system

Abstract

The system according to the embodiment aims to provide multilingual support and support at the time of purchase. [Solution] A system according to an embodiment includes a recognition unit, a dialogue unit, a provision unit, and a procedure unit. The recognition unit recognizes a user's language. The dialogue unit conducts a dialogue based on the language recognized by the recognition unit. The provision unit provides information about products selected by the user. The procedure unit supports the purchase procedure based on the information provided by the provision 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 has had the problem of insufficient multilingual support and purchasing support in vending machines.

[0005] The system according to the embodiment aims to provide multilingual support and support at the time of purchase. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, a dialogue unit, a provision unit, and a procedure unit. The recognition unit recognizes the language of a user. The dialogue unit conducts dialogue based on the language recognized by the recognition unit. The provision unit provides information on products selected by the user. The procedure unit supports the purchase procedure based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide multilingual support and support at the time of purchase. [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) An interactive vending machine system according to an embodiment of the present invention is a multilingual interactive vending machine incorporating a generation AI. When a user approaches the vending machine, the generation AI recognizes the user's language and initiates a dialogue in the appropriate language. Next, the user selects a product, and the generation AI provides information about the product and assists with the purchase process. This allows all users, including foreign tourists, to smoothly purchase products. For example, when a user approaches the vending machine, the generation AI identifies the user's language using voice recognition technology. Multiple languages, including English, French, and Chinese, are supported. This allows foreign tourists to interact in their own language. Next, when the user selects a product, the generation AI provides information about the product. For example, the AI ​​can provide the user with information about the drink's ingredients, allergy information, and price. This allows the user to make an informed purchase decision. Furthermore, the generation AI assists with the purchase process. After the user selects a product, the generation AI guides the user through payment options and proceeds with the purchase. Multiple payment methods, including cash, credit card, and electronic money, are supported. This allows the user to select the payment method that best suits them and complete the purchase smoothly. This system allows all users, including foreign tourists, to purchase products smoothly. For example, when an English-speaking tourist approaches a vending machine, the generating AI will begin a dialogue in English, supporting them from product selection to the purchase process. This allows them to purchase products with peace of mind without feeling any language barrier. The generating AI also records the user's purchase history, which can be used as a reference for the next purchase. For example, if a customer wants to repurchase a product they previously purchased, the generating AI will provide that information, allowing them to smoothly proceed with the purchase process. This improves user convenience and is expected to lead to an increase in repeat customers. In this way, a multilingual interactive vending machine equipped with generating AI can provide a smooth purchasing experience and improve convenience for all users, including foreign tourists.This allows the interactive vending machine system to enable all users, including foreign tourists, to purchase products smoothly.

[0029] An interactive vending machine system according to an embodiment includes a recognition unit, a dialogue unit, a provision unit, and a procedure unit. The recognition unit recognizes a user's language. The recognition unit identifies the user's language using, for example, voice recognition technology. For example, the recognition unit can support multiple languages, such as English, French, and Chinese. The recognition unit can also identify the user's language using a generation AI. For example, the generation AI identifies the user's language using voice recognition technology and conducts a dialogue based on that language. The dialogue unit conducts a dialogue based on the language recognized by the recognition unit. For example, the dialogue unit initiates a dialogue in the recognized language when the user approaches the vending machine. The dialogue unit can also conduct a dialogue with the user using the generation AI. For example, the generation AI conducts a dialogue based on the user's language and supports the user in selecting a product they wish to purchase. The provision unit provides information about the product selected by the user. For example, the provision unit informs the user of the drink's ingredients, allergy information, price, etc. The provision unit can also provide product information using the generation AI. For example, the generation AI provides information about a product selected by a user, allowing the user to make a purchase decision after gaining sufficient information about the product. The procedure unit supports the purchase procedure based on the information provided by the provision unit. The procedure unit supports multiple payment methods, such as cash, credit card, and electronic money. The procedure unit can also support the purchase procedure using the generation AI. For example, the generation AI guides the user through payment methods for the product selected by the user and proceeds with the purchase procedure. As a result, the interactive vending machine system according to the embodiment can enable all users, including foreign tourists, to purchase products smoothly.

[0030] The recognition unit can identify the user's language using speech recognition technology. Examples of speech recognition technology include deep learning-based speech recognition technology and HMM (hidden Markov model). The recognition unit can identify the user's language using deep learning-based speech recognition technology. Deep learning-based speech recognition technology learns large amounts of speech data to achieve highly accurate speech recognition. For example, the recognition unit receives the user's speech as input and identifies the language using a deep learning model. The recognition unit can also identify the user's language using an HMM. The HMM models temporal changes in speech and achieves highly accurate speech recognition. For example, the recognition unit receives the user's speech as input and identifies the language using the HMM. This allows the speech recognition technology to accurately identify the user's language. Some or all of the above-described processing in the recognition unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the recognition unit can input the user's speech data to a generation AI, which can identify the language from the speech data.

[0031] The provision unit can communicate the drink's ingredients, allergy information, and price to the user. The provision unit, for example, provides the drink's ingredient information to the user. The ingredient information includes, for example, major ingredients and additives. The provision unit can also use the generation AI to provide the ingredient information to the user. For example, the generation AI analyzes the drink's ingredient information and provides it to the user. The provision unit can also provide allergy information to the user. The allergy information includes, for example, specific allergens and the degree of allergic reaction. The provision unit can also use the generation AI to provide the allergy information to the user. For example, the generation AI analyzes the drink's allergy information and provides it to the user. The provision unit can also provide price information to the user. The price information includes, for example, a price including tax and a discount price. The provision unit can also use the generation AI to provide the price information to the user. For example, the generation AI analyzes the drink's price information and provides it to the user. This allows the user to make a purchase decision after gaining sufficient information about the product. Some or all of the above-mentioned processing in the provision unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the provision unit can input information about the drink's ingredients, allergy information, and price information into the generation AI, which can then analyze the information and provide it to the user.

[0032] The processing unit can accommodate multiple payment methods, including cash, credit cards, and electronic money. The processing unit, for example, accommodates cash payments. Cash payments include, for example, bills and coins. The processing unit can also use a generation AI to support cash payment procedures. For example, the generation AI guides the user through the procedure for inserting cash. The processing unit can also accommodate credit card payments. Credit card payments include, for example, VISA, MasterCard, and American Express. The processing unit can also use a generation AI to support credit card payment procedures. For example, the generation AI guides the user through the procedure for inserting a credit card. The processing unit can also accommodate electronic money payments. Electronic money payments include, for example, transportation IC cards, two-dimensional code (e.g., QR Code (registered trademark)) payments, and mobile payments. The processing unit can also use a generation AI to support electronic money payment procedures. For example, the generation AI guides the user through the procedure for using electronic money. This allows the user to select a payment method that suits them and complete a purchase smoothly. Some or all of the above-described processing in the procedure section may be performed using the generation AI, or may be performed without using the generation AI. For example, the procedure section may input the user's payment method selection into the generation AI, which may then guide the user through the payment procedure.

[0033] The providing unit records the user's purchase history and can refer to it the next time they make a purchase. The providing unit, for example, records the user's purchase history. The purchase history includes, for example, the purchase date and time, the purchased items, and the purchase amount. The providing unit can also use the generation AI to record the purchase history. For example, the generation AI stores the user's purchase history in a database and uses it as a reference the next time they make a purchase. The providing unit can also provide information that will be useful the next time they make a purchase based on the user's purchase history. For example, if the providing unit wants to repurchase a product that they previously purchased, the generation AI can provide that information to facilitate a smooth purchase process. This improves user convenience and is expected to increase repeat customers. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's purchase history into the generation AI, which can analyze the information and provide information that will be useful the next time they make a purchase.

