, when the generation AI translates a Japanese product description into English, it takes into account Japan's cultural background and regional expressions to convert it into expressions that are natural for English-speaking consumers. For example, the generation AI appropriately expresses the concept of "omotenashi" (hospitality) included in traditional Japanese product descriptions as "hospitality" in English. The generation AI also takes into account regional expressions when translating. For example, when translating a product description in Kansai dialect into standard Japanese, the generation AI takes into account Kansai-specific expressions and converts it into standard Japanese. This enables natural translation that takes into account cultural background and regional expressions.

[0053] The product description translation unit can also translate the captions and tags of product images. For example, when the generation AI translates a product description, the product description translation unit simultaneously translates the captions and tags of the product images. For example, the generation AI translates the product image caption "Beautiful Scenery" into English as "Beautiful Scenery" along with the Japanese product description. The generation AI also translates the product image tags. For example, the generation AI translates the Japanese tag "Fukei" into English as "Scenery." This makes the captions and tags of product images multilingual, making it easier to understand visual information.

[0054] The product description translation unit can use the emotion estimation function to analyze the user's emotions and select expressions that elicit positive emotions. For example, when the generation AI translates a product description, the product description translation unit uses the emotion estimation function to analyze the user's emotions and select expressions that elicit positive emotions. For example, the generation AI increases the user's trust by including "high quality" in the product description. The generation AI also analyzes the user's emotions and selects expressions that elicit positive emotions. For example, the generation AI captures the user's facial expressions while reading the product description with a camera and analyzes the emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions and selects expressions that elicit positive emotions. This makes it possible to translate products that take the user's emotions into consideration.

[0055] The product description translation unit can also translate product reviews or ratings. For example, when the generation AI translates a product description, the product description translation unit simultaneously translates product reviews and ratings. For example, the generation AI translates a Japanese review such as "This product is great" into English as "This product is excellent." The generation AI also translates product reviews and ratings. For example, the generation AI translates a Japanese rating such as "5 stars" into English as "5 stars." This makes it easier for buyers to understand the opinions of other users.

[0056] The product description translation unit can provide the translation results audibly using speech synthesis technology. For example, when the generation AI translates a product description, the product description translation unit provides the translation results audibly using speech synthesis technology. For example, the generation AI translates a Japanese product description into English and plays back the translation results audibly. The generation AI also provides the translation results audibly using speech synthesis technology. For example, the generation AI provides the translation results audibly using text-to-speech technology. This makes it possible to accommodate visually impaired people and the elderly.

[0057] The product description translation unit can use the emotion estimation function to monitor the user's emotions in real time and dynamically adjust the translation content. For example, when the generation AI translates a product description, the product description translation unit uses the emotion estimation function to monitor the user's emotions in real time and dynamically adjust the translation content. For example, if the user expresses positive emotions, the generation AI adds expressions that emphasize those emotions. The generation AI also monitors the user's emotions in real time and dynamically adjusts the translation content. For example, if the user expresses negative emotions, the generation AI adds expressions that soften those emotions. This makes it possible to translate in accordance with the user's emotions.

[0058] The communication translation unit can understand the context of the conversation and perform translation. For example, when the generation AI translates a conversation between a seller and a buyer, the communication translation unit understands the context of the conversation and provides a more appropriate translation. For example, if the buyer asks, "How long will this product take to arrive?", the generation AI will understand the context and translate it as, "It usually arrives in 2-3 days." The generation AI also understands the context of the conversation and performs translation. For example, if the seller explains, "This product is handmade," the generation AI will understand the context and translate it as, "This product is handmade." This enables appropriate translations that understand the context of the conversation.

[0059] The communication translation unit can also translate the tone or nuance of the conversation. For example, when the generation AI translates a conversation between a seller and a buyer, the communication translation unit also translates the tone and nuance of the conversation. For example, if the buyer exclaims, "This product is really amazing!", the generation AI will preserve that tone and translate it as, "This product is really amazing!" The generation AI also translates the nuance of the conversation. For example, if the seller explains, "This product is a bit expensive, but I guarantee the quality," the generation AI will preserve that nuance and translate it as, "This product is a bit expensive, but I guarantee the quality." This makes it possible to take the tone and nuance of the conversation into account when translating.

