system

Abstract

An object of a system according to an embodiment is to generate an interactive character using a QR code and explain a product or a service through a dialogue with a user.SOLUTION: A system includes a QR code reading unit, a character generation unit, an explanation unit, and a communication unit. The QR code reading unit reads a QR code. The character generation unit generates a character based on the information read by the QR code reading unit. In the explanation section, the character generated by the character generation section explains the product or service. In the communication unit, the character generated by the character generation unit performs a conversation with the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that the information provided using QR codes is static and lacks interactive communication with users.

[0005] The system according to the embodiment aims to generate an interactive character using a QR code and explain products and services through dialogue with the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a QR code reader, a character generation unit, an explanation unit, and a communication unit. The QR code reader reads a QR code. The character generation unit generates a character based on information read by the QR code reader. The explanation unit uses the character generated by the character generation unit to explain products and services. The communication unit uses the character generated by the character generation unit to interact with the user. [Effects of the Invention]

[0007] The system according to the embodiment uses QR codes to generate interactive characters that can explain products and services through dialogue with users. [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) The AI ​​character system according to an embodiment of the present invention is a system in which an AI character corresponding to a store appears when a QR code is read. This AI character is created by inputting store information into a large-scale language model (LLM, generative AI). The character provides detailed explanations of recommended products and services and answers user questions, making it easier for users to understand and providing a highly satisfying experience. This allows the AI ​​character system to provide users with detailed information about products and services, providing a highly satisfying experience.

[0029] An AI character system according to an embodiment includes a QR code reader, a character generation unit, an explanation unit, and a communication unit. The QR code reader reads a QR code. For example, it scans a QR code using a smartphone camera and acquires the information. The QR code reader can read static and dynamic QR codes. For example, static QR codes have fixed information, while dynamic QR codes have changeable information. The character generation unit generates a character based on the information read by the QR code reader. For example, the generation AI inputs store information into a large-scale language model (LLM, generation AI) to determine the character's personality and appearance. The character generation unit can generate animated characters and 3D characters. For example, animated characters are dynamic characters, and 3D characters are three-dimensional characters. The explanation unit allows the character generated by the character generation unit to explain products and services. For example, the generation AI generates appropriate answers to user questions and explains products and services. The explanation unit can provide explanations using text or audio. For example, text explanations are displayed on the screen, and audio explanations are provided by a character speaking. The communication unit allows the character generated by the character generation unit to have a dialogue with the user. For example, the generation AI responds to the user's questions in real time and engages in a dialogue. The communication unit can also carry out voice dialogue and text dialogue. For example, in voice dialogue, the character responds by voice when the user speaks, and in text dialogue, the character responds by text to text entered by the user. This allows the AI ​​character system according to the embodiment to provide a user with detailed information about products and services, providing a highly satisfying experience.

[0030] The QR code reader can automatically obtain information about the nearest store using the user's location information and display the most appropriate AI character. For example, when a user scans a QR code, the QR code reader obtains the user's current location using the smartphone's GPS function and automatically obtains information about the nearest store. For example, if the user scans a QR code inside a shopping mall, the most appropriate AI character is displayed based on store information within the mall. The QR code reader also displays promotional information for nearby stores based on the user's location information. For example, if a user scans a QR code in front of a station, an AI character with special offer information for stores around the station appears. The QR code reader also uses the user's location information to display characters limited to a specific area. For example, if a QR code is scanned at a tourist spot, a character providing tourist information for that area appears. This improves user convenience by displaying the most appropriate AI character using the user's location information.

[0031] The QR code reader can refer to the user's past visit history and display a special character according to the frequency of visits. For example, when reading a QR code, the QR code reader refers to the user's past visit history and displays a special character according to the frequency of visits. For example, a character that gives a special greeting appears for regular customers. The QR code reader also displays a character that offers special promotions to users who have visited multiple times within a specific period based on the user's visit history. For example, a character that informs users of special benefits appears on the third visit. The QR code reader also uses the past visit history to generate a character that displays a personalized message to the user. For example, a character that provides information related to a product purchased on the previous visit appears. In this way, by referencing the user's past visit history and displaying a special character, user satisfaction is improved.