[0034] The dialogue unit can initiate a dialogue in the recognized language when a user approaches the vending machine. For example, the dialogue unit detects the user's approach using a distance sensor when the user approaches the vending machine. The distance sensor includes, for example, an infrared sensor or an ultrasonic sensor. The dialogue unit can also detect the user's approach using a generation AI. For example, the generation AI analyzes data from the distance sensor to detect the user's approach. The dialogue unit can also recognize the user's face using a camera to detect the user's approach. For example, the camera detects the user's face using facial recognition technology to detect the user's approach. The dialogue unit can also analyze camera data using the generation AI to detect the user's approach. This allows a dialogue to be smoothly initiated when the user approaches the vending machine. Some or all of the above-described processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input data from a distance sensor or a camera into the generation AI, which can then detect the user's approach and initiate a dialogue.

[0035] The recognition unit can analyze the user's past dialogue history and select the optimal language recognition algorithm. For example, the recognition unit retrieves the user's past dialogue history from a database and analyzes it using a generation AI. For example, the recognition unit selects the optimal language recognition algorithm using the generation AI based on the languages ​​the user has used in the past. The recognition unit can also improve recognition accuracy by learning the accent and pronunciation of a specific language from the user's past dialogue history. For example, the recognition unit uses a generation AI to analyze the user's past dialogue history and learn the accent and pronunciation of a specific language. Furthermore, the recognition unit can adjust the recognition accuracy of a language that the user has previously misrecognized based on that language. For example, the recognition unit uses a generation AI to analyze the user's past dialogue history and adjust the recognition accuracy of the misrecognized language. This improves recognition accuracy by selecting the optimal language recognition algorithm based on the user's past dialogue history. Some or all of the above-mentioned processing in the recognition unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recognition unit can input the user's past dialogue history into the generation AI, which can then select the optimal language recognition algorithm.

[0036] The recognition unit can improve recognition accuracy by taking into account the user's pronunciation and accent during language recognition. For example, the recognition unit learns the user's pronunciation characteristics and performs language recognition based on those characteristics using a generation AI. For example, the recognition unit learns the user's pronunciation characteristics using a generation AI and performs language recognition based on those characteristics. The recognition unit can also take the user's accent into consideration and perform language recognition adapted to that accent using a generation AI. For example, the recognition unit learns the user's accent using a generation AI and performs language recognition adapted to that accent. Furthermore, the recognition unit can learn the user's pronunciation habits and improve recognition accuracy based on those habits using a generation AI. For example, the recognition unit learns the user's pronunciation habits using a generation AI and improves recognition accuracy based on those habits. This improves recognition accuracy by taking the user's pronunciation and accent into consideration. Some or all of the above-described processing in the recognition unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the recognition unit can input data on the user's pronunciation and accent into the generation AI, and the generation AI can improve recognition accuracy based on that data.

[0037] During language recognition, the recognition unit can prioritize recognizing highly relevant languages ​​by taking into account the user's geographical location information. For example, the recognition unit obtains the user's geographical location information from GPS data and analyzes it using a generation AI. For example, when the user is in a tourist destination, the recognition unit causes the generation AI to prioritize recognizing languages ​​commonly used in that area. Furthermore, when the user is in an airport, the recognition unit can also prioritize recognizing internationally accepted languages. For example, when the user is in an airport, the recognition unit uses the generation AI to prioritize recognizing English or other internationally accepted languages. Furthermore, when the user is in a specific country, the recognition unit can also prioritize recognizing the official language of that country. For example, when the user is in a specific country, the recognition unit uses the generation AI to prioritize recognizing the official language of that country. This allows highly relevant languages ​​to be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the recognition unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recognition unit can input the user's geographical location information into the generation AI, which can then prioritize recognizing highly relevant language based on that information.

[0038] During language recognition, the recognition unit can analyze the user's social media activities and recognize associated languages. The recognition unit, for example, analyzes the user's social media posts using a generation AI. For example, the recognition unit uses a generation AI to analyze the user's social media posts and recognize the language. The recognition unit can also analyze the user's social media friend list using a generation AI and recognize the language used by the friends. For example, the recognition unit uses a generation AI to analyze the user's social media friend list and recognize the language used by the friends. The recognition unit can also analyze the time periods during which the user is active on social media using a generation AI and recognize the language used during those time periods. For example, the recognition unit uses a generation AI to analyze the time periods during which the user is active on social media and recognize the language used during those time periods. In this way, associated languages ​​can be recognized by analyzing the user's social media activities. Some or all of the above-described processing in the recognition unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the recognition unit can input the user's social media data into a generation AI, which can recognize associated languages ​​based on that data.

[0039] During a dialogue, the dialogue unit can provide optimal dialogue content by referring to the user's past dialogue history. For example, the dialogue unit retrieves the user's past dialogue history from a database and analyzes it using a generation AI. For example, the dialogue unit may provide dialogue content related to a product that the user has previously purchased using the generation AI. The dialogue unit may also provide dialogue content tailored to the user's preferences based on the user's past dialogue history. For example, the dialogue unit may use the generation AI to analyze the user's past dialogue history and provide dialogue content tailored to the user's preferences. Furthermore, the dialogue unit may provide dialogue content related to a question that the user has previously asked using the generation AI. For example, the dialogue unit may use the generation AI to analyze the user's past dialogue history and provide dialogue content related to the question. In this way, optimal dialogue content can be provided by referring to the user's past dialogue history. Some or all of the above-described processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit may input the user's past dialogue history into the generation AI, and the generation AI may provide optimal dialogue content based on that information.

[0040] The dialogue unit can customize dialogue content during dialogue, taking into account the user's cultural background and habits. For example, the dialogue unit allows the generation AI to provide dialogue content appropriate for the culture based on the user's cultural background. For example, the dialogue unit uses the generation AI to analyze the user's cultural background and provide dialogue content appropriate for the culture. The dialogue unit can also take the user's habits into account and allow the generation AI to provide dialogue content tailored to the habits. For example, the dialogue unit uses the generation AI to analyze the user's habits and provide dialogue content tailored to the habits. Furthermore, the dialogue unit can also provide dialogue content appropriate for the time of year based on holidays and events in the user's country. For example, the dialogue unit uses the generation AI to analyze holidays and events in the user's country and provide dialogue content appropriate for the time of year. This allows more appropriate dialogue content to be provided by taking into account the user's cultural background and habits. Some or all of the above-described processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input data on the user's cultural background and habits into the generation AI, and the generation AI can customize the dialogue content based on that data.

[0041] During a dialogue, the dialogue unit can provide optimal dialogue content by taking into account the user's current situation and environment. For example, when the user is outdoors, the dialogue unit uses the generation AI to provide dialogue content appropriate for the environment. For example, when the user is outdoors, the dialogue unit uses the generation AI to provide dialogue content appropriate for the environment. Furthermore, when the user is in a quiet place, the dialogue unit can also have the generation AI provide a dialogue including detailed explanations. For example, when the user is in a quiet place, the dialogue unit uses the generation AI to provide a dialogue including detailed explanations. Furthermore, when the user is in a noisy place, the dialogue unit can also have the generation AI provide a concise and to-the-point dialogue. For example, when the user is in a noisy place, the dialogue unit uses the generation AI to provide a concise and to-the-point dialogue. This allows optimal dialogue content to be provided by taking into account the user's current situation and environment. Some or all of the above-described processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input data about the user's current situation and environment into the generation AI, and the generation AI can provide optimal dialogue content based on that data.

[0042] The dialogue unit can analyze the user's social media activity during dialogue and provide related dialogue content. The dialogue unit, for example, analyzes the user's social media posts using a generation AI. For example, the dialogue unit can use the generation AI to analyze the user's social media posts and engage in dialogue related to the posts. The dialogue unit can also analyze the user's social media friend list using a generation AI and engage in dialogue related to the friends. For example, the dialogue unit can use the generation AI to analyze the user's social media friend list and engage in dialogue related to the friends. The dialogue unit can also analyze the user's social media activity time period using a generation AI and engage in dialogue related to the time period. For example, the dialogue unit can use the generation AI to analyze the user's social media activity time period and engage in dialogue related to the time period. In this way, related dialogue content can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the dialogue unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the dialogue unit can input the user's social media data into a generation AI, and the generation AI can provide related dialogue content based on the data.

[0043] When providing product information, the providing unit can provide optimal information by referring to the user's past purchase history. For example, the providing unit retrieves the user's past purchase history from a database and analyzes it using a generation AI. For example, the providing unit causes the generation AI to provide information related to products based on products the user has previously purchased. The providing unit can also cause the generation AI to provide information tailored to the user's preferences based on the user's past purchase history. For example, the providing unit uses the generation AI to analyze the user's past purchase history and provide information tailored to the user's preferences. Furthermore, the providing unit can also cause the generation AI to provide information related to questions the user has previously asked based on the questions. For example, the providing unit uses the generation AI to analyze the user's past purchase history and provide information related to the questions asked in the past. In this way, optimal product information can be provided by referring to the user's past purchase history. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past purchase history into the generation AI, and the generation AI can provide optimal product information based on that information.

[0044] When providing product information, the providing unit can customize the information by taking into account the user's health condition and allergy information. For example, the providing unit retrieves the user's health condition and allergy information from a database and analyzes it using a generation AI. For example, if the user has an allergy, the providing unit causes the generation AI to provide information related to the allergy. The providing unit can also take the user's health condition into consideration and cause the generation AI to provide product information appropriate for that condition. For example, the providing unit uses the generation AI to analyze the user's health condition and provide product information appropriate for that condition. Furthermore, if the user has a specific health goal, the providing unit can cause the generation AI to provide product information tailored to that goal. For example, the providing unit uses the generation AI to analyze the user's health goal and provide product information tailored to that goal. This allows more appropriate product information to be provided by taking the user's health condition and allergy information into consideration. Some or all of the above-described processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the user's health condition and allergy information into the generation AI, and the generation AI can customize product information based on that information.

[0045] When providing product information, the providing unit can prioritize providing highly relevant information by taking into account the user's geographical location information. The providing unit, for example, acquires the user's geographical location information from GPS data and analyzes it using a generation AI. For example, when the user is in a tourist destination, the providing unit can have the generation AI provide product information that is commonly purchased in that area. Furthermore, when the user is at an airport, the providing unit can have the generation AI provide internationally accepted product information. For example, when the user is at an airport, the providing unit can use the generation AI to provide internationally accepted product information. Furthermore, when the user is in a specific country, the providing unit can have the generation AI provide product information that is popular in that country. For example, when the user is in a specific country, the providing unit can use the generation AI to provide product information that is popular in that country. In this way, highly relevant product information can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's geographical location information into the generation AI, and the generation AI can provide highly relevant product information based on that information.

[0046] When providing product information, the providing unit can analyze the user's social media activity and provide related information. The providing unit, for example, analyzes the user's social media posts using a generation AI. For example, the providing unit can use the generation AI to analyze the user's social media posts and provide product information related to the content. The providing unit can also analyze the user's social media friend list using a generation AI and provide product information purchased by the friends. For example, the providing unit can use the generation AI to analyze the user's social media friend list and provide product information purchased by the friends. The providing unit can also analyze the user's social media activity time period using a generation AI and provide product information related to that time period. For example, the providing unit can use the generation AI to analyze the user's social media activity time period and provide product information related to that time period. In this way, related product information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's social media data into a generation AI, and the generation AI can provide related product information based on that data.

[0047] During the purchase process, the procedure unit can guide the user through the optimal procedure by referring to the user's past purchase history. For example, the procedure unit retrieves the user's past purchase history from a database and analyzes it using a generation AI. For example, the procedure unit may cause the generation AI to prioritize a payment method based on a payment method the user has used in the past. The procedure unit may also cause the generation AI to guide the user through procedures tailored to the user's preferences based on the user's past purchase history. For example, the procedure unit may use the generation AI to analyze the user's past purchase history and guide the user through procedures tailored to the user's preferences. Furthermore, the procedure unit may also cause the generation AI to guide the user through procedures related to products the user has previously purchased based on the products the user has previously purchased. For example, the procedure unit may use the generation AI to analyze the user's past purchase history and guide the user through procedures related to products the user has previously purchased. In this way, the optimal procedure can be guided by referring to the user's past purchase history. Some or all of the above-described processing in the procedure unit may be performed using or without the generation AI. For example, the procedure unit may input the user's past purchase history into the generation AI, and the generation AI may guide the user through the optimal procedure based on that information.

[0048] The procedure unit can customize the purchase procedure by taking into account the user's payment method preferences. For example, the procedure unit retrieves the user's payment method preferences from a database and analyzes them using a generation AI. For example, if the user prefers cash, the procedure unit can prioritize the cash payment procedure. Also, if the user prefers credit cards, the procedure unit can prioritize the credit card payment procedure. For example, the procedure unit can analyze the user's payment method preferences using the generation AI and prioritize the credit card payment procedure. Furthermore, if the user prefers electronic money, the procedure unit can prioritize the electronic money payment procedure. For example, the procedure unit can analyze the user's payment method preferences using the generation AI and prioritize the electronic money payment procedure. This allows for more appropriate procedures to be guided by taking the user's payment method preferences into consideration. Some or all of the above-described processing in the procedure unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the procedure section can input the user's payment preferences into the generation AI, which can then customize the procedure based on that information.

[0049] During the purchase process, the procedure unit can guide the user to the optimal procedure by taking into account the user's geographical location information. For example, the procedure unit obtains the user's geographical location information from GPS data and analyzes it using a generation AI. For example, if the user is in a tourist destination, the procedure unit can have the generation AI prioritize local payment methods. Furthermore, if the user is at an airport, the procedure unit can have the generation AI prioritize internationally accepted payment methods. For example, if the user is at an airport, the procedure unit can have the generation AI prioritize internationally accepted payment methods. Furthermore, if the user is in a specific country, the generation AI can prioritize local payment methods. For example, if the user is in a specific country, the generation AI can prioritize local payment methods. This allows the optimal procedure to be guided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the procedure unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the procedure unit can input the user's geographical location information into the generation AI, which can then guide the user to the optimal procedure based on that information.

[0050] The procedure unit can analyze the user's social media activity during the purchase process and guide the user through related procedures. For example, the procedure unit can analyze the user's social media posts using a generation AI. For example, the procedure unit can use a generation AI to analyze the user's social media posts and guide the user through procedures related to the content. The procedure unit can also analyze the user's social media friend list using a generation AI and guide the user through payment methods used by the friends. For example, the procedure unit can use a generation AI to analyze the user's social media friend list and guide the user through payment methods used by the friends. The procedure unit can also analyze the time periods during which the user is active on social media using a generation AI and guide the user through procedures related to those time periods. For example, the procedure unit can use a generation AI to analyze the time periods during which the user is active on social media and guide the user through procedures related to those time periods. In this way, the user's social media activity can be analyzed to guide the user through related procedures. Some or all of the above-described processing in the procedure unit can be performed using a generation AI, or can be performed without a generation AI. For example, the procedure unit can input the user's social media data into a generation AI, which can then guide the user through related procedures based on that data.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The interactive vending machine system can further include a health management unit that monitors the user's health condition. The health management unit uses sensors to collect biometric data such as the user's heart rate, blood pressure, and body temperature, and analyzes it using generative AI. For example, if the user has high blood pressure, the health management unit can communicate that information to the provision unit, which can then recommend a low-sodium drink suitable for the user. The health management unit can also instruct the dialogue unit to adjust the content of the dialogue based on the user's health condition. For example, if the user is tired, the dialogue unit can use a tone of voice that encourages relaxation. This makes it possible to suggest products and dialogue that are optimal for the user's health condition.