[0060] The communication translation unit can analyze the user's emotions using the emotion estimation function and select appropriate expressions according to the emotions. For example, when the generation AI translates a conversation between a seller and a buyer, the communication translation unit uses the emotion estimation function to analyze the user's emotions and select appropriate expressions according to the emotions. For example, if the buyer is excited, the generation AI selects expressions that reflect that emotion. The generation AI also analyzes the user's emotions and selects appropriate expressions according to the emotions. For example, if the user is dissatisfied, the generation AI selects expressions that will soothe that emotion. This makes it possible to select appropriate expressions according to the user's emotions.

[0061] The communication translation unit can automatically save the conversation history so that it can be referenced later. For example, when the generation AI translates a conversation between a seller and a buyer, the communication translation unit automatically saves the conversation history so that it can be referenced later. For example, the generation AI searches past conversation history to check previous interactions. The generation AI also automatically saves the conversation history so that it can be referenced later. For example, the generation AI saves the conversation history as a text log so that it can be searched as needed. This allows the conversation history to be saved and referenced later.

[0062] The communication translation unit can integrate the translation results into a chatbot or virtual assistant, allowing users to have natural conversations. For example, when the generation AI translates a conversation between a seller and a buyer, the communication translation unit integrates the translation results into a chatbot or virtual assistant, allowing users to have natural conversations. For example, when a buyer asks a question to the chatbot, the generation AI translates the question and passes it on to the seller. The generation AI also translates the seller's response and passes it on to the buyer. This allows users to have natural conversations.

[0063] The communication translation unit can monitor the user's emotions in real time using the emotion estimation function and dynamically adjust the flow of the conversation. For example, when the generation AI translates a conversation between a seller and a buyer, the communication translation unit can monitor the user's emotions in real time using the emotion estimation function and dynamically adjust the flow of the conversation. For example, if the user expresses dissatisfaction, the generation AI adds expressions to alleviate that emotion. The generation AI also monitors the user's emotions in real time and dynamically adjusts the flow of the conversation. For example, if the user expresses joy, the generation AI adds expressions to emphasize that emotion. This makes it possible to dynamically adjust the flow of the conversation according to the user's emotions.

[0064] The cross-border procedure automation unit updates the latest regulations and laws of each country in real time and can provide the latest information. For example, the generation AI in the cross-border procedure automation unit updates the import and export regulations and laws of each country in real time and provides the latest information to sellers. For example, the generation AI automatically presents the latest documents and procedures required when exporting a specific product. The generation AI also updates the latest regulations and laws of each country in real time and provides the latest information. For example, the generation AI automatically updates information on legal amendments and the scope of regulatory application and provides this to sellers. This enables cross-border procedures to be carried out in accordance with the latest regulations and laws.

[0065] The cross-border procedure automation unit can automatically generate the necessary documents and provide them to the seller. In the cross-border procedure automation unit, for example, the generation AI automatically generates the documents necessary for import and export procedures in each country and provides them to the seller. For example, the generation AI automatically creates invoices and packing lists required when exporting specific products. The generation AI also automatically generates the necessary documents and provides them to the seller. For example, the generation AI automatically creates export licenses and customs declarations. This allows the necessary documents to be automatically generated and provided to the seller.

[0066] The border crossing procedure automation unit can use the emotion estimation function to analyze the user's emotions and make suggestions to reduce stress. For example, when the generation AI automates border crossing procedures, the border crossing procedure automation unit uses the emotion estimation function to analyze the user's emotions and make suggestions to reduce stress. For example, if the procedures are complicated, the generation AI makes suggestions to simplify the procedures. The generation AI also analyzes the user's emotions and makes suggestions to reduce stress. For example, if the user is feeling stressed, the generation AI suggests relaxation methods to ease those emotions. This makes it possible to make suggestions to reduce the user's stress.

[0067] The cross-border procedure automation unit automatically collaborates with logistics companies and can propose the optimal delivery method. For example, when the generation AI automates cross-border procedures, the cross-border procedure automation unit automatically collaborates with logistics companies and proposes the optimal delivery method. For example, the generation AI automatically calculates the most efficient route for delivering a specific product. The generation AI also automatically collaborates with logistics companies and proposes the optimal delivery method. For example, the generation AI considers delivery time and cost to propose the optimal delivery method. This makes it possible to propose the optimal delivery method.

[0068] The cross-border procedure automation unit can display the progress of the procedure on a visual dashboard, allowing the user to intuitively understand. In the cross-border procedure automation unit, for example, the generation AI displays the results of automating the cross-border procedure on a visual dashboard, allowing the user to intuitively understand the progress of the procedure. For example, the generation AI visually displays each step of the procedure. The generation AI also displays the progress of the procedure on a visual dashboard, allowing the user to intuitively understand. For example, the generation AI displays the progress of the procedure using a progress bar or graph. This allows the user to intuitively understand the progress of the procedure.

[0069] The border crossing procedure automation unit can use the emotion estimation function to monitor the user's emotions in real time and dynamically adjust the progress of the procedure. For example, when the generation AI automates border crossing procedures, the border crossing procedure automation unit uses the emotion estimation function to monitor the user's emotions in real time and dynamically adjust the progress of the procedure. For example, if the user is feeling stressed, the generation AI makes suggestions to simplify the procedure. The generation AI also monitors the user's emotions in real time and dynamically adjusts the progress of the procedure. For example, if the user shows a sense of relief, the generation AI will speed up the procedure. This makes it possible to dynamically adjust the progress of the procedure according to the user's emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The cross-border product trading system may further include a proposal unit that analyzes a user's purchase history and makes individually customized product proposals. For example, the proposal unit may propose highly relevant products based on products the user has previously purchased or viewed. The proposal unit may also analyze a user's purchase history and propose products that are in line with the season or trends. Furthermore, the proposal unit may preferentially propose products from specific brands or categories based on the user's purchase history. This allows for more attractive product proposals to be made to users.

[0072] The cross-border commodity trading system may further include a customer support unit that estimates the user's emotions and adjusts customer support responses based on the estimated emotions. For example, if the user expresses dissatisfaction, the customer support unit may respond in a courteous manner to alleviate the user's emotions. Alternatively, if the user expresses joy, the customer support unit may offer special offers or discounts to further enhance the user's emotions. Furthermore, the customer support unit may monitor the user's emotions in real time and contact the user for follow-up at an appropriate time. This enables flexible customer support that responds to the user's emotions.

[0073] The cross-border commodity trading system can also be equipped with an inventory management unit that predicts user purchasing behavior and optimizes inventory management based on the prediction. For example, the inventory management unit analyzes past purchase data and predicts when a particular product will sell well. The inventory management unit can also appropriately replenish inventory based on the prediction to prevent stockouts. Furthermore, the inventory management unit can optimize inventory allocation based on the prediction to achieve efficient logistics. This makes inventory management more efficient and reduces costs.

[0074] The cross-border commodity trading system may further include an advertisement display unit that estimates a user's emotions and optimizes advertisement display based on the estimated emotions. For example, if the user is excited, the advertisement display unit may display an attractive advertisement to further enhance the user's emotions. Alternatively, if the user is depressed, the advertisement display unit may display a relaxing advertisement to soothe the user's emotions. Furthermore, the advertisement display unit may monitor the user's emotions in real time and display advertisements at appropriate times. This enables effective advertisement display according to the user's emotions.

[0075] The cross-border product trading system can also have a repeat customer rewards section that provides rewards to repeat customers based on the user's purchasing history. For example, the repeat customer rewards section can provide discount coupons or points to users who have made multiple purchases within a certain period of time. The repeat customer rewards section can also provide rewards for the next purchase to users who have purchased a specific product multiple times. Furthermore, the repeat customer rewards section can analyze the user's purchasing history and provide exclusive products and services for repeat customers. This can increase repeat customer satisfaction and encourage continued use.

[0076] The cross-border commodity trading system may further include a message sending unit that estimates the user's emotions and sends a personalized message based on the estimated emotions. For example, if the user expresses joy, the message sending unit may send a message of gratitude to further enhance the emotion. Alternatively, if the user expresses dissatisfaction, the message sending unit may send a message of apology to alleviate the emotion. Furthermore, the message sending unit may monitor the user's emotions in real time and send a follow-up message at an appropriate time. This enables personalized message sending according to the user's emotions.

[0077] The cross-border commodity trading system may further include a price notification unit that notifies the user of price fluctuations for specific commodities based on the user's purchasing history. For example, the price notification unit may notify the user if the price of a product that the user previously purchased or viewed drops. The price notification unit may also notify the user if a specific product becomes a sale item. Furthermore, the price notification unit may analyze the user's purchasing history, predict price fluctuations, and notify the user at the appropriate time. This allows the user to purchase products at a good price.

[0078] The cross-border commodity trading system may further include a promotion unit that estimates a user's emotions and provides promotions to increase purchasing motivation based on the estimated emotions. For example, if the user is excited, the promotion unit may provide a limited sale to further increase the user's emotions. Alternatively, if the user is depressed, the promotion unit may provide a special discount to ease the user's emotions. Furthermore, the promotion unit may monitor the user's emotions in real time and provide promotions at appropriate times. This enables effective promotions according to the user's emotions.

[0079] The cross-border product trading system may further include a follow-up unit that performs post-purchase follow-up based on the user's purchase history. For example, the follow-up unit may provide instructions on how to use and maintain a product after the user purchases it. The follow-up unit may also request a review of the product the user purchased. Furthermore, the follow-up unit may analyze the user's purchase history and offer special benefits for the next purchase. This increases user satisfaction and encourages repeat customers.

[0080] The cross-border commodity trading system may further include a customer support unit that estimates the user's emotions and adjusts customer support responses based on the estimated emotions. For example, if the user expresses dissatisfaction, the customer support unit may respond in a courteous manner to alleviate the user's emotions. Alternatively, if the user expresses joy, the customer support unit may offer special offers or discounts to further enhance the user's emotions. Furthermore, the customer support unit may monitor the user's emotions in real time and contact the user for follow-up at an appropriate time. This enables flexible customer support that responds to the user's emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The product description translation unit automatically translates the product description. For example, the generation AI automatically translates the product description entered by the seller into multiple languages. The generation AI translates the product description using text generation AI (e.g., LLM). The generation AI can also translate the product description using multimodal generation AI. The generation AI can also extract and translate important parts of the product description. For example, the text generation AI has learned from large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from the product description and translates it based on that. Step 2: The communication translation unit automatically translates communications between sellers and buyers. For example, the generation AI automatically translates communications between sellers and buyers. The generation AI translates communications using text generation AI (e.g., LLM). The generation AI can also translate communications using multimodal generation AI. The generation AI can also extract and translate important parts of communications. For example, the text generation AI has learned from large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from the communications and translates it based on that information. Step 3: The cross-border procedure automation unit automates cross-border procedures. For example, the generation AI automatically analyzes each country's import / export procedures, tariffs, and regulations, and suggests the necessary procedures. The generation AI automates cross-border procedures using text generation AI (e.g., LLM). The generation AI can also automate cross-border procedures using multimodal generation AI. The generation AI can also extract and automate important parts of cross-border procedures. For example, the text generation AI has learned from large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information in cross-border procedures and automate them based on that information.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A product description translation section that automatically translates product descriptions, a communication translation unit that automatically translates communications between sellers and buyers; and a cross-border procedure automation unit that automates cross-border procedures. A system characterized by:

2. The product description translation department: Translations that take into account cultural background and local expressions 2. The system of claim 1.

3. The communication translation unit: Understand the context of the conversation and translate 2. The system of claim 1.

4. The cross-border procedure automation department Provides real-time updates on the latest regulations and laws of each country to provide the latest information 2. The system of claim 1.

5. The product description translation department: Analyze user emotions and select expressions that elicit positive emotions 2. The system of claim 1.

6. The communication translation unit: Analyze the user's emotions and select appropriate expressions according to their emotions 2. The system of claim 1.

7. The cross-border procedure automation department Analyze user emotions and make suggestions to reduce stress 2. The system of claim 1.

8. The cross-border procedure automation department Monitor user emotions in real time and dynamically adjust the progress of the procedure 2. The system of claim 1.

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