system

Abstract

The system according to the embodiment aims to provide multilingual support and 24-hour service, thereby improving the efficiency of customer service. [Solution] A system according to an embodiment includes a reception unit, a provision unit, a recommendation unit, a support unit, and an after-sales service unit. The reception unit receives customer inquiries. The provision unit provides product information based on the inquiries received by the reception unit. The recommendation unit recommends products to customers based on the product information provided by the provision unit. The support unit supports customer orders based on the products recommended by the recommendation unit. The after-sales service unit provides after-sales service to customers based on orders supported by the support unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately support multiple languages ​​or 24-hour service in cross-border e-commerce, and there is room for improvement in the efficiency of customer service.

[0005] The system according to the embodiment aims to provide multilingual support and 24-hour service, thereby improving the efficiency of customer service. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a provision unit, a recommendation unit, a support unit, and an after-sales service unit. The reception unit receives customer inquiries. The provision unit provides product information based on the inquiries received by the reception unit. The recommendation unit recommends products to customers based on the product information provided by the provision unit. The support unit supports customer orders based on the products recommended by the recommendation unit. The after-sales service unit provides after-sales service to customers based on orders supported by the support unit. [Effects of the Invention]

[0007] The system according to the embodiment provides multilingual support and 24-hour service, making customer service more efficient. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A multilingual chat commerce system according to an embodiment of the present invention is developed using generative AI. This multilingual chat commerce system is designed to enhance customer support in cross-border e-commerce. The multilingual chat commerce system uses generative AI to support multiple languages, enabling communication with customers around the world. For example, it can converse in many languages, including English, Japanese, Chinese, and Spanish. The AI ​​is available 24 hours a day, 365 days a year, responding quickly to customer inquiries. Furthermore, it has a product information provision function that provides customers with real-time information they need, such as product details, stock availability, and delivery options, enabling them to make quick purchasing decisions. It also has a purchasing assistance function that can recommend and compare products based on customer questions. For example, if a customer inquires via chat, "I'm looking for a waterproof camera for outdoor use," the generative AI might suggest a product recommendation such as, "How about our popular waterproof action camera X? It can take high-resolution images and is highly waterproof." The e-commerce site also allows product comparison suggestions. For example, if a customer asks, "What's the difference between Product A and Product B?", the AI ​​will compare the product features, such as, "Product A has excellent waterproofing, while Product B has a long battery life. Consider which one best suits your needs." Regarding order support, if a customer asks, "I'd like to order this product. What should I do?", the AI ​​will support the ordering process by providing a response such as, "Add the product name to your cart and proceed with the checkout process. Enter the required information and select your payment method." Furthermore, the site also offers comprehensive after-sales service. If a customer inquires, "My product hasn't arrived. What should I do?", the AI ​​will respond by providing a response such as, "I'm sorry. Could you please let me know your order number? I'll check the delivery status based on that." This allows the multilingual chat commerce system to respond flexibly to customer needs. This significantly enhances customer service at inbound facilities and cross-border e-commerce sites.

[0029] A multilingual chat commerce system according to an embodiment includes a reception unit, a provision unit, a recommendation unit, a support unit, and an after-sales service unit. The reception unit receives customer inquiries. For example, the reception unit can receive inquiries when customers make inquiries through chat. The provision unit provides product information based on the inquiries received by the reception unit. For example, the provision unit can provide information such as product details, stock status, and delivery options in real time. The recommendation unit recommends products to customers based on the product information provided by the provision unit. For example, the recommendation unit can recommend and compare products in response to customer questions. The support unit supports customer orders based on products recommended by the recommendation unit. For example, the support unit can provide necessary information and support the order process when a customer adds a product to a cart and proceeds with the checkout procedure. The after-sales service unit provides customer after-sales service based on orders supported by the support unit. For example, the after-sales service unit can handle order inquiries, check delivery status, and return / exchange procedures. This enables the multilingual chat commerce system according to an embodiment to provide consistent support from customer inquiries to after-sales service.

[0030] The reception unit includes a translation unit that automatically detects the language of the customer and translates into that language. The translation unit can automatically detect the language of the customer using, for example, voice recognition technology. For example, the translation unit analyzes text entered by the customer in chat and identifies that language. The translation unit can also translate into the identified language using machine translation technology. For example, the translation unit translates text entered by the customer in real time and converts it into a corresponding language. This allows the reception unit to automatically detect the language of the customer and translate it, enabling multilingual support.

[0031] The providing unit can provide information on product details, stock status, and delivery options in real time. The providing unit, for example, provides product details in real time. For example, the providing unit can provide detailed information such as product specifications, prices, and reviews. The providing unit can also provide stock status in real time. For example, the providing unit provides information such as the number of products in stock and the expected arrival date. The providing unit can also provide information on delivery options in real time. For example, the providing unit provides information such as delivery method, delivery time, and shipping fee. In this way, the providing unit can provide the information that customers need in real time, enabling them to make quick purchasing decisions.

[0032] The recommendation unit can recommend products in response to customer questions and compare product features. For example, if a customer inquires via chat, "I'm looking for a waterproof camera for outdoor use," the recommendation unit can use the generation AI to suggest a recommended product, such as, "How about our popular waterproof action camera X? It can take high-resolution images and is highly waterproof." Similarly, if a customer asks, "What's the difference between product A and product B?" the recommendation unit can use the generation AI to compare product features, such as, "Product A has excellent waterproof performance, while product B has a long battery life. Consider which one best suits your needs." This allows the recommendation unit to recommend and compare the best products in response to customer questions, improving the customer's purchasing experience.

[0033] The support department can support the customer's ordering process. For example, if a customer asks, "I want to order this product. What should I do?", the support department can use generative AI to support the ordering process by saying, "Please add the product name to your cart and proceed with the checkout. Enter the required information and select your payment method." The support department can also provide the necessary information and support the ordering process as the customer proceeds with the order. For example, the support department can confirm the information the customer enters and provide advice on selecting a payment method. In this way, the support department can support the customer's ordering process, enabling a smooth purchase.

[0034] The after-sales service department can handle order inquiries, check delivery status, and return / exchange procedures. For example, if a customer asks, "My product hasn't arrived yet, what should I do?", the after-sales service department can respond using generative AI by saying, "I'm sorry. Could you please tell me your order number? I'll check the delivery status based on that." The after-sales service department can also provide necessary information when customers inquire about orders and respond to their inquiries. For example, the after-sales service department can provide information for customers to check their order status and track their delivery status. The after-sales service department can also handle return / exchange procedures. For example, if a customer wishes to return or exchange an item, the after-sales service department can guide them through the necessary procedures and conditions. This allows the after-sales service department to enhance customer after-sales service by handling order inquiries, checking delivery status, and return / exchange procedures.

[0035] The reception department can analyze the customer's past inquiry history and select a response method. The reception department can analyze the customer's past inquiry history, for example, using generative AI. For example, the reception department can analyze the customer's past inquiry content and response history, and select the optimal response method based on that information. Also, if the customer has made a similar inquiry in the past, the reception department can respond quickly based on that history. Furthermore, if the customer has had a specific problem in the past, the reception department can also propose the optimal solution to that problem. In this way, the reception department can select the optimal response method by analyzing the customer's past inquiry history.

[0036] When receiving an inquiry, the reception unit can filter inquiries based on the customer's current purchase history and areas of interest. The reception unit can, for example, use generative AI to analyze the customer's current purchase history and areas of interest. For example, the reception unit prioritizes inquiries related to products recently purchased by the customer. The reception unit can also filter inquiries based on product categories in which the customer is interested. Furthermore, the reception unit can analyze the customer's purchase history and respond quickly to related inquiries. This allows the reception unit to provide more appropriate responses by filtering based on the customer's purchase history and areas of interest.

[0037] When receiving an inquiry, the reception unit can prioritize obtaining highly relevant information by taking into account the geographical location information of the customer. The reception unit can obtain the geographical location information of the customer by using, for example, generative AI. For example, if the customer is in a specific area, the reception unit can prioritize obtaining information related to that area. Also, if the customer is traveling, the reception unit can provide information related to the customer's travel destination. Furthermore, the reception unit can guide the customer to the most suitable support center based on the customer's current location. In this way, the reception unit can prioritize obtaining highly relevant information by taking into account the geographical location information of the customer.

[0038] When receiving an inquiry, the reception unit can analyze the customer's social media activity and obtain relevant information. The reception unit can, for example, use generative AI to analyze the customer's social media activity. For example, if the customer mentions a specific product on social media, the reception unit can respond based on that information. The reception unit can also identify products of interest from the customer's social media activity and provide related information. Furthermore, the reception unit can analyze the customer's feedback on social media and select the optimal way to respond. This allows the reception unit to obtain relevant information by analyzing the customer's social media activity.

[0039] When providing product information, the provision unit can adjust the level of detail of the information based on the importance of the product. The provision unit can, for example, use a generative AI to evaluate the importance of the product. For example, the provision unit evaluates the importance of the product based on information such as product sales, inventory status, and customer reviews. The provision unit can also adjust the level of detail of the information based on the importance of the product. For example, the provision unit can provide detailed descriptions and images for important products. The provision unit can also provide concise descriptions for general products. Furthermore, the provision unit can provide detailed specifications and reviews for expensive products. In this way, the provision unit can adjust the level of detail of the information based on the importance of the product, thereby providing optimal information to customers.

[0040] When providing product information, the providing unit can apply different information provision algorithms depending on the product category. The providing unit can, for example, identify the product category using generative AI. For example, the providing unit can identify the category based on the product's attributes and tags. The providing unit can also apply different information provision algorithms depending on the product category. For example, the providing unit can provide information that emphasizes specifications and reviews for electronic devices. For fashion items, the providing unit can also provide information that emphasizes images and styling suggestions. Furthermore, for food, the providing unit can provide information that emphasizes ingredients and nutritional information. In this way, the providing unit can provide the most suitable information to customers by applying an information provision algorithm depending on the product category.

[0041] When providing product information, the providing unit can determine the priority of information based on the time of product submission. The providing unit can, for example, use a generation AI to identify the time of product submission. For example, the providing unit can identify the time of submission based on information such as the product release date and sale period. The providing unit can also determine the priority of information based on the time of product submission. For example, the providing unit can provide information preferentially about new products. The providing unit can also provide information preferentially about products on sale. Furthermore, the providing unit can provide information about seasonal products according to the season. In this way, the providing unit can determine the priority of information based on the time of product submission, thereby providing the most appropriate information to customers.

[0042] When providing product information, the providing unit can adjust the order of information based on the relevance of the products. The providing unit can, for example, use a generative AI to evaluate the relevance of the products. For example, the providing unit evaluates the relevance of the products based on the customer's purchase history and search history. The providing unit can also adjust the order of information based on the relevance of the products. For example, the providing unit can preferentially display products in which the customer is interested. The providing unit can also preferentially display products that are highly relevant based on the customer's purchase history. Furthermore, the providing unit can also preferentially display products that are highly relevant based on the customer's search history. In this way, the providing unit can adjust the order of information based on the relevance of the products, thereby providing optimal information to the customer.

[0043] When selecting recommended products, the recommendation unit can analyze the customer's past purchase history and select the most suitable product. The recommendation unit can, for example, use generation AI to analyze the customer's past purchase history. For example, the recommendation unit can recommend products related to products that the customer has previously purchased. The recommendation unit can also recommend products in the same category based on the customer's purchase history. Furthermore, the recommendation unit can analyze the customer's purchase history and recommend the most highly rated product. This allows the recommendation unit to select the most suitable product by analyzing the customer's past purchase history.

[0044] When selecting recommended products, the recommendation unit can customize products based on the customer's current living situation. The recommendation unit can, for example, use generative AI to identify the customer's current living situation. For example, the recommendation unit can identify the customer's current living situation based on information such as the customer's family composition, income, and lifestyle. The recommendation unit can also customize products based on the customer's current living situation. For example, if the customer is traveling, the recommendation unit can recommend travel-related products. If the customer is starting a new life, the recommendation unit can also recommend products necessary for that new life. Furthermore, if the customer is attending a specific event, the recommendation unit can recommend products related to the event. This allows the recommendation unit to customize products based on the customer's current living situation, enabling more appropriate product recommendations.

[0045] When selecting recommended products, the recommendation unit can select the most suitable product by taking into account the customer's geographical location information. The recommendation unit can obtain the customer's geographical location information by using, for example, generation AI. For example, if the customer is in a specific area, the recommendation unit can recommend products that are popular in that area. Also, if the customer is traveling, the recommendation unit can recommend products that can be used at the customer's travel destination. Furthermore, the recommendation unit can select the most suitable product based on the customer's current location. This allows the recommendation unit to select the most suitable product by taking into account the customer's geographical location information.

[0046] When selecting recommended products, the recommendation unit can analyze the customer's social media activity and suggest products. The recommendation unit can, for example, use generative AI to analyze the customer's social media activity. For example, if the customer mentions a specific product on social media, the recommendation unit recommends that product. The recommendation unit can also identify products that the customer is interested in from the customer's social media activity and recommend them. Furthermore, the recommendation unit can analyze the customer's feedback on social media and suggest optimal products. This allows the recommendation unit to suggest optimal products by analyzing the customer's social media activity.

[0047] When providing support, the support department can analyze the customer's past consumption behavior and select the optimal support method. The support department can, for example, use generative AI to analyze the customer's past consumption behavior. For example, if the customer has had a similar problem in the past, the support department can respond based on that history. The support department can also select the optimal support method based on the customer's consumption behavior. Furthermore, the support department can also prioritize support methods that the customer has given high ratings to in the past. This allows the support department to select the optimal support method by analyzing the customer's past consumption behavior.

[0048] When providing support, the support department can customize the means of support based on the customer's current living situation. The support department can, for example, use generative AI to identify the customer's current living situation. For example, the support department can identify the customer's current living situation based on information such as the customer's family composition, income, and lifestyle. The support department can also customize the means of support based on the customer's current living situation. For example, if the customer is traveling, the support department can provide support at the customer's destination. Also, if the customer is starting a new life, the support department can provide support necessary for that new life. Furthermore, if the customer is participating in a specific event, the support department can provide support related to that event. This allows the support department to customize the means of support based on the customer's current living situation, enabling more appropriate support.

[0049] When providing support, the support department can select the optimal support method by taking into account the customer's geographic location information. The support department can obtain the customer's geographic location information using, for example, generative AI. For example, if the customer is in a specific area, the support department can provide support related to that area. Also, if the customer is traveling, the support department can provide support at the customer's destination. Furthermore, the support department can guide the customer to the optimal support center based on the customer's current location. This allows the support department to select the optimal support method by taking into account the customer's geographic location information.

[0050] When providing support, the support department can analyze the customer's social media activity and suggest support methods. The support department can, for example, use generative AI to analyze the customer's social media activity. For example, if the customer mentions a specific product on social media, the support department can respond based on that information. The support department can also identify products that the customer is interested in from the customer's social media activity and provide related support. Furthermore, the support department can analyze the customer's social media feedback and select the optimal support method. This allows the support department to suggest the optimal support method by analyzing the customer's social media activity.

[0051] During after-sales service, the after-sales service department can analyze the customer's past consumption behavior and select the optimal after-sales service method. The after-sales service department can, for example, use generative AI to analyze the customer's past consumption behavior. For example, if the customer has had a similar problem in the past, the after-sales service department can respond based on that history. The after-sales service department can also select the optimal after-sales service method based on the customer's consumption behavior. Furthermore, the after-sales service department can prioritize after-sales service methods that the customer has given high ratings to in the past. This allows the after-sales service department to select the optimal after-sales service method by analyzing the customer's past consumption behavior.

[0052] The after-sales service department can customize after-sales service methods based on the customer's current living situation during after-sales service. The after-sales service department can, for example, use generative AI to identify the customer's current living situation. For example, the after-sales service department can identify the customer's current living situation based on information such as the customer's family composition, income, and lifestyle. The after-sales service department can also customize after-sales service methods based on the customer's current living situation. For example, if the customer is traveling, the after-sales service department can provide after-sales service at the customer's destination. Furthermore, if the customer is starting a new life, the after-sales service department can also provide after-sales service necessary for that new life. Furthermore, if the customer is attending a specific event, the after-sales service department can also provide after-sales service related to that event. This allows the after-sales service department to customize after-sales service methods based on the customer's current living situation, enabling more appropriate after-sales service.

[0053] During after-sales service, the after-sales service department can select the optimal after-sales service method by taking into account the customer's geographical location information. The after-sales service department can obtain the customer's geographical location information using, for example, generative AI. For example, if the customer is in a specific area, the after-sales service department can provide after-sales service related to that area. Also, if the customer is traveling, the after-sales service department can provide after-sales service at the customer's destination. Furthermore, the after-sales service department can guide the customer to the optimal after-sales service center based on the customer's current location. This allows the after-sales service department to select the optimal after-sales service method by taking into account the customer's geographical location information.

[0054] During after-sales service, the after-sales service department can analyze the customer's social media activity and propose after-sales service methods. The after-sales service department can, for example, use generative AI to analyze the customer's social media activity. For example, if the customer mentions a specific product on social media, the after-sales service department can respond based on that information. The after-sales service department can also identify products of interest from the customer's social media activity and provide related after-sales service. Furthermore, the after-sales service department can analyze the customer's social media feedback and select the optimal after-sales service method. In this way, the after-sales service department can propose the optimal after-sales service method by analyzing the customer's social media activity.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception department can automatically search for and provide relevant FAQs based on the content of a customer's inquiry. For example, if a customer inquires, "How do I return an item?", the reception department can search for relevant FAQs and respond in the form of, "Please refer to this link for information on how to return an item." In addition, if a customer asks, "How long is the product warranty period?", the reception department can provide information from the FAQ such as, "The product warranty period is one year." Furthermore, if a customer inquires, "What are the payment methods?", the reception department can provide information from the FAQ such as, "Credit cards, debit cards, and PayPal are available." This allows the reception department to respond to customer inquiries quickly.

[0057] The provision unit can provide coupons and discount information for related products based on the customer's purchase history. For example, if a new product related to a product that the customer previously purchased is released, the provision unit can provide coupon information in the form of "We will send you a discount coupon for the new product." In addition, if a customer frequently purchases products in a specific category, the provision unit can also provide discount information for products in that category. Furthermore, if a customer has purchased expensive products in the past, the provision unit can provide special discount information to that customer. This allows the provision unit to provide more personalized information based on the customer's purchase history.

[0058] The recommendation unit can analyze a customer's social media activity and recommend products that they are interested in. For example, if a customer mentions a specific product on social media, the recommendation unit can recommend related products by saying, "It seems you're interested in this product. We also recommend this product." The recommendation unit can also recommend related products based on information about brands and influencers that the customer follows on social media. Furthermore, the recommendation unit can analyze the content of posts that customers share on social media and recommend products based on that content. This allows the recommendation unit to utilize a customer's social media activity to recommend more appropriate products.

[0059] The support department can provide the most appropriate support method based on the customer's current living situation. For example, if a customer is starting a new life, the support department can provide support in the form of, "Here are the products you need for your new life." If the customer is traveling, the support department can provide travel-related support in the form of, "Here is support for your travel destination." Furthermore, if the customer is participating in a specific event, the support department can provide event-related support in the form of, "Here are products related to the event." This allows the support department to provide more appropriate support based on the customer's current living situation.

[0060] The after-sales service department can analyze a customer's past consumption behavior and select the most appropriate after-sales service method. For example, if a customer has had a similar problem in the past, they can respond based on that history. They can also prioritize after-sales service methods that the customer has given high ratings to in the past. Furthermore, if a customer has frequently reported a particular problem in the past, they can propose the most appropriate solution to that problem. This allows the after-sales service department to provide more appropriate after-sales service by analyzing a customer's past consumption behavior.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives an inquiry from a customer. For example, when a customer makes an inquiry through chat, the reception unit can receive the inquiry. Step 2: The providing unit provides product information based on the inquiry received by the receiving unit. For example, the providing unit can provide information such as product details, stock status, and delivery options in real time. Step 3: The recommendation unit recommends products to the customer based on the product information provided by the provision unit. For example, the recommendation unit can recommend and compare products in response to a customer's question. Step 4: The support department assists the customer in placing an order based on the products recommended by the recommendation department. For example, the support department can provide necessary information and support the order process when the customer adds products to the cart and proceeds with the checkout procedure. Step 5: The after-sales service department provides customer after-sales service based on the order supported by the support department. For example, the after-sales service department can handle order inquiries, delivery status checks, and return / exchange procedures.

[0063] (Example 2) A multilingual chat commerce system according to an embodiment of the present invention is developed using generative AI. This multilingual chat commerce system is designed to enhance customer support in cross-border e-commerce. The multilingual chat commerce system uses generative AI to support multiple languages, enabling communication with customers around the world. For example, it can converse in many languages, including English, Japanese, Chinese, and Spanish. The AI ​​is available 24 hours a day, 365 days a year, responding quickly to customer inquiries. Furthermore, it has a product information provision function that provides customers with real-time information they need, such as product details, stock availability, and delivery options, enabling them to make quick purchasing decisions. It also has a purchasing assistance function that can recommend and compare products based on customer questions. For example, if a customer inquires via chat, "I'm looking for a waterproof camera for outdoor use," the generative AI might suggest a product recommendation such as, "How about our popular waterproof action camera X? It can take high-resolution images and is highly waterproof." The e-commerce site also allows product comparison suggestions. For example, if a customer asks, "What's the difference between Product A and Product B?", the AI ​​will compare the product features, such as, "Product A has excellent waterproofing, while Product B has a long battery life. Consider which one best suits your needs." Regarding order support, if a customer asks, "I'd like to order this product. What should I do?", the AI ​​will support the ordering process by providing a response such as, "Add the product name to your cart and proceed with the checkout process. Enter the required information and select your payment method." Furthermore, the site also offers comprehensive after-sales service. If a customer inquires, "My product hasn't arrived. What should I do?", the AI ​​will respond by providing a response such as, "I'm sorry. Could you please let me know your order number? I'll check the delivery status based on that." This allows the multilingual chat commerce system to respond flexibly to customer needs. This significantly enhances customer service at inbound facilities and cross-border e-commerce sites.

[0064] A multilingual chat commerce system according to an embodiment includes a reception unit, a provision unit, a recommendation unit, a support unit, and an after-sales service unit. The reception unit receives customer inquiries. For example, the reception unit can receive inquiries when customers make inquiries through chat. The provision unit provides product information based on the inquiries received by the reception unit. For example, the provision unit can provide information such as product details, stock status, and delivery options in real time. The recommendation unit recommends products to customers based on the product information provided by the provision unit. For example, the recommendation unit can recommend and compare products in response to customer questions. The support unit supports customer orders based on products recommended by the recommendation unit. For example, the support unit can provide necessary information and support the order process when a customer adds a product to a cart and proceeds with the checkout procedure. The after-sales service unit provides customer after-sales service based on orders supported by the support unit. For example, the after-sales service unit can handle order inquiries, check delivery status, and return / exchange procedures. This enables the multilingual chat commerce system according to an embodiment to provide consistent support from customer inquiries to after-sales service.

[0065] The reception unit includes a translation unit that automatically detects the language of the customer and translates into that language. The translation unit can automatically detect the language of the customer using, for example, voice recognition technology. For example, the translation unit analyzes text entered by the customer in chat and identifies that language. The translation unit can also translate into the identified language using machine translation technology. For example, the translation unit translates text entered by the customer in real time and converts it into a corresponding language. This allows the reception unit to automatically detect the language of the customer and translate it, enabling multilingual support.

[0066] The providing unit can provide information on product details, stock status, and delivery options in real time. The providing unit, for example, provides product details in real time. For example, the providing unit can provide detailed information such as product specifications, prices, and reviews. The providing unit can also provide stock status in real time. For example, the providing unit provides information such as the number of products in stock and the expected arrival date. The providing unit can also provide information on delivery options in real time. For example, the providing unit provides information such as delivery method, delivery time, and shipping fee. In this way, the providing unit can provide the information that customers need in real time, enabling them to make quick purchasing decisions.

[0067] The recommendation unit can recommend products in response to customer questions and compare product features. For example, if a customer inquires via chat, "I'm looking for a waterproof camera for outdoor use," the recommendation unit can use the generation AI to suggest a recommended product, such as, "How about our popular waterproof action camera X? It can take high-resolution images and is highly waterproof." Similarly, if a customer asks, "What's the difference between product A and product B?" the recommendation unit can use the generation AI to compare product features, such as, "Product A has excellent waterproof performance, while product B has a long battery life. Consider which one best suits your needs." This allows the recommendation unit to recommend and compare the best products in response to customer questions, improving the customer's purchasing experience.

[0068] The support department can support the customer's ordering process. For example, if a customer asks, "I want to order this product. What should I do?", the support department can use generative AI to support the ordering process by saying, "Please add the product name to your cart and proceed with the checkout. Enter the required information and select your payment method." The support department can also provide the necessary information and support the ordering process as the customer proceeds with the order. For example, the support department can confirm the information the customer enters and provide advice on selecting a payment method. In this way, the support department can support the customer's ordering process, enabling a smooth purchase.

[0069] The after-sales service department can handle order inquiries, check delivery status, and return / exchange procedures. For example, if a customer asks, "My product hasn't arrived yet, what should I do?", the after-sales service department can respond using generative AI by saying, "I'm sorry. Could you please tell me your order number? I'll check the delivery status based on that." The after-sales service department can also provide necessary information when customers inquire about orders and respond to their inquiries. For example, the after-sales service department can provide information for customers to check their order status and track their delivery status. The after-sales service department can also handle return / exchange procedures. For example, if a customer wishes to return or exchange an item, the after-sales service department can guide them through the necessary procedures and conditions. This allows the after-sales service department to enhance customer after-sales service by handling order inquiries, checking delivery status, and return / exchange procedures.

[0070] The reception unit can estimate the customer's emotions and prioritize inquiries based on the estimated customer's emotions. The reception unit can estimate the customer's emotions using, for example, generative AI. For example, the reception unit analyzes text entered by the customer in chat to identify the emotion. The reception unit can also prioritize inquiries based on the estimated customer's emotions. For example, if the customer is dissatisfied, the reception unit processes the inquiry with the highest priority. The reception unit can also respond immediately to inquiries if the customer has an urgent problem. Furthermore, if the customer is relaxed, the reception unit can process the inquiry with normal priority. This allows the reception unit to provide more appropriate responses by prioritizing inquiries based on the customer's emotions.

[0071] The reception department can analyze the customer's past inquiry history and select a response method. The reception department can analyze the customer's past inquiry history, for example, using generative AI. For example, the reception department can analyze the customer's past inquiry content and response history, and select the optimal response method based on that information. Also, if the customer has made a similar inquiry in the past, the reception department can respond quickly based on that history. Furthermore, if the customer has had a specific problem in the past, the reception department can also propose the optimal solution to that problem. In this way, the reception department can select the optimal response method by analyzing the customer's past inquiry history.

[0072] When receiving an inquiry, the reception unit can filter inquiries based on the customer's current purchase history and areas of interest. The reception unit can, for example, use generative AI to analyze the customer's current purchase history and areas of interest. For example, the reception unit prioritizes inquiries related to products recently purchased by the customer. The reception unit can also filter inquiries based on product categories in which the customer is interested. Furthermore, the reception unit can analyze the customer's purchase history and respond quickly to related inquiries. This allows the reception unit to provide more appropriate responses by filtering based on the customer's purchase history and areas of interest.

[0073] The reception unit can estimate the customer's emotions and adjust the speed at which inquiries are handled based on the estimated customer emotions. The reception unit can estimate the customer's emotions using, for example, generative AI. For example, the reception unit analyzes text entered by the customer in chat and identifies the emotion. The reception unit can also adjust the speed at which inquiries are handled based on the estimated customer emotions. For example, the reception unit can respond quickly if the customer is dissatisfied. The reception unit can also handle inquiries at a normal response speed if the customer is relaxed. Furthermore, the reception unit can respond immediately if the customer has an urgent problem. This allows the reception unit to provide more appropriate responses by adjusting the response speed based on the customer's emotions.

[0074] When receiving an inquiry, the reception unit can prioritize obtaining highly relevant information by taking into account the geographical location information of the customer. The reception unit can obtain the geographical location information of the customer by using, for example, generative AI. For example, if the customer is in a specific area, the reception unit can prioritize obtaining information related to that area. Also, if the customer is traveling, the reception unit can provide information related to the customer's travel destination. Furthermore, the reception unit can guide the customer to the most suitable support center based on the customer's current location. In this way, the reception unit can prioritize obtaining highly relevant information by taking into account the geographical location information of the customer.

[0075] When receiving an inquiry, the reception unit can analyze the customer's social media activity and obtain relevant information. The reception unit can, for example, use generative AI to analyze the customer's social media activity. For example, if the customer mentions a specific product on social media, the reception unit can respond based on that information. The reception unit can also identify products of interest from the customer's social media activity and provide related information. Furthermore, the reception unit can analyze the customer's feedback on social media and select the optimal way to respond. This allows the reception unit to obtain relevant information by analyzing the customer's social media activity.

[0076] The provision unit can estimate the customer's emotions and adjust the way in which information is presented to be provided based on the estimated customer's emotions. The provision unit can estimate the customer's emotions using, for example, a generative AI. For example, the provision unit analyzes text entered by the customer in chat and identifies the emotion. The provision unit can also adjust the way in which information is presented to be provided based on the estimated customer's emotions. For example, the provision unit can provide polite and detailed information when the customer is dissatisfied. The provision unit can also provide concise and easy-to-understand information when the customer is relaxed. Furthermore, the provision unit can provide visually appealing information when the customer is excited. This enables the provision unit to provide more appropriate information by adjusting the way in which information is presented based on the customer's emotions.

[0077] When providing product information, the provision unit can adjust the level of detail of the information based on the importance of the product. The provision unit can, for example, use a generative AI to evaluate the importance of the product. For example, the provision unit evaluates the importance of the product based on information such as product sales, inventory status, and customer reviews. The provision unit can also adjust the level of detail of the information based on the importance of the product. For example, the provision unit can provide detailed descriptions and images for important products. The provision unit can also provide concise descriptions for general products. Furthermore, the provision unit can provide detailed specifications and reviews for expensive products. In this way, the provision unit can adjust the level of detail of the information based on the importance of the product, thereby providing optimal information to customers.

[0078] When providing product information, the providing unit can apply different information provision algorithms depending on the product category. The providing unit can, for example, identify the product category using generative AI. For example, the providing unit can identify the category based on the product's attributes and tags. The providing unit can also apply different information provision algorithms depending on the product category. For example, the providing unit can provide information that emphasizes specifications and reviews for electronic devices. For fashion items, the providing unit can also provide information that emphasizes images and styling suggestions. Furthermore, for food, the providing unit can provide information that emphasizes ingredients and nutritional information. In this way, the providing unit can provide the most suitable information to customers by applying an information provision algorithm depending on the product category.

[0079] The provision unit can estimate the customer's emotions and adjust the length of information to be provided based on the estimated customer's emotions. The provision unit can estimate the customer's emotions using, for example, generative AI. For example, the provision unit analyzes text entered by the customer in chat and identifies the emotion. The provision unit can also adjust the length of information to be provided based on the estimated customer's emotions. For example, the provision unit can provide short, to-the-point information when the customer is in a hurry. The provision unit can also provide detailed information when the customer is relaxed. Furthermore, the provision unit can provide visually appealing information when the customer is excited. This allows the provision unit to provide more appropriate information by adjusting the length of information based on the customer's emotions.

[0080] When providing product information, the providing unit can determine the priority of information based on the time of product submission. The providing unit can, for example, use a generation AI to identify the time of product submission. For example, the providing unit can identify the time of submission based on information such as the product release date and sale period. The providing unit can also determine the priority of information based on the time of product submission. For example, the providing unit can provide information preferentially about new products. The providing unit can also provide information preferentially about products on sale. Furthermore, the providing unit can provide information about seasonal products according to the season. In this way, the providing unit can determine the priority of information based on the time of product submission, thereby providing the most appropriate information to customers.

[0081] When providing product information, the providing unit can adjust the order of information based on the relevance of the products. The providing unit can, for example, use a generative AI to evaluate the relevance of the products. For example, the providing unit evaluates the relevance of the products based on the customer's purchase history and search history. The providing unit can also adjust the order of information based on the relevance of the products. For example, the providing unit can preferentially display products in which the customer is interested. The providing unit can also preferentially display products that are highly relevant based on the customer's purchase history. Furthermore, the providing unit can also preferentially display products that are highly relevant based on the customer's search history. In this way, the providing unit can adjust the order of information based on the relevance of the products, thereby providing optimal information to the customer.

[0082] The recommendation unit can estimate the customer's emotions and prioritize recommended products based on the estimated customer emotions. The recommendation unit can estimate the customer's emotions using, for example, generative AI. For example, the recommendation unit analyzes text entered by the customer in chat to identify the emotion. The recommendation unit can also prioritize recommended products based on the estimated customer emotions. For example, if the customer is dissatisfied, the recommendation unit can prioritize the most highly rated products. If the customer is relaxed, the recommendation unit can also recommend products based on the customer's interests. Furthermore, if the customer is excited, the recommendation unit can prioritize the most visually appealing products. This allows the recommendation unit to prioritize recommended products based on the customer's emotions, enabling more appropriate product recommendations.

[0083] When selecting recommended products, the recommendation unit can analyze the customer's past purchase history and select the most suitable product. The recommendation unit can, for example, use generation AI to analyze the customer's past purchase history. For example, the recommendation unit can recommend products related to products that the customer has previously purchased. The recommendation unit can also recommend products in the same category based on the customer's purchase history. Furthermore, the recommendation unit can analyze the customer's purchase history and recommend the most highly rated product. This allows the recommendation unit to select the most suitable product by analyzing the customer's past purchase history.

[0084] When selecting recommended products, the recommendation unit can customize products based on the customer's current living situation. The recommendation unit can, for example, use generative AI to identify the customer's current living situation. For example, the recommendation unit can identify the customer's current living situation based on information such as the customer's family composition, income, and lifestyle. The recommendation unit can also customize products based on the customer's current living situation. For example, if the customer is traveling, the recommendation unit can recommend travel-related products. If the customer is starting a new life, the recommendation unit can also recommend products necessary for that new life. Furthermore, if the customer is attending a specific event, the recommendation unit can recommend products related to the event. This allows the recommendation unit to customize products based on the customer's current living situation, enabling more appropriate product recommendations.

[0085] The recommendation unit can estimate the customer's emotions and adjust the display method of recommended products based on the estimated customer emotions. The recommendation unit can estimate the customer's emotions using, for example, generative AI. For example, the recommendation unit can analyze text entered by the customer in chat and identify the emotion. The recommendation unit can also adjust the display method of recommended products based on the estimated customer emotions. For example, if the customer is dissatisfied, the recommendation unit can provide a visually appealing display method. If the customer is relaxed, the recommendation unit can also provide a display method including detailed information. Furthermore, if the customer is excited, the recommendation unit can also provide a visually stimulating display method. This allows the recommendation unit to adjust the display method of recommended products based on the customer's emotions, thereby enabling more appropriate product recommendations.

[0086] When selecting recommended products, the recommendation unit can select the most suitable product by taking into account the customer's geographical location information. The recommendation unit can obtain the customer's geographical location information by using, for example, generation AI. For example, if the customer is in a specific area, the recommendation unit can recommend products that are popular in that area. Also, if the customer is traveling, the recommendation unit can recommend products that can be used at the customer's travel destination. Furthermore, the recommendation unit can select the most suitable product based on the customer's current location. This allows the recommendation unit to select the most suitable product by taking into account the customer's geographical location information.

[0087] When selecting recommended products, the recommendation unit can analyze the customer's social media activity and suggest products. The recommendation unit can, for example, use generative AI to analyze the customer's social media activity. For example, if the customer mentions a specific product on social media, the recommendation unit recommends that product. The recommendation unit can also identify products that the customer is interested in from the customer's social media activity and recommend them. Furthermore, the recommendation unit can analyze the customer's feedback on social media and suggest optimal products. This allows the recommendation unit to suggest optimal products by analyzing the customer's social media activity.

[0088] The support department can estimate the customer's emotions and adjust the support method based on the estimated customer's emotions. The support department can estimate the customer's emotions using, for example, generative AI. For example, the support department analyzes text entered by the customer in chat and identifies the emotion. The support department can also adjust the support method based on the estimated customer's emotions. For example, the support department can provide courteous and prompt support if the customer is dissatisfied. On the other hand, the support department can provide a standard support method if the customer is relaxed. Furthermore, the support department can respond immediately if the customer has an urgent problem. This allows the support department to provide more appropriate support by adjusting the support method based on the customer's emotions.

[0089] When providing support, the support department can analyze the customer's past consumption behavior and select the optimal support method. The support department can, for example, use generative AI to analyze the customer's past consumption behavior. For example, if the customer has had a similar problem in the past, the support department can respond based on that history. The support department can also select the optimal support method based on the customer's consumption behavior. Furthermore, the support department can also prioritize support methods that the customer has given high ratings to in the past. This allows the support department to select the optimal support method by analyzing the customer's past consumption behavior.

[0090] When providing support, the support department can customize the means of support based on the customer's current living situation. The support department can, for example, use generative AI to identify the customer's current living situation. For example, the support department can identify the customer's current living situation based on information such as the customer's family composition, income, and lifestyle. The support department can also customize the means of support based on the customer's current living situation. For example, if the customer is traveling, the support department can provide support at the customer's destination. Also, if the customer is starting a new life, the support department can provide support necessary for that new life. Furthermore, if the customer is participating in a specific event, the support department can provide support related to that event. This allows the support department to customize the means of support based on the customer's current living situation, enabling more appropriate support.

[0091] The support department can estimate the customer's emotions and determine support priorities based on the estimated customer emotions. The support department can estimate the customer's emotions using, for example, generative AI. For example, the support department analyzes text entered by the customer in chat to identify the emotion. The support department can also determine support priorities based on the estimated customer emotions. For example, if a customer is dissatisfied, the support department can handle the inquiry as the highest priority. Also, if a customer has an urgent problem, the support department can respond to the inquiry immediately. Furthermore, if the customer is relaxed, the support department can handle the inquiry with normal priority. This allows the support department to provide more appropriate support by determining support priorities based on the customer's emotions.

[0092] When providing support, the support department can select the optimal support method by taking into account the customer's geographic location information. The support department can obtain the customer's geographic location information using, for example, generative AI. For example, if the customer is in a specific area, the support department can provide support related to that area. Also, if the customer is traveling, the support department can provide support at the customer's destination. Furthermore, the support department can guide the customer to the optimal support center based on the customer's current location. This allows the support department to select the optimal support method by taking into account the customer's geographic location information.

[0093] When providing support, the support department can analyze the customer's social media activity and suggest support methods. The support department can, for example, use generative AI to analyze the customer's social media activity. For example, if the customer mentions a specific product on social media, the support department can respond based on that information. The support department can also identify products that the customer is interested in from the customer's social media activity and provide related support. Furthermore, the support department can analyze the customer's social media feedback and select the optimal support method. This allows the support department to suggest the optimal support method by analyzing the customer's social media activity.

[0094] The after-sales service department can estimate the customer's emotions and adjust the after-sales service method based on the estimated customer emotions. The after-sales service department can estimate the customer's emotions using, for example, generative AI. For example, the after-sales service department can analyze text entered by the customer in a chat and identify the customer's emotions. The after-sales service department can also adjust the after-sales service method based on the estimated customer emotions. For example, the after-sales service department can provide courteous and prompt after-sales service if the customer is dissatisfied. The after-sales service department can also provide a standard after-sales service method if the customer is relaxed. Furthermore, the after-sales service department can respond immediately if the customer has an urgent problem. This allows the after-sales service department to adjust the after-sales service method based on the customer's emotions, enabling more appropriate after-sales service.

[0095] During after-sales service, the after-sales service department can analyze the customer's past consumption behavior and select the optimal after-sales service method. The after-sales service department can, for example, use generative AI to analyze the customer's past consumption behavior. For example, if the customer has had a similar problem in the past, the after-sales service department can respond based on that history. The after-sales service department can also select the optimal after-sales service method based on the customer's consumption behavior. Furthermore, the after-sales service department can prioritize after-sales service methods that the customer has given high ratings to in the past. This allows the after-sales service department to select the optimal after-sales service method by analyzing the customer's past consumption behavior.

[0096] The after-sales service department can customize after-sales service methods based on the customer's current living situation during after-sales service. The after-sales service department can, for example, use generative AI to identify the customer's current living situation. For example, the after-sales service department can identify the customer's current living situation based on information such as the customer's family composition, income, and lifestyle. The after-sales service department can also customize after-sales service methods based on the customer's current living situation. For example, if the customer is traveling, the after-sales service department can provide after-sales service at the customer's destination. Furthermore, if the customer is starting a new life, the after-sales service department can also provide after-sales service necessary for that new life. Furthermore, if the customer is attending a specific event, the after-sales service department can also provide after-sales service related to that event. This allows the after-sales service department to customize after-sales service methods based on the customer's current living situation, enabling more appropriate after-sales service.

[0097] The after-sales service department can estimate the customer's emotions and determine the priority of after-sales service based on the estimated customer's emotions. The after-sales service department can estimate the customer's emotions using, for example, generative AI. For example, the after-sales service department analyzes text entered by the customer in a chat to identify the emotion. The after-sales service department can also determine the priority of after-sales service based on the estimated customer's emotions. For example, if the customer is dissatisfied, the after-sales service department can handle the inquiry with the highest priority. Furthermore, if the customer has an urgent problem, the after-sales service department can also handle the inquiry with normal priority if the customer is relaxed. This allows the after-sales service department to provide more appropriate after-sales service by determining the priority of after-sales service based on the customer's emotions.

[0098] During after-sales service, the after-sales service department can select the optimal after-sales service method by taking into account the customer's geographical location information. The after-sales service department can obtain the customer's geographical location information using, for example, generative AI. For example, if the customer is in a specific area, the after-sales service department can provide after-sales service related to that area. Also, if the customer is traveling, the after-sales service department can provide after-sales service at the customer's destination. Furthermore, the after-sales service department can guide the customer to the optimal after-sales service center based on the customer's current location. This allows the after-sales service department to select the optimal after-sales service method by taking into account the customer's geographical location information.

[0099] During after-sales service, the after-sales service department can analyze the customer's social media activity and propose after-sales service methods. The after-sales service department can, for example, use generative AI to analyze the customer's social media activity. For example, if the customer mentions a specific product on social media, the after-sales service department can respond based on that information. The after-sales service department can also identify products of interest from the customer's social media activity and provide related after-sales service. Furthermore, the after-sales service department can analyze the customer's social media feedback and select the optimal after-sales service method. In this way, the after-sales service department can propose the optimal after-sales service method by analyzing the customer's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, provision unit, recommendation unit, support unit, after-sales service unit, and translation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives customer inquiries. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides product information. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends products to customers. The support unit is realized by the control unit 46A of the smart device 14 and supports customer orders. The after-sales service unit is realized by the specific processing unit 290 of the data processing device 12 and provides after-sales service to customers. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically detects and translates the customer's language. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, provision unit, recommendation unit, support unit, after-sales service unit, and translation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives customer inquiries. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides product information. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends products to customers. The support unit is realized, for example, by the control unit 46A of the smart glasses 214 and supports customer orders. The after-sales service unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides after-sales service to customers. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically detects and translates the customer's language. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, provision unit, recommendation unit, support unit, after-sales service unit, and translation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives customer inquiries. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides product information. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends products to customers. The support unit is realized by the control unit 46A of the headset type terminal 314 and supports customer orders. The after-sales service unit is realized by the specific processing unit 290 of the data processing device 12 and provides after-sales service to customers. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically detects and translates the customer's language. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, provision unit, recommendation unit, support unit, after-sales service unit, and translation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives customer inquiries. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides product information. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends products to customers. The support unit is realized, for example, by the control unit 46A of the robot 414 and supports customer orders. The after-sales service unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides after-sales service to customers. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically detects and translates the customer's language.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The reception department can automatically search for and provide relevant FAQs based on the content of a customer's inquiry. For example, if a customer inquires, "How do I return an item?", the reception department can search for relevant FAQs and respond in the form of, "Please refer to this link for information on how to return an item." In addition, if a customer asks, "How long is the product warranty period?", the reception department can provide information from the FAQ such as, "The product warranty period is one year." Furthermore, if a customer inquires, "What are the payment methods?", the reception department can provide information from the FAQ such as, "Credit cards, debit cards, and PayPal are available." This allows the reception department to respond to customer inquiries quickly.

[0102] The provision unit can provide coupons and discount information for related products based on the customer's purchase history. For example, if a new product related to a product that the customer previously purchased is released, the provision unit can provide coupon information in the form of "We will send you a discount coupon for the new product." In addition, if a customer frequently purchases products in a specific category, the provision unit can also provide discount information for products in that category. Furthermore, if a customer has purchased expensive products in the past, the provision unit can provide special discount information to that customer. This allows the provision unit to provide more personalized information based on the customer's purchase history.

[0103] The recommendation unit can analyze a customer's social media activity and recommend products that they are interested in. For example, if a customer mentions a specific product on social media, the recommendation unit can recommend related products by saying, "It seems you're interested in this product. We also recommend this product." The recommendation unit can also recommend related products based on information about brands and influencers that the customer follows on social media. Furthermore, the recommendation unit can analyze the content of posts that customers share on social media and recommend products based on that content. This allows the recommendation unit to utilize a customer's social media activity to recommend more appropriate products.

[0104] The support department can provide the most appropriate support method based on the customer's current living situation. For example, if a customer is starting a new life, the support department can provide support in the form of, "Here are the products you need for your new life." If the customer is traveling, the support department can provide travel-related support in the form of, "Here is support for your travel destination." Furthermore, if the customer is participating in a specific event, the support department can provide event-related support in the form of, "Here are products related to the event." This allows the support department to provide more appropriate support based on the customer's current living situation.

[0105] The after-sales service department can analyze a customer's past consumption behavior and select the most appropriate after-sales service method. For example, if a customer has had a similar problem in the past, they can respond based on that history. They can also prioritize after-sales service methods that the customer has given high ratings to in the past. Furthermore, if a customer has frequently reported a particular problem in the past, they can propose the most appropriate solution to that problem. This allows the after-sales service department to provide more appropriate after-sales service by analyzing a customer's past consumption behavior.

[0106] The reception unit can estimate the customer's emotions and determine the priority of inquiries based on the estimated customer emotions. For example, if a customer is dissatisfied, the inquiry can be handled with the highest priority. Also, if a customer has an urgent problem, the inquiry can be dealt with immediately. Furthermore, if a customer is relaxed, the inquiry can be dealt with with normal priority. This allows the reception unit to prioritize inquiries based on the customer's emotions, enabling more appropriate responses.

[0107] The providing unit can estimate the customer's emotions and adjust the way information is presented to be provided based on the estimated customer's emotions. For example, if the customer is dissatisfied, polite and detailed information can be provided. If the customer is relaxed, concise and easy-to-understand information can be provided. Furthermore, if the customer is excited, visually appealing information can be provided. In this way, the providing unit can provide more appropriate information by adjusting the way information is presented based on the customer's emotions.

[0108] The recommendation unit can estimate the customer's emotions and prioritize recommended products based on the estimated customer emotions. For example, if the customer is dissatisfied, it can prioritize the most highly rated products to recommend. Also, if the customer is relaxed, it can recommend products based on the customer's interests. Furthermore, if the customer is excited, it can prioritize the most visually appealing products to recommend. This allows the recommendation unit to prioritize recommended products based on the customer's emotions, thereby enabling more appropriate product recommendations.

[0109] The support department can estimate the customer's emotions and adjust the support method based on the estimated customer's emotions. For example, if the customer is dissatisfied, polite and prompt support can be provided. If the customer is relaxed, a normal support method can be provided. Furthermore, if the customer has an urgent problem, immediate action can be taken. This allows the support department to provide more appropriate support by adjusting the support method based on the customer's emotions.

[0110] The after-sales service department can estimate the customer's emotions and adjust the after-sales service method based on the estimated customer's emotions. For example, if the customer is dissatisfied, courteous and prompt after-sales service can be provided. If the customer is relaxed, a normal after-sales service method can be provided. Furthermore, if the customer has an urgent problem, an immediate response can be made. This allows the after-sales service department to adjust the after-sales service method based on the customer's emotions, enabling more appropriate after-sales service.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit receives an inquiry from a customer. For example, when a customer makes an inquiry through chat, the reception unit can receive the inquiry. Step 2: The providing unit provides product information based on the inquiry received by the receiving unit. For example, the providing unit can provide information such as product details, stock status, and delivery options in real time. Step 3: The recommendation unit recommends products to the customer based on the product information provided by the provision unit. For example, the recommendation unit can recommend and compare products in response to a customer's question. Step 4: The support department assists the customer in placing an order based on the products recommended by the recommendation department. For example, the support department can provide necessary information and support the order process when the customer adds products to the cart and proceeds with the checkout procedure. Step 5: The after-sales service department provides customer after-sales service based on the order supported by the support department. For example, the after-sales service department can handle order inquiries, delivery status checks, and return / exchange procedures.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 reception section for receiving customer inquiries; a providing unit that provides product information based on the inquiry received by the receiving unit; a recommendation unit that recommends products to customers based on the product information provided by the provision unit; a support unit that supports customers in placing orders based on the products recommended by the recommendation unit; an after-sales service department that provides after-sales service to customers based on the orders supported by the support department. A system characterized by:

2. The reception unit Equipped with a translation section that automatically detects the customer's language and translates into that language 2. The system of claim 1.

3. The providing unit Providing real-time product details, availability, and delivery options 2. The system of claim 1.

4. The recommendation unit Recommend products based on customer questions and compare product features 2. The system of claim 1.

5. The support portion is Help customers check out 2. The system of claim 1.

6. The after-sales service department Inquire about your order, check delivery status, and process returns and exchanges 2. The system of claim 1.

7. The reception unit Estimate customer sentiment and prioritize inquiries based on the estimated customer sentiment 2. The system of claim 1.

8. The reception unit Analyze the customer's past inquiry history and decide how to respond 2. The system of claim 1.

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