[0032] The QR code reader can use voice recognition technology to add a function that allows a character to be called by the user's voice. For example, when reading a QR code, the QR code reader can add a function that allows a character to be called by the user uttering a specific keyword using voice recognition technology. For example, a character will appear when the user says "hello." The QR code reader can also add a function that allows a user to control the character's behavior using voice commands when reading a QR code. For example, when the user says "tell me the menu," the character will display the menu. The QR code reader can also use voice recognition technology to analyze the tone and emotion of the user's voice and display a character that corresponds to that. For example, if the user is excited, a cheerful character will appear. This allows a character to be called by the user's voice using voice recognition technology.

[0033] The QR code reader can use AR technology to make a character appear in real space. For example, when reading a QR code, the QR code reader uses AR technology to make a character appear in real space. For example, a character may be displayed as standing on a table using a smartphone camera. The QR code reader also uses AR technology to provide an interactive experience in which a character walks around the user. For example, the character may approach the user and talk to them. When reading a QR code, the QR code reader also uses AR technology to make the character interact with a real object. For example, the character may bring a menu. This improves the user's experience by making the character appear in real space using AR technology.

[0034] The character generation unit can use the user's past interaction data to generate a character with a personality that is optimal for the user. For example, the character generation unit analyzes the user's past interaction data to generate a character with a personality based on the user's preferences and interests. For example, the character's personality is determined based on information about stores the user frequently visits. The character generation unit also uses the past interaction data to generate a character with a speaking style and tone that the user prefers. For example, if the user prefers a relaxed atmosphere, the character generation unit creates a character that speaks calmly. The character generation unit also generates a character with a personality that matches a specific event or promotion based on the user's interaction data. For example, a character that matches a seasonal event is created. In this way, the user's past interaction data is used to generate a character with an optimal personality, thereby improving user satisfaction.

[0035] The character generation unit can generate characters with a seasonal feel by reflecting seasonal event information for the store. The character generation unit generates characters with a seasonal feel based on, for example, seasonal event information for the store. For example, a Santa Claus character is created for the Christmas season. The character generation unit also changes the character's clothing and accessories by reflecting seasonal event information. For example, a character wearing a swimsuit is displayed in summer. The character generation unit also generates characters with personalities that match seasonal events. For example, a ghost character is created for Halloween and event information is provided to the user. In this way, the user's experience is improved by generating characters with a seasonal feel by reflecting seasonal event information.

[0036] The character generation unit can refer to the user's SNS account information and generate a character with a personality based on the user's interests and concerns. For example, the character generation unit analyzes the user's SNS account information and generates a character with a personality based on the user's interests and concerns. For example, if the user likes music, it creates a character that is knowledgeable about music. The character generation unit also analyzes the accounts the user follows and the content of their posts based on the SNS account information and generates a character with a personality corresponding to that. For example, if the user likes traveling, it creates a character that provides information about travel. The character generation unit also uses the user's SNS account information to generate a character with a personality tailored to a specific event or promotion. For example, it creates a character related to an event the user plans to attend. In this way, by referencing the user's SNS account information and generating a character with a personality based on the user's interests and concerns, user satisfaction is improved.

[0037] The character generation unit can generate a personalized character according to the age and gender of the user. The character generation unit generates a character with a personalized personality based on, for example, the user's age and gender. For example, a bright and energetic character is created for children, and a calm character is created for adults. The character generation unit also customizes the character's appearance and speaking style according to age and gender. For example, a character dressed in casual clothing is created for young people, and a character dressed in formal clothing for elderly people. The character generation unit also generates a character with a personality according to specific interests and concerns based on the user's age and gender. For example, a character knowledgeable about beauty and fashion is created for women. In this way, user satisfaction is improved by generating a personalized character according to the user's age and gender.

[0038] The explanation unit can refer to the user's past purchase history and recommend optimal products and services to the user. For example, the explanation unit analyzes the user's past purchase history and recommends optimal products and services based on the user's preferences and purchasing patterns. For example, it recommends new products related to products the user has previously purchased. The explanation unit also identifies products and services that the user is likely to be interested in based on the purchase history and makes personalized recommendations. For example, it prioritizes the recommendation of products in categories that the user frequently purchases. The explanation unit also uses the user's purchase history to recommend products and services tailored to specific events or promotions. For example, it recommends new products related to seasonal products that the user has previously purchased. In this way, the explanation unit can improve user satisfaction by referring to the user's past purchase history and recommending optimal products and services.