[0053] The provision unit can suggest products that match the season or event based on the user's purchase history. For example, cold drinks and ice cream can be suggested in the summer, and hot drinks and soup in the winter. The provision unit can also suggest limited-edition products for specific events (e.g., Christmas or Halloween). Furthermore, the provision unit can suggest special products or discounts for the user's birthday or anniversary. This makes the user's purchasing experience more personalized and increases satisfaction.

[0054] The procedure unit can expand the user's payment method options and support virtual currencies and point systems. For example, if a user wishes to pay with virtual currencies such as Bitcoin or Ethereum, the procedure unit can guide the user through the payment procedure. Also, if a user wishes to pay using a point system, the procedure unit can check the user's point balance and guide the user through the payment procedure. Furthermore, the procedure unit can enable the user to make payments using a combination of multiple payment methods. This increases the user's payment flexibility and convenience.

[0055] The dialogue unit can customize the dialogue content taking into account the cultural background of the user. For example, if the user is Japanese, the dialogue unit can conduct dialogue based on Japanese culture and customs. If the user is American, the dialogue unit can conduct dialogue based on American culture and customs. Furthermore, the dialogue unit can adjust the dialogue content taking into account the religious background of the user. This enables optimal dialogue according to the user's cultural background.

[0056] The recognition unit can analyze the user's past interaction history and learn the user's preferences and interests. For example, if the user has frequently purchased a particular drink in the past, the recognition unit can communicate that information to the provision unit, which can then preferentially suggest that drink. Also, if the user has frequently asked a particular question in the past, the recognition unit can communicate that information to the dialogue unit, which can then preferentially provide information related to that question. Furthermore, the recognition unit can also suggest new products that the user might be interested in based on the user's past interaction history. This makes it possible to make optimal suggestions based on the user's preferences and interests.

[0057] The recognition unit can prioritize recognition of regional languages ​​and dialects by taking into account the user's geographical location information. For example, if the user is in the Kansai region, the recognition unit can prioritize recognition of the Kansai dialect. Also, if the user is in Okinawa, the recognition unit can prioritize recognition of the Okinawa dialect. Furthermore, if the user is in a specific tourist destination, the recognition unit can prioritize recognition of languages ​​and dialects commonly used in that region. This enables optimal language recognition according to the user's geographical location information.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The recognition unit recognizes the user's language. The recognition unit uses voice recognition technology to identify the user's language and can support multiple languages, including English, French, and Chinese. It is also possible to identify the user's language using generation AI. Step 2: The dialogue unit engages in dialogue based on the language recognized by the recognition unit. The dialogue unit starts dialogue in the recognized language when the user approaches the vending machine, and can also use a generation AI to dialogue with the user. This supports the user in selecting the product they want to purchase. Step 3: The provider provides information about the product selected by the user. The provider informs the user of the drink's ingredients, allergy information, price, etc., and can also provide product information using a generation AI. This allows the user to make a purchase decision after gaining sufficient information about the product. Step 4: The procedure section supports the purchase process based on the information provided by the provision section. The procedure section supports multiple payment methods, including cash, credit cards, and electronic money, and can also support the purchase process using generation AI. This allows the user to proceed with the purchase process by providing information on payment methods for the product selected by the user.

[0060] (Example 2) An interactive vending machine system according to an embodiment of the present invention is a multilingual interactive vending machine incorporating a generation AI. When a user approaches the vending machine, the generation AI recognizes the user's language and initiates a dialogue in the appropriate language. Next, the user selects a product, and the generation AI provides information about the product and assists with the purchase process. This allows all users, including foreign tourists, to smoothly purchase products. For example, when a user approaches the vending machine, the generation AI identifies the user's language using voice recognition technology. Multiple languages, including English, French, and Chinese, are supported. This allows foreign tourists to interact in their own language. Next, when the user selects a product, the generation AI provides information about the product. For example, the AI ​​can provide the user with information about the drink's ingredients, allergy information, and price. This allows the user to make an informed purchase decision. Furthermore, the generation AI assists with the purchase process. After the user selects a product, the generation AI guides the user through payment options and proceeds with the purchase. Multiple payment methods, including cash, credit card, and electronic money, are supported. This allows the user to select the payment method that best suits them and complete the purchase smoothly. This system allows all users, including foreign tourists, to purchase products smoothly. For example, when an English-speaking tourist approaches a vending machine, the generating AI will begin a dialogue in English, supporting them from product selection to the purchase process. This allows them to purchase products with peace of mind without feeling any language barrier. The generating AI also records the user's purchase history, which can be used as a reference for the next purchase. For example, if a customer wants to repurchase a product they previously purchased, the generating AI will provide that information, allowing them to smoothly proceed with the purchase process. This improves user convenience and is expected to lead to an increase in repeat customers. In this way, a multilingual interactive vending machine equipped with generating AI can provide a smooth purchasing experience and improve convenience for all users, including foreign tourists.This allows the interactive vending machine system to enable all users, including foreign tourists, to purchase products smoothly.

[0061] An interactive vending machine system according to an embodiment includes a recognition unit, a dialogue unit, a provision unit, and a procedure unit. The recognition unit recognizes a user's language. The recognition unit identifies the user's language using, for example, voice recognition technology. For example, the recognition unit can support multiple languages, such as English, French, and Chinese. The recognition unit can also identify the user's language using a generation AI. For example, the generation AI identifies the user's language using voice recognition technology and conducts a dialogue based on that language. The dialogue unit conducts a dialogue based on the language recognized by the recognition unit. For example, the dialogue unit initiates a dialogue in the recognized language when the user approaches the vending machine. The dialogue unit can also conduct a dialogue with the user using the generation AI. For example, the generation AI conducts a dialogue based on the user's language and supports the user in selecting a product they wish to purchase. The provision unit provides information about the product selected by the user. For example, the provision unit informs the user of the drink's ingredients, allergy information, price, etc. The provision unit can also provide product information using the generation AI. For example, the generation AI provides information about a product selected by a user, allowing the user to make a purchase decision after gaining sufficient information about the product. The procedure unit supports the purchase procedure based on the information provided by the provision unit. The procedure unit supports multiple payment methods, such as cash, credit card, and electronic money. The procedure unit can also support the purchase procedure using the generation AI. For example, the generation AI guides the user through payment methods for the product selected by the user and proceeds with the purchase procedure. As a result, the interactive vending machine system according to the embodiment can enable all users, including foreign tourists, to purchase products smoothly.

[0062] The recognition unit can identify the user's language using speech recognition technology. Examples of speech recognition technology include deep learning-based speech recognition technology and HMM (hidden Markov model). The recognition unit can identify the user's language using deep learning-based speech recognition technology. Deep learning-based speech recognition technology learns large amounts of speech data to achieve highly accurate speech recognition. For example, the recognition unit receives the user's speech as input and identifies the language using a deep learning model. The recognition unit can also identify the user's language using an HMM. The HMM models temporal changes in speech and achieves highly accurate speech recognition. For example, the recognition unit receives the user's speech as input and identifies the language using the HMM. This allows the speech recognition technology to accurately identify the user's language. Some or all of the above-described processing in the recognition unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the recognition unit can input the user's speech data to a generation AI, which can identify the language from the speech data.

[0063] The provision unit can communicate the drink's ingredients, allergy information, and price to the user. The provision unit, for example, provides the drink's ingredient information to the user. The ingredient information includes, for example, major ingredients and additives. The provision unit can also use the generation AI to provide the ingredient information to the user. For example, the generation AI analyzes the drink's ingredient information and provides it to the user. The provision unit can also provide allergy information to the user. The allergy information includes, for example, specific allergens and the degree of allergic reaction. The provision unit can also use the generation AI to provide the allergy information to the user. For example, the generation AI analyzes the drink's allergy information and provides it to the user. The provision unit can also provide price information to the user. The price information includes, for example, a price including tax and a discount price. The provision unit can also use the generation AI to provide the price information to the user. For example, the generation AI analyzes the drink's price information and provides it to the user. This allows the user to make a purchase decision after gaining sufficient information about the product. Some or all of the above-mentioned processing in the provision unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the provision unit can input information about the drink's ingredients, allergy information, and price information into the generation AI, which can then analyze the information and provide it to the user.

[0064] The procedure unit can support multiple payment methods, including cash, credit cards, and electronic money. The procedure unit, for example, supports cash payments. Cash payments include, for example, banknotes and coins. The procedure unit can also use a generation AI to support cash payment procedures. For example, the generation AI guides the user through the procedure when inserting cash. The procedure unit can also support credit card payments. Credit card payments include, for example, VISA, MasterCard, and American Express. The procedure unit can also use a generation AI to support credit card payment procedures. For example, the generation AI guides the user through the procedure when inserting a credit card. The procedure unit can also support electronic money payments. Electronic money payments include, for example, transportation IC cards, two-dimensional code (e.g., QR code) payments, and mobile payments. The procedure unit can also use a generation AI to support electronic money payment procedures. For example, the generation AI guides the user through the procedure when using electronic money. This allows the user to select a payment method that suits them and smoothly complete a purchase. Some or all of the above-described processing in the procedure section may be performed using the generation AI, or may be performed without using the generation AI. For example, the procedure section may input the user's payment method selection into the generation AI, which may then guide the user through the payment procedure.

[0065] The providing unit records the user's purchase history and can refer to it the next time they make a purchase. The providing unit, for example, records the user's purchase history. The purchase history includes, for example, the purchase date and time, the purchased items, and the purchase amount. The providing unit can also use the generation AI to record the purchase history. For example, the generation AI stores the user's purchase history in a database and uses it as a reference the next time they make a purchase. The providing unit can also provide information that will be useful the next time they make a purchase based on the user's purchase history. For example, if the providing unit wants to repurchase a product that they previously purchased, the generation AI can provide that information to facilitate a smooth purchase process. This improves user convenience and is expected to increase repeat customers. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's purchase history into the generation AI, which can analyze the information and provide information that will be useful the next time they make a purchase.

[0066] The dialogue unit can initiate a dialogue in the recognized language when a user approaches the vending machine. For example, the dialogue unit detects the user's approach using a distance sensor when the user approaches the vending machine. The distance sensor includes, for example, an infrared sensor or an ultrasonic sensor. The dialogue unit can also detect the user's approach using a generation AI. For example, the generation AI analyzes data from the distance sensor to detect the user's approach. The dialogue unit can also recognize the user's face using a camera to detect the user's approach. For example, the camera detects the user's face using facial recognition technology to detect the user's approach. The dialogue unit can also analyze camera data using the generation AI to detect the user's approach. This allows a dialogue to be smoothly initiated when the user approaches the vending machine. Some or all of the above-described processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input data from a distance sensor or a camera into the generation AI, which can then detect the user's approach and initiate a dialogue.

[0067] The recognition unit can estimate the user's emotion and adjust the accuracy of language recognition based on the estimated user emotion. For example, the recognition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the recognition unit calculates an emotion score based on changes in facial expression and adjusts the accuracy of language recognition. The recognition unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the recognition unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the accuracy of language recognition. Furthermore, the recognition unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the recognition unit calculates an emotion score based on heart rate fluctuations and adjusts the accuracy of language recognition. This adjusts the accuracy of language recognition according to the user's emotion, thereby reducing misrecognition. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-mentioned processing in the recognition unit may be performed using or without the generation AI. For example, the recognition unit may input image data of the user taken with a camera into the generation AI, which may then estimate the user's emotions and adjust the accuracy of language recognition.

[0068] The recognition unit can analyze the user's past dialogue history and select the optimal language recognition algorithm. For example, the recognition unit retrieves the user's past dialogue history from a database and analyzes it using a generation AI. For example, the recognition unit selects the optimal language recognition algorithm using the generation AI based on the languages ​​the user has used in the past. The recognition unit can also improve recognition accuracy by learning the accent and pronunciation of a specific language from the user's past dialogue history. For example, the recognition unit uses a generation AI to analyze the user's past dialogue history and learn the accent and pronunciation of a specific language. Furthermore, the recognition unit can adjust the recognition accuracy of a language that the user has previously misrecognized based on that language. For example, the recognition unit uses a generation AI to analyze the user's past dialogue history and adjust the recognition accuracy of the misrecognized language. This improves recognition accuracy by selecting the optimal language recognition algorithm based on the user's past dialogue history. Some or all of the above-mentioned processing in the recognition unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recognition unit can input the user's past dialogue history into the generation AI, which can then select the optimal language recognition algorithm.

[0069] The recognition unit can improve recognition accuracy by taking into account the user's pronunciation and accent during language recognition. For example, the recognition unit learns the user's pronunciation characteristics and performs language recognition based on those characteristics using a generation AI. For example, the recognition unit learns the user's pronunciation characteristics using a generation AI and performs language recognition based on those characteristics. The recognition unit can also take the user's accent into consideration and perform language recognition adapted to that accent using a generation AI. For example, the recognition unit learns the user's accent using a generation AI and performs language recognition adapted to that accent. Furthermore, the recognition unit can learn the user's pronunciation habits and improve recognition accuracy based on those habits using a generation AI. For example, the recognition unit learns the user's pronunciation habits using a generation AI and improves recognition accuracy based on those habits. This improves recognition accuracy by taking the user's pronunciation and accent into consideration. Some or all of the above-described processing in the recognition unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the recognition unit can input data on the user's pronunciation and accent into the generation AI, and the generation AI can improve recognition accuracy based on that data.

[0070] The recognition unit can estimate a user's emotions and determine the priority of languages ​​to recognize based on the estimated user emotions. For example, the recognition unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the recognition unit calculates an emotion score based on changes in facial expressions and determines the priority of languages ​​to recognize. The recognition unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the recognition unit analyzes the tone and speed of the voice to calculate an emotion score and determine the priority of languages ​​to recognize. Furthermore, the recognition unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate emotions using an emotion estimation algorithm. For example, the recognition unit calculates an emotion score based on heart rate fluctuations and determines the priority of languages ​​to recognize. This allows for dialogue in the most appropriate language by determining the priority of languages ​​to recognize based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recognition unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recognition unit may input image data of a user captured by a camera into the generation AI, and the generation AI may estimate the user's emotions and determine the priority of the languages ​​to be recognized.

[0071] During language recognition, the recognition unit can prioritize recognizing highly relevant languages ​​by taking into account the user's geographical location information. For example, the recognition unit obtains the user's geographical location information from GPS data and analyzes it using a generation AI. For example, when the user is in a tourist destination, the recognition unit causes the generation AI to prioritize recognizing languages ​​commonly used in that area. Furthermore, when the user is in an airport, the recognition unit can also prioritize recognizing internationally accepted languages. For example, when the user is in an airport, the recognition unit uses the generation AI to prioritize recognizing English or other internationally accepted languages. Furthermore, when the user is in a specific country, the recognition unit can also prioritize recognizing the official language of that country. For example, when the user is in a specific country, the recognition unit uses the generation AI to prioritize recognizing the official language of that country. This allows highly relevant languages ​​to be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the recognition unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recognition unit can input the user's geographical location information into the generation AI, which can then prioritize recognizing highly relevant language based on that information.

[0072] During language recognition, the recognition unit can analyze the user's social media activities and recognize associated languages. The recognition unit, for example, analyzes the user's social media posts using a generation AI. For example, the recognition unit uses a generation AI to analyze the user's social media posts and recognize the language. The recognition unit can also analyze the user's social media friend list using a generation AI and recognize the language used by the friends. For example, the recognition unit uses a generation AI to analyze the user's social media friend list and recognize the language used by the friends. The recognition unit can also analyze the time periods during which the user is active on social media using a generation AI and recognize the language used during those time periods. For example, the recognition unit uses a generation AI to analyze the time periods during which the user is active on social media and recognize the language used during those time periods. In this way, associated languages ​​can be recognized by analyzing the user's social media activities. Some or all of the above-described processing in the recognition unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the recognition unit can input the user's social media data into a generation AI, which can recognize associated languages ​​based on that data.

[0073] The dialogue unit can estimate the user's emotions and adjust the tone and expression of the dialogue based on the estimated user's emotions. For example, the dialogue unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the dialogue unit calculates an emotion score based on changes in facial expressions and adjusts the tone and expression of the dialogue. The dialogue unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the dialogue unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the tone and expression of the dialogue. Furthermore, the dialogue unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the dialogue unit calculates an emotion score based on fluctuations in heart rate and adjusts the tone and expression of the dialogue. This allows for more appropriate dialogue by adjusting the tone and expression of the dialogue according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit may input image data of a user captured by a camera into the generation AI, which may then estimate the user's emotions and adjust the tone and expression of the dialogue.

[0074] During a dialogue, the dialogue unit can provide optimal dialogue content by referring to the user's past dialogue history. For example, the dialogue unit retrieves the user's past dialogue history from a database and analyzes it using a generation AI. For example, the dialogue unit may provide dialogue content related to a product that the user has previously purchased using the generation AI. The dialogue unit may also provide dialogue content tailored to the user's preferences based on the user's past dialogue history. For example, the dialogue unit may use the generation AI to analyze the user's past dialogue history and provide dialogue content tailored to the user's preferences. Furthermore, the dialogue unit may provide dialogue content related to a question that the user has previously asked using the generation AI. For example, the dialogue unit may use the generation AI to analyze the user's past dialogue history and provide dialogue content related to the question. In this way, optimal dialogue content can be provided by referring to the user's past dialogue history. Some or all of the above-described processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit may input the user's past dialogue history into the generation AI, and the generation AI may provide optimal dialogue content based on that information.

[0075] The dialogue unit can customize dialogue content during dialogue, taking into account the user's cultural background and habits. For example, the dialogue unit allows the generation AI to provide dialogue content appropriate for the culture based on the user's cultural background. For example, the dialogue unit uses the generation AI to analyze the user's cultural background and provide dialogue content appropriate for the culture. The dialogue unit can also take the user's habits into account and allow the generation AI to provide dialogue content tailored to the habits. For example, the dialogue unit uses the generation AI to analyze the user's habits and provide dialogue content tailored to the habits. Furthermore, the dialogue unit can also provide dialogue content appropriate for the time of year based on holidays and events in the user's country. For example, the dialogue unit uses the generation AI to analyze holidays and events in the user's country and provide dialogue content appropriate for the time of year. This allows more appropriate dialogue content to be provided by taking into account the user's cultural background and habits. Some or all of the above-described processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input data on the user's cultural background and habits into the generation AI, and the generation AI can customize the dialogue content based on that data.

[0076] The dialogue unit can estimate the user's emotions and adjust the length and level of detail of the dialogue based on the estimated user emotions. For example, the dialogue unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the dialogue unit calculates an emotion score based on changes in facial expressions and adjusts the length and level of detail of the dialogue. The dialogue unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the dialogue unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length and level of detail of the dialogue. Furthermore, the dialogue unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the dialogue unit calculates an emotion score based on heart rate fluctuations and adjusts the length and level of detail of the dialogue. This allows for more appropriate dialogue by adjusting the length and level of detail of the dialogue according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit may input image data of the user captured by a camera into the generation AI, which may then estimate the user's emotions and adjust the length and level of detail of the dialogue.

[0077] During a dialogue, the dialogue unit can provide optimal dialogue content by taking into account the user's current situation and environment. For example, when the user is outdoors, the dialogue unit uses the generation AI to provide dialogue content appropriate for the environment. For example, when the user is outdoors, the dialogue unit uses the generation AI to provide dialogue content appropriate for the environment. Furthermore, when the user is in a quiet place, the dialogue unit can also have the generation AI provide a dialogue including detailed explanations. For example, when the user is in a quiet place, the dialogue unit uses the generation AI to provide a dialogue including detailed explanations. Furthermore, when the user is in a noisy place, the dialogue unit can also have the generation AI provide a concise and to-the-point dialogue. For example, when the user is in a noisy place, the dialogue unit uses the generation AI to provide a concise and to-the-point dialogue. This allows optimal dialogue content to be provided by taking into account the user's current situation and environment. Some or all of the above-described processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input data about the user's current situation and environment into the generation AI, and the generation AI can provide optimal dialogue content based on that data.

[0078] The dialogue unit can analyze the user's social media activity during dialogue and provide related dialogue content. The dialogue unit, for example, analyzes the user's social media posts using a generation AI. For example, the dialogue unit can use the generation AI to analyze the user's social media posts and engage in dialogue related to the posts. The dialogue unit can also analyze the user's social media friend list using a generation AI and engage in dialogue related to the friends. For example, the dialogue unit can use the generation AI to analyze the user's social media friend list and engage in dialogue related to the friends. The dialogue unit can also analyze the user's social media activity time period using a generation AI and engage in dialogue related to the time period. For example, the dialogue unit can use the generation AI to analyze the user's social media activity time period and engage in dialogue related to the time period. In this way, related dialogue content can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the dialogue unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the dialogue unit can input the user's social media data into a generation AI, and the generation AI can provide related dialogue content based on the data.

[0079] The providing unit can estimate the user's emotions and adjust the method of providing product information based on the estimated user emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression and adjusts the method of providing product information. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the method of providing product information. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on fluctuations in heart rate and adjusts the method of providing product information. This allows for more appropriate information provision by adjusting the method of providing product information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit may input image data of a user taken with a camera into the generation AI, and the generation AI may estimate the user's emotions and adjust the method of providing product information.

[0080] When providing product information, the providing unit can provide optimal information by referring to the user's past purchase history. For example, the providing unit retrieves the user's past purchase history from a database and analyzes it using a generation AI. For example, the providing unit causes the generation AI to provide information related to products based on products the user has previously purchased. The providing unit can also cause the generation AI to provide information tailored to the user's preferences based on the user's past purchase history. For example, the providing unit uses the generation AI to analyze the user's past purchase history and provide information tailored to the user's preferences. Furthermore, the providing unit can also cause the generation AI to provide information related to questions the user has previously asked based on the questions. For example, the providing unit uses the generation AI to analyze the user's past purchase history and provide information related to the questions asked in the past. In this way, optimal product information can be provided by referring to the user's past purchase history. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past purchase history into the generation AI, and the generation AI can provide optimal product information based on that information.

[0081] When providing product information, the providing unit can customize the information by taking into account the user's health condition and allergy information. For example, the providing unit retrieves the user's health condition and allergy information from a database and analyzes it using a generation AI. For example, if the user has an allergy, the providing unit causes the generation AI to provide information related to the allergy. The providing unit can also take the user's health condition into consideration and cause the generation AI to provide product information appropriate for that condition. For example, the providing unit uses the generation AI to analyze the user's health condition and provide product information appropriate for that condition. Furthermore, if the user has a specific health goal, the providing unit can cause the generation AI to provide product information tailored to that goal. For example, the providing unit uses the generation AI to analyze the user's health goal and provide product information tailored to that goal. This allows more appropriate product information to be provided by taking the user's health condition and allergy information into consideration. Some or all of the above-described processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the user's health condition and allergy information into the generation AI, and the generation AI can customize product information based on that information.

[0082] The providing unit can estimate the user's emotions and determine the priority of providing product information based on the estimated user emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression and determines the priority of providing product information. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of providing product information. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations and determines the priority of providing product information. This allows the optimal information to be provided by determining the priority of providing product information based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit may input image data of a user taken with a camera into the generation AI, and the generation AI may estimate the user's emotions and determine the priority of providing product information.

[0083] When providing product information, the providing unit can prioritize providing highly relevant information by taking into account the user's geographical location information. The providing unit, for example, acquires the user's geographical location information from GPS data and analyzes it using a generation AI. For example, when the user is in a tourist destination, the providing unit can have the generation AI provide product information that is commonly purchased in that area. Furthermore, when the user is at an airport, the providing unit can have the generation AI provide internationally accepted product information. For example, when the user is at an airport, the providing unit can use the generation AI to provide internationally accepted product information. Furthermore, when the user is in a specific country, the providing unit can have the generation AI provide product information that is popular in that country. For example, when the user is in a specific country, the providing unit can use the generation AI to provide product information that is popular in that country. In this way, highly relevant product information can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's geographical location information into the generation AI, and the generation AI can provide highly relevant product information based on that information.

[0084] When providing product information, the providing unit can analyze the user's social media activity and provide related information. The providing unit, for example, analyzes the user's social media posts using a generation AI. For example, the providing unit can use the generation AI to analyze the user's social media posts and provide product information related to the content. The providing unit can also analyze the user's social media friend list using a generation AI and provide product information purchased by the friends. For example, the providing unit can use the generation AI to analyze the user's social media friend list and provide product information purchased by the friends. The providing unit can also analyze the user's social media activity time period using a generation AI and provide product information related to that time period. For example, the providing unit can use the generation AI to analyze the user's social media activity time period and provide product information related to that time period. In this way, related product information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's social media data into a generation AI, and the generation AI can provide related product information based on that data.

[0085] The processing unit can estimate the user's emotions and adjust the purchasing procedure guidance method based on the estimated user emotions. For example, the processing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the processing unit calculates an emotion score based on changes in facial expressions and adjusts the purchasing procedure guidance method. The processing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the processing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the purchasing procedure guidance method. Furthermore, the processing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the processing unit calculates an emotion score based on heart rate fluctuations and adjusts the purchasing procedure guidance method. This allows the purchasing procedure guidance method to be adjusted according to the user's emotions, resulting in more appropriate guidance. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the procedure unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the procedure unit may input image data of a user taken with a camera into the generation AI, which may infer the user's emotions and adjust the method of guidance during the purchase process.

[0086] During the purchase process, the procedure unit can guide the user through the optimal procedure by referring to the user's past purchase history. For example, the procedure unit retrieves the user's past purchase history from a database and analyzes it using a generation AI. For example, the procedure unit may cause the generation AI to prioritize a payment method based on a payment method the user has used in the past. The procedure unit may also cause the generation AI to guide the user through procedures tailored to the user's preferences based on the user's past purchase history. For example, the procedure unit may use the generation AI to analyze the user's past purchase history and guide the user through procedures tailored to the user's preferences. Furthermore, the procedure unit may also cause the generation AI to guide the user through procedures related to products the user has previously purchased based on the products the user has previously purchased. For example, the procedure unit may use the generation AI to analyze the user's past purchase history and guide the user through procedures related to products the user has previously purchased. In this way, the optimal procedure can be guided by referring to the user's past purchase history. Some or all of the above-described processing in the procedure unit may be performed using or without the generation AI. For example, the procedure unit may input the user's past purchase history into the generation AI, and the generation AI may guide the user through the optimal procedure based on that information.

[0087] The procedure unit can customize the purchase procedure by taking into account the user's payment method preferences. For example, the procedure unit retrieves the user's payment method preferences from a database and analyzes them using a generation AI. For example, if the user prefers cash, the procedure unit can prioritize the cash payment procedure. Also, if the user prefers credit cards, the procedure unit can prioritize the credit card payment procedure. For example, the procedure unit can analyze the user's payment method preferences using the generation AI and prioritize the credit card payment procedure. Furthermore, if the user prefers electronic money, the procedure unit can prioritize the electronic money payment procedure. For example, the procedure unit can analyze the user's payment method preferences using the generation AI and prioritize the electronic money payment procedure. This allows for more appropriate procedures to be guided by taking the user's payment method preferences into consideration. Some or all of the above-described processing in the procedure unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the procedure section can input the user's payment preferences into the generation AI, which can then customize the procedure based on that information.

[0088] The processing unit can estimate the user's emotions and determine the priority of the purchase process based on the estimated user emotions. For example, the processing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the processing unit calculates an emotion score based on changes in facial expressions and determines the priority of the purchase process. The processing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the processing unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of the purchase process. Furthermore, the processing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the processing unit calculates an emotion score based on heart rate fluctuations and determines the priority of the purchase process. This allows the user to prioritize the purchase process according to the user's emotions and guide them through the optimal process. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processes in the procedure unit may be performed using or without the generation AI. For example, the procedure unit may input image data of the user taken with a camera into the generation AI, which may then estimate the user's emotions and determine the priority of the purchase procedure.

[0089] During the purchase process, the procedure unit can guide the user to the optimal procedure by taking into account the user's geographical location information. For example, the procedure unit obtains the user's geographical location information from GPS data and analyzes it using a generation AI. For example, if the user is in a tourist destination, the procedure unit can have the generation AI prioritize local payment methods. Furthermore, if the user is at an airport, the procedure unit can have the generation AI prioritize internationally accepted payment methods. For example, if the user is at an airport, the procedure unit can have the generation AI prioritize internationally accepted payment methods. Furthermore, if the user is in a specific country, the generation AI can prioritize local payment methods. For example, if the user is in a specific country, the generation AI can prioritize local payment methods. This allows the optimal procedure to be guided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the procedure unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the procedure unit can input the user's geographical location information into the generation AI, which can then guide the user to the optimal procedure based on that information.

[0090] The procedure unit can analyze the user's social media activity during the purchase process and guide the user through related procedures. For example, the procedure unit can analyze the user's social media posts using a generation AI. For example, the procedure unit can use a generation AI to analyze the user's social media posts and guide the user through procedures related to the content. The procedure unit can also analyze the user's social media friend list using a generation AI and guide the user through payment methods used by the friends. For example, the procedure unit can use a generation AI to analyze the user's social media friend list and guide the user through payment methods used by the friends. The procedure unit can also analyze the time periods during which the user is active on social media using a generation AI and guide the user through procedures related to those time periods. For example, the procedure unit can use a generation AI to analyze the time periods during which the user is active on social media and guide the user through procedures related to those time periods. In this way, the user's social media activity can be analyzed to guide the user through related procedures. Some or all of the above-described processing in the procedure unit can be performed using a generation AI, or can be performed without a generation AI. For example, the procedure unit can input the user's social media data into a generation AI, which can then guide the user through related procedures based on that data. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned recognition unit, dialogue unit, provision unit, and procedure unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recognition unit identifies the user's language using the camera 42 or microphone 38B of the smart device 14, and the language is recognized by the control unit 46A. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and conducts a dialogue based on the recognized language. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides information on a product selected by the user. The procedure unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports the purchase procedure. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned recognition unit, dialogue unit, provision unit, and procedure unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit identifies the user's language using the camera 42 or microphone 238 of the smart glasses 214, and the language is recognized by the control unit 46A. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and conducts a dialogue based on the recognized language. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides information on a product selected by the user. The procedure unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports the purchase procedure. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned recognition unit, dialogue unit, provision unit, and procedure unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the recognition unit identifies the user's language using the camera 42 or microphone 238 of the headset type terminal 314, and the language is recognized by the control unit 46A. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and conducts a dialogue based on the recognized language. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides information on a product selected by the user. The procedure unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports the purchase procedure. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned recognition unit, dialogue unit, provision unit, and procedure unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recognition unit identifies the user's language using the camera 42 or microphone 238 of the robot 414, and the language is recognized by the control unit 46A. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and conducts a dialogue based on the recognized language. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides information on a product selected by the user. The procedure unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports the purchase procedure.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The interactive vending machine system can further include a health management unit that monitors the user's health condition. The health management unit uses sensors to collect biometric data such as the user's heart rate, blood pressure, and body temperature, and analyzes it using generative AI. For example, if the user has high blood pressure, the health management unit can communicate that information to the provision unit, which can then recommend a low-sodium drink suitable for the user. The health management unit can also instruct the dialogue unit to adjust the content of the dialogue based on the user's health condition. For example, if the user is tired, the dialogue unit can use a tone of voice that encourages relaxation. This makes it possible to suggest products and dialogue that are optimal for the user's health condition.

[0093] The recognition unit can analyze the tone and speed of the user's voice to estimate the user's level of urgency. For example, if the user sounds hurried, the recognition unit conveys that information to the procedure unit, which can then guide the user through a quick purchase procedure. The recognition unit can also instruct the procedure unit to guide the user through a standard procedure if the user's tone of voice is calm. Furthermore, if the user's tone of voice is unstable, the recognition unit can instruct the dialogue unit to engage in dialogue that reassures the user. This makes it possible to provide the optimal response according to the user's level of urgency.

[0094] The provision unit can suggest products that match the season or event based on the user's purchase history. For example, cold drinks and ice cream can be suggested in the summer, and hot drinks and soup in the winter. The provision unit can also suggest limited-edition products for specific events (e.g., Christmas or Halloween). Furthermore, the provision unit can suggest special products or discounts for the user's birthday or anniversary. This makes the user's purchasing experience more personalized and increases satisfaction.

[0095] The procedure unit can expand the user's payment method options and support virtual currencies and point systems. For example, if a user wishes to pay with virtual currencies such as Bitcoin or Ethereum, the procedure unit can guide the user through the payment procedure. Also, if a user wishes to pay using a point system, the procedure unit can check the user's point balance and guide the user through the payment procedure. Furthermore, the procedure unit can enable the user to make payments using a combination of multiple payment methods. This increases the user's payment flexibility and convenience.

[0096] The providing unit can estimate the user's emotions and adjust the way in which the product is explained based on the estimated emotions. For example, if the user is excited, the providing unit can provide a concise and to-the-point explanation. If the user is relaxed, the providing unit can provide a detailed explanation. Furthermore, if the user is feeling anxious, the providing unit can provide an explanation that gives a sense of security. This makes it possible to provide optimal information according to the user's emotions.

[0097] The dialogue unit can customize the dialogue content taking into account the cultural background of the user. For example, if the user is Japanese, the dialogue unit can conduct dialogue based on Japanese culture and customs. If the user is American, the dialogue unit can conduct dialogue based on American culture and customs. Furthermore, the dialogue unit can adjust the dialogue content taking into account the religious background of the user. This enables optimal dialogue according to the user's cultural background.

[0098] The recognition unit can estimate the stress level of the user from their facial expressions and voice, and adjust the dialogue content based on the estimated stress level. For example, if the user is in a high stress state, the recognition unit conveys that information to the dialogue unit, which can then engage in a dialogue that helps the user relax. Alternatively, if the user is in a low stress state, the dialogue unit can engage in a normal dialogue. Furthermore, the recognition unit can instruct the provision unit to adjust product suggestions according to the user's stress level. This allows for optimal responses according to the user's stress level.

[0099] The recognition unit can analyze the user's past interaction history and learn the user's preferences and interests. For example, if the user has frequently purchased a particular drink in the past, the recognition unit can communicate that information to the provision unit, which can then preferentially suggest that drink. Also, if the user has frequently asked a particular question in the past, the recognition unit can communicate that information to the dialogue unit, which can then preferentially provide information related to that question. Furthermore, the recognition unit can also suggest new products that the user might be interested in based on the user's past interaction history. This makes it possible to make optimal suggestions based on the user's preferences and interests.

[0100] The recognition unit can prioritize recognition of regional languages ​​and dialects by taking into account the user's geographical location information. For example, if the user is in the Kansai region, the recognition unit can prioritize recognition of the Kansai dialect. Also, if the user is in Okinawa, the recognition unit can prioritize recognition of the Okinawa dialect. Furthermore, if the user is in a specific tourist destination, the recognition unit can prioritize recognition of languages ​​and dialects commonly used in that region. This enables optimal language recognition according to the user's geographical location information.

[0101] The recognition unit can estimate the user's emotions and provide language recognition feedback based on the estimated emotions. For example, if the user is dissatisfied, the recognition unit conveys that information to the dialogue unit, which can then apologize or suggest improvements to the user. If the user is satisfied, the dialogue unit can provide positive feedback based on that information. Furthermore, if the user is confused, the dialogue unit can provide a detailed explanation based on that information. This makes it possible to provide optimal feedback according to the user's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The recognition unit recognizes the user's language. The recognition unit uses voice recognition technology to identify the user's language and can support multiple languages, including English, French, and Chinese. It is also possible to identify the user's language using generation AI. Step 2: The dialogue unit engages in dialogue based on the language recognized by the recognition unit. The dialogue unit starts dialogue in the recognized language when the user approaches the vending machine, and can also use a generation AI to dialogue with the user. This supports the user in selecting the product they want to purchase. Step 3: The provider provides information about the product selected by the user. The provider informs the user of the drink's ingredients, allergy information, price, etc., and can also provide product information using a generation AI. This allows the user to make a purchase decision after gaining sufficient information about the product. Step 4: The procedure section supports the purchase process based on the information provided by the provision section. The procedure section supports multiple payment methods, including cash, credit cards, and electronic money, and can also support the purchase process using generation AI. This allows the user to proceed with the purchase process by providing information on payment methods for the product selected by the user.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 recognition unit for recognizing a language of a user; a dialogue unit that conducts dialogue based on the language recognized by the recognition unit; a providing unit that provides information on a product selected by a user; a procedure unit that supports a purchase procedure based on the information provided by the provision unit; Equipped with A system characterized by:

2. The recognition unit Identifying the user's language using voice recognition technology 2. The system of claim 1.

3. The providing unit Inform users about drink ingredients, allergy information, and prices 2. The system of claim 1.

4. The procedure division Accepts multiple payment methods including cash, credit cards, and electronic money 2. The system of claim 1.

5. The providing unit Record the user's purchase history and use it as a reference for the next purchase 2. The system of claim 1.

6. The dialogue unit When the user approaches the vending machine, it initiates a dialogue in the recognized language.

2. The system of claim 1.

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

2. The system of claim 1.

8. The recognition unit Analyze the user's past conversation history and select the optimal language recognition algorithm 2. The system of claim 1.

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