[0039] The explanation unit can collect real-time user feedback and dynamically adjust the content of the explanation based on that feedback. For example, while explaining a product or service, the explanation unit collects real-time user feedback and dynamically adjusts the content of the explanation based on that feedback. For example, the explanation unit provides detailed explanations of parts that the user is interested in. The explanation unit also analyzes user feedback in real time and builds a system that customizes the content of the explanation. For example, the explanation unit re-explains parts that the user finds difficult to understand. The explanation unit also provides explanations of products or services based on the user's interests and concerns based on the real-time feedback. For example, if the user is interested in a particular function, the explanation unit provides detailed information about that function. In this way, the explanation unit collects real-time user feedback and dynamically adjusts the content of the explanation, thereby deepening the user's understanding.

[0040] The explanation unit can add visual explanations using videos and images to promote user understanding. The explanation unit adds visual explanations using videos and images when explaining products or services, for example. For example, it displays a video showing how to use the product. The explanation unit also uses visual explanations to build a system that promotes user understanding. For example, it shows the features of the product using images. The explanation unit also adds explanations using videos and images to make it easier for users to understand visually. For example, it displays an image showing the internal structure of the product. In this way, adding visual explanations using videos and images promotes user understanding.

[0041] The explanation unit can provide multilingual support in accordance with the user's language setting. The explanation unit provides multilingual support in accordance with the user's language setting, for example, when explaining products or services. For example, explanations are provided in multiple languages, such as English, French, and Chinese. The explanation unit also builds a system that provides product or service explanations in an appropriate language based on the user's language setting. For example, if the user selects Japanese, explanations are provided in Japanese. The explanation unit also provides multilingual support and provides product or service explanations in a language that the user can easily understand. For example, if the user selects Spanish, explanations are provided in Spanish. This provides multilingual support in accordance with the user's language setting, thereby deepening the user's understanding.

[0042] The communication unit can refer to the user's past dialogue history and provide a more personalized response. For example, the communication unit analyzes the user's past dialogue history and provides a personalized response based on the user's preferences and interests. For example, it provides information related to topics the user has previously discussed. The communication unit also generates a character with a speaking style and tone that the user prefers based on the past dialogue history. For example, if the user prefers a relaxed atmosphere, it creates a character that speaks calmly. The communication unit also uses the user's dialogue history to provide a personalized response tailored to specific events or promotions. For example, it provides information tailored to seasonal events. In this way, by referring to the user's past dialogue history and providing a personalized response, user satisfaction is improved.

[0043] The communication unit can provide special content based on the user's preferences and interests. For example, the communication unit analyzes the user's preferences and interests and provides special content based on them. For example, if the user likes music, it provides information about music. The communication unit also builds a system that provides special content based on the user's interests. For example, if the user likes traveling, it provides information about travel. The communication unit also provides special content according to the user's preferences and interests, improving user satisfaction. For example, if the user likes cooking, it provides information about cooking. In this way, by providing special content based on the user's preferences and interests, user satisfaction is improved.

[0044] The communication unit can add a voice dialogue function to achieve more natural communication. For example, the communication unit can add a voice dialogue function to communication with a user's individual AI character to achieve more natural communication. For example, when the user speaks, the character responds by voice. The communication unit also uses the voice dialogue function to analyze the user's tone of voice and emotions and responds accordingly. For example, if the user is excited, the communication unit provides a detailed explanation. The communication unit also builds a system that provides personalized responses based on the user's voice dialogue data. For example, if the user wants to relax, the communication unit responds in a calm tone. In this way, adding the voice dialogue function achieves more natural communication.

[0045] The communication unit can incorporate game elements to increase user engagement. For example, the communication unit can incorporate game elements into communication with each user's individual AI character to increase user engagement. For example, a system can be introduced that allows points to be earned through dialogue with the character. The communication unit can also use game elements to build a system that allows users to communicate with characters while having fun. For example, users can interact with characters through quizzes or mini-games. The communication unit can also introduce rewards and perks for communication with characters to increase user engagement. For example, a system can be introduced that allows users to receive perks when they complete certain missions. In this way, incorporating game elements increases user engagement.

[0046] The communication unit can refer to the user's past interaction data in communication between characters and provide the user with optimal information. For example, the communication unit analyzes the user's past interaction data in communication between characters and provides the user with optimal information. For example, the communication unit provides information related to topics in which the user has shown interest in the past. The communication unit also generates conversations between characters with topics and tones that the user prefers based on the past interaction data. For example, if the user prefers a relaxed atmosphere, the communication unit creates characters that have calm conversations. The communication unit also uses the user's interaction data to generate conversations between characters that provide information tailored to specific events or promotions. For example, the communication unit provides information tailored to seasonal events. In this way, the communication unit can refer to the user's past interaction data and provide optimal information, thereby improving user satisfaction.

[0047] The communication unit can reflect the latest store information and event information in real time in communication between characters. The communication unit, for example, builds a system that reflects the latest store information and event information in real time in communication between characters. For example, information about new products is introduced in conversation between characters. The communication unit also generates conversations between characters based on store information that is updated in real time. For example, characters communicate information about events that are currently being held in conversation. The communication unit also reflects the latest store information and event information in real time and generates conversations between characters that provide new information to users. For example, characters communicate information about special sales in conversation. In this way, the latest store information and event information is reflected in real time, providing users with the latest information.

[0048] The communication unit allows characters to share information between different stores in communication with each other, thereby providing users with new discoveries. The communication unit, for example, builds a system for sharing information between different stores in communication between characters. For example, characters communicate promotional information about adjacent stores in conversation. The communication unit also generates conversations between characters that share information about different stores and provide users with new discoveries. For example, characters introduce information about stores that the user has never visited in conversation. The communication unit also shares information between stores and generates conversations between characters that provide users with new information. For example, characters communicate information about special offers at affiliated stores in conversation. In this way, information is shared between different stores, providing users with new discoveries.

[0049] The communication unit can hold interactive events in which users can participate in communication between characters. The communication unit, for example, builds a system for holding interactive events in which users can participate in communication between characters. For example, a character asks a quiz and the user answers it. The communication unit also introduces a mechanism that allows users to participate in conversations between characters through interactive events. For example, a user can pose questions to a character. The communication unit also holds interactive events in which users can participate, increasing user engagement through conversations between characters. For example, a character plays a game together with the user. In this way, user engagement is increased by holding interactive events in which users can participate.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The QR code reader can add a function that allows a character to be called by the user's voice. For example, when reading a QR code, voice recognition technology can be used to add a function that allows a user to call a character by speaking a specific keyword. For example, a character will appear when the user says "hello." The QR code reader can also add a function that allows a user to control the character's behavior using voice commands when reading a QR code. For example, if the user says "tell me the menu," the character will display the menu. The QR code reader can also use voice recognition technology to analyze the tone and emotion of the user's voice and display a character that corresponds to that. For example, if the user is excited, a lively character will appear. This allows a character to be called by the user's voice using voice recognition technology.

[0052] The QR code reader can use AR technology to make a character appear in real space. For example, when reading a QR code, AR technology is used to make a character appear in real space. For example, a character may be displayed as standing on a table using a smartphone camera. The QR code reader also uses AR technology to provide an interactive experience in which a character walks around the user. For example, the character may approach the user and talk to them. When reading a QR code, the QR code reader also uses AR technology to make the character interact with a real object. For example, the character may bring a menu. In this way, the user's experience is improved by using AR technology to make a character appear in real space.

[0053] The QR code reader can refer to the user's past visit history and display a special character according to the frequency of visits. For example, when reading a QR code, the user's past visit history can be referenced and a special character according to the frequency of visits can be displayed. For example, a character that gives a special greeting can appear for regular customers. The QR code reader can also display a character that offers special promotions to users who have visited multiple times within a specific period based on the user's visit history. For example, a character that provides information about special offers can appear on the third visit. The QR code reader can also use the user's past visit history to generate a character that displays a personalized message to the user. For example, a character that provides information related to a product purchased on the previous visit can appear. In this way, by referencing the user's past visit history and displaying a special character, user satisfaction can be improved.

[0054] The QR code reader can use the user's location information to automatically obtain information about the nearest store and display the most appropriate AI character. For example, when a user scans a QR code, the smartphone's GPS function is used to obtain the user's current location and automatically obtain information about the nearest store. For example, if the user scans a QR code inside a shopping mall, the most appropriate AI character is displayed based on the store information within the mall. The QR code reader also displays promotional information for nearby stores based on the user's location information. For example, if the user scans a QR code in front of a station, an AI character with special offer information for stores around the station will appear. The QR code reader also uses the user's location information to display characters limited to a specific area. For example, if the QR code is scanned at a tourist spot, a character providing tourist information for that area will appear. This improves user convenience by displaying the most appropriate AI character using the user's location information.

[0055] The character generation unit can use the user's past interaction data to generate a character with a personality that is optimal for the user. For example, the character generation unit analyzes the user's past interaction data to generate a character with a personality based on the user's preferences and interests. For example, the character's personality is determined based on information about stores the user frequently visits. The character generation unit also uses the past interaction data to generate a character with a speaking style and tone that the user prefers. For example, if the user prefers a relaxed atmosphere, the character generation unit creates a character that speaks calmly. The character generation unit also generates a character with a personality that matches a specific event or promotion based on the user's interaction data. For example, a character that suits a seasonal event is created. In this way, the user's past interaction data is used to generate a character with an optimal personality, thereby improving user satisfaction.

[0056] The character generation unit can generate characters with a seasonal feel by reflecting seasonal event information for the store. For example, a character with a seasonal feel is generated based on the seasonal event information for the store. For example, a Santa Claus character is created for the Christmas season. The character generation unit also changes the character's clothing and accessories by reflecting seasonal event information. For example, a character wearing a swimsuit is displayed in summer. The character generation unit also generates characters with personalities that match seasonal events. For example, a ghost character is created for Halloween and event information is provided to the user. In this way, the user's experience is improved by generating characters with a seasonal feel by reflecting seasonal event information.

[0057] The explanation unit can refer to the user's past purchase history and recommend optimal products and services to the user. For example, it analyzes the user's past purchase history and recommends optimal products and services based on the user's preferences and purchasing patterns. For example, it recommends new products related to products the user has previously purchased. The explanation unit also identifies products and services that the user is likely to be interested in based on the purchase history and makes personalized recommendations. For example, it prioritizes the recommendation of products in categories that the user frequently purchases. The explanation unit also uses the user's purchase history to recommend products and services tailored to specific events or promotions. For example, it recommends new products related to seasonal products that the user has previously purchased. In this way, the explanation unit can refer to the user's past purchase history and recommend optimal products and services, thereby improving user satisfaction.

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

[0059] Step 1: The QR code reader reads the QR code. For example, it scans the QR code using a smartphone camera and obtains its information. The QR code reader can also read static and dynamic QR codes. Static QR codes contain fixed information, while dynamic QR codes contain changeable information. Step 2: The character generation unit generates a character based on the information read by the QR code reader. For example, the generation AI inputs the store information into a large-scale language model (LLM, generation AI) to determine the character's personality and appearance. The character generation unit can also generate animated characters and 3D characters. Step 3: In the explanation section, the character generated by the character generation section explains the product or service. For example, the generation AI generates appropriate answers to questions from the user and explains the product or service. The explanation section can also provide text and audio explanations. Text explanations are displayed on the screen, and audio explanations are provided by the character speaking. Step 4: In the communication unit, the character generated by the character generation unit converses with the user. For example, the generation AI responds to the user's questions in real time and engages in a dialogue. The communication unit can also conduct voice and text dialogue. In voice dialogue, the character responds by voice when the user speaks, and in text dialogue, the character responds by text to the text entered by the user.

[0060] (Example 2) The AI ​​character system according to an embodiment of the present invention is a system in which an AI character corresponding to a store appears when a QR code is read. This AI character is created by inputting store information into a large-scale language model (LLM, generative AI). The character provides detailed explanations of recommended products and services and answers user questions, making it easier for users to understand and providing a highly satisfying experience. This allows the AI ​​character system to provide users with detailed information about products and services, providing a highly satisfying experience.

[0061] An AI character system according to an embodiment includes a QR code reader, a character generation unit, an explanation unit, and a communication unit. The QR code reader reads a QR code. For example, it scans a QR code using a smartphone camera and acquires the information. The QR code reader can read static and dynamic QR codes. For example, static QR codes have fixed information, while dynamic QR codes have changeable information. The character generation unit generates a character based on the information read by the QR code reader. For example, the generation AI inputs store information into a large-scale language model (LLM, generation AI) to determine the character's personality and appearance. The character generation unit can generate animated characters and 3D characters. For example, animated characters are dynamic characters, and 3D characters are three-dimensional characters. The explanation unit allows the character generated by the character generation unit to explain products and services. For example, the generation AI generates appropriate answers to user questions and explains products and services. The explanation unit can provide explanations using text or audio. For example, text explanations are displayed on the screen, and audio explanations are provided by a character speaking. The communication unit allows the character generated by the character generation unit to have a dialogue with the user. For example, the generation AI responds to the user's questions in real time and engages in a dialogue. The communication unit can also carry out voice dialogue and text dialogue. For example, in voice dialogue, the character responds by voice when the user speaks, and in text dialogue, the character responds by text to text entered by the user. This allows the AI ​​character system according to the embodiment to provide a user with detailed information about products and services, providing a highly satisfying experience.

[0062] The QR code reader can automatically obtain information about the nearest store using the user's location information and display the most appropriate AI character. For example, when a user scans a QR code, the QR code reader obtains the user's current location using the smartphone's GPS function and automatically obtains information about the nearest store. For example, if the user scans a QR code inside a shopping mall, the most appropriate AI character is displayed based on store information within the mall. The QR code reader also displays promotional information for nearby stores based on the user's location information. For example, if a user scans a QR code in front of a station, an AI character with special offer information for stores around the station appears. The QR code reader also uses the user's location information to display characters limited to a specific area. For example, if a QR code is scanned at a tourist spot, a character providing tourist information for that area appears. This improves user convenience by displaying the most appropriate AI character using the user's location information.

[0063] The QR code reader can refer to the user's past visit history and display a special character according to the frequency of visits. For example, when reading a QR code, the QR code reader refers to the user's past visit history and displays a special character according to the frequency of visits. For example, a character that gives a special greeting appears for regular customers. The QR code reader also displays a character that offers special promotions to users who have visited multiple times within a specific period based on the user's visit history. For example, a character that informs users of special benefits appears on the third visit. The QR code reader also uses the past visit history to generate a character that displays a personalized message to the user. For example, a character that provides information related to a product purchased on the previous visit appears. In this way, by referencing the user's past visit history and displaying a special character, user satisfaction is improved.

[0064] The QR code reader uses an emotion estimation function to display a character that corresponds to the user's current emotional state and can respond according to the user's mood. When reading a QR code, the QR code reader, for example, uses a smartphone camera to analyze the user's facial expression and estimate the user's emotional state. For example, if the user is smiling, a bright and cheerful character appears. The QR code reader also uses the emotion estimation function to analyze the user's voice tone and display a character that corresponds to the user's emotional state. For example, if the user is tired, a relaxing character appears. The QR code reader also dynamically changes the character's response based on the user's emotional state. For example, if the user is sad, a character that sends an encouraging message appears. In this way, displaying a character that corresponds to the user's emotional state improves user satisfaction.

[0065] The QR code reader can use voice recognition technology to add a function that allows a character to be called by the user's voice. For example, when reading a QR code, the QR code reader can add a function that allows a character to be called by the user uttering a specific keyword using voice recognition technology. For example, a character will appear when the user says "hello." The QR code reader can also add a function that allows a user to control the character's behavior using voice commands when reading a QR code. For example, when the user says "tell me the menu," the character will display the menu. The QR code reader can also use voice recognition technology to analyze the tone and emotion of the user's voice and display a character that corresponds to that. For example, if the user is excited, a cheerful character will appear. This allows a character to be called by the user's voice using voice recognition technology.

[0066] The QR code reader can use AR technology to make a character appear in real space. For example, when reading a QR code, the QR code reader uses AR technology to make a character appear in real space. For example, a character may be displayed as standing on a table using a smartphone camera. The QR code reader also uses AR technology to provide an interactive experience in which a character walks around the user. For example, the character may approach the user and talk to them. When reading a QR code, the QR code reader also uses AR technology to make the character interact with a real object. For example, the character may bring a menu. This improves the user's experience by making the character appear in real space using AR technology.

[0067] The QR code reader can use its emotion estimation function to analyze the emotion a user feels when scanning a QR code and display a character designed to elicit positive emotions. For example, when scanning a QR code, the QR code reader analyzes the user's facial expressions and voice to estimate their emotional state. For example, if the user is tired, a character designed to relax them will appear. The QR code reader can also use its emotion estimation function to display a character according to the user's emotional state and take measures to elicit positive emotions. For example, if the user is sad, a character sending an encouraging message will appear. The QR code reader can also monitor the user's emotional state in real time and display character actions and messages that will change the user's emotion to a positive one. For example, a character telling a joke that will make the user smile will appear. This improves user satisfaction by analyzing the user's emotions and displaying a character designed to elicit positive emotions.

[0068] The character generation unit can use the user's past interaction data to generate a character with a personality that is optimal for the user. For example, the character generation unit analyzes the user's past interaction data to generate a character with a personality based on the user's preferences and interests. For example, the character's personality is determined based on information about stores the user frequently visits. The character generation unit also uses the past interaction data to generate a character with a speaking style and tone that the user prefers. For example, if the user prefers a relaxed atmosphere, the character generation unit creates a character that speaks calmly. The character generation unit also generates a character with a personality that matches a specific event or promotion based on the user's interaction data. For example, a character that matches a seasonal event is created. In this way, the user's past interaction data is used to generate a character with an optimal personality, thereby improving user satisfaction.

[0069] The character generation unit can generate characters with a seasonal feel by reflecting seasonal event information for the store. The character generation unit generates characters with a seasonal feel based on, for example, seasonal event information for the store. For example, a Santa Claus character is created for the Christmas season. The character generation unit also changes the character's clothing and accessories by reflecting seasonal event information. For example, a character wearing a swimsuit is displayed in summer. The character generation unit also generates characters with personalities that match seasonal events. For example, a ghost character is created for Halloween and event information is provided to the user. In this way, the user's experience is improved by generating characters with a seasonal feel by reflecting seasonal event information.

[0070] The character generation unit uses the emotion estimation function to generate a character with a personality that corresponds to the user's emotion and can respond in a way that is sensitive to the user's emotion. The character generation unit, for example, uses the emotion estimation function to analyze the user's emotional state and generate a character with a personality that corresponds to the user's emotion. For example, if the user is tired, a character with a relaxing personality is created. The character generation unit also generates a character with a personality that corresponds to the user's emotion and responds in a way that is sensitive to the user's emotion. For example, if the user is sad, a character that sends an encouraging message is created. The character generation unit also uses the emotion estimation function to dynamically change the character's personality based on the user's emotional state. For example, a character that tells jokes that make the user smile is created. In this way, a character with a personality that corresponds to the user's emotion and responds in a way that is sensitive to the user's emotion is generated, thereby improving user satisfaction.

[0071] The character generation unit can refer to the user's SNS account information and generate a character with a personality based on the user's interests and concerns. For example, the character generation unit analyzes the user's SNS account information and generates a character with a personality based on the user's interests and concerns. For example, if the user likes music, it creates a character that is knowledgeable about music. The character generation unit also analyzes the accounts the user follows and the content of their posts based on the SNS account information and generates a character with a personality corresponding to that. For example, if the user likes traveling, it creates a character that provides information about travel. The character generation unit also uses the user's SNS account information to generate a character with a personality tailored to a specific event or promotion. For example, it creates a character related to an event the user plans to attend. In this way, by referencing the user's SNS account information and generating a character with a personality based on the user's interests and concerns, user satisfaction is improved.

[0072] The character generation unit can generate a personalized character according to the age and gender of the user. The character generation unit generates a character with a personalized personality based on, for example, the user's age and gender. For example, a bright and energetic character is created for children, and a calm character is created for adults. The character generation unit also customizes the character's appearance and speaking style according to age and gender. For example, a character dressed in casual clothing is created for young people, and a character dressed in formal clothing for elderly people. The character generation unit also generates a character with a personality according to specific interests and concerns based on the user's age and gender. For example, a character knowledgeable about beauty and fashion is created for women. In this way, user satisfaction is improved by generating a personalized character according to the user's age and gender.

[0073] The character generation unit can use the emotion estimation function to analyze how the user feels about the character's personality and generate a character with an optimal personality. The character generation unit, for example, uses the emotion estimation function to analyze in real time how the user feels about the character's personality. For example, it generates a character with a personality that evokes positive emotions from the user. The character generation unit also builds a system that dynamically changes the character's personality based on the user's emotional response data. For example, if the user feels negative emotions, it changes the personality to elicit positive emotions. The character generation unit also generates a character with the user's most preferred personality based on the emotion estimation data. For example, if the user wants to relax, it creates a character with a calm personality. In this way, the user's emotions about the character's personality are analyzed and a character with an optimal personality is generated, thereby improving user satisfaction.

[0074] The explanation unit can refer to the user's past purchase history and recommend optimal products and services to the user. For example, the explanation unit analyzes the user's past purchase history and recommends optimal products and services based on the user's preferences and purchasing patterns. For example, it recommends new products related to products the user has previously purchased. The explanation unit also identifies products and services that the user is likely to be interested in based on the purchase history and makes personalized recommendations. For example, it prioritizes the recommendation of products in categories that the user frequently purchases. The explanation unit also uses the user's purchase history to recommend products and services tailored to specific events or promotions. For example, it recommends new products related to seasonal products that the user has previously purchased. In this way, the explanation unit can improve user satisfaction by referring to the user's past purchase history and recommending optimal products and services.

[0075] The explanation unit can collect real-time user feedback and dynamically adjust the content of the explanation based on that feedback. For example, while explaining a product or service, the explanation unit collects real-time user feedback and dynamically adjusts the content of the explanation based on that feedback. For example, the explanation unit provides detailed explanations of parts that the user is interested in. The explanation unit also analyzes user feedback in real time and builds a system that customizes the content of the explanation. For example, the explanation unit re-explains parts that the user finds difficult to understand. The explanation unit also provides explanations of products or services based on the user's interests and concerns based on the real-time feedback. For example, if the user is interested in a particular function, the explanation unit provides detailed information about that function. In this way, the explanation unit collects real-time user feedback and dynamically adjusts the content of the explanation, thereby deepening the user's understanding.

[0076] The explanation unit uses the emotion estimation function to provide an explanation according to the user's emotion, thereby deepening the user's understanding. The explanation unit, for example, uses the emotion estimation function to analyze the user's emotional state and provide an explanation according to that. For example, if the user is excited, a detailed explanation is provided. The explanation unit also provides an explanation according to the user's emotion, thereby building a system that deepens understanding. For example, if the user is confused, a concise and easy-to-understand explanation is provided. The explanation unit also provides an explanation that matches the user's emotional state based on the emotion estimation data. For example, if the user is relaxed, the explanation is provided in a relaxed tone. In this way, the explanation according to the user's emotion deepens the user's understanding.

[0077] The explanation unit can add visual explanations using videos and images to promote user understanding. The explanation unit adds visual explanations using videos and images when explaining products or services, for example. For example, it displays a video showing how to use the product. The explanation unit also uses visual explanations to build a system that promotes user understanding. For example, it shows the features of the product using images. The explanation unit also adds explanations using videos and images to make it easier for users to understand visually. For example, it displays an image showing the internal structure of the product. In this way, adding visual explanations using videos and images promotes user understanding.

[0078] The explanation unit can provide multilingual support in accordance with the user's language setting. The explanation unit provides multilingual support in accordance with the user's language setting, for example, when explaining products or services. For example, explanations are provided in multiple languages, such as English, French, and Chinese. The explanation unit also builds a system that provides product or service explanations in an appropriate language based on the user's language setting. For example, if the user selects Japanese, explanations are provided in Japanese. The explanation unit also provides multilingual support and provides product or service explanations in a language that the user can easily understand. For example, if the user selects Spanish, explanations are provided in Spanish. This provides multilingual support in accordance with the user's language setting, thereby deepening the user's understanding.

[0079] The explanation unit can use the emotion estimation function to analyze the emotions of the user when receiving an explanation and provide an explanation that elicits positive emotions. The explanation unit, for example, uses the emotion estimation function to analyze the emotions of the user when receiving an explanation in real time. For example, if the user is excited, the explanation unit provides a detailed explanation. The explanation unit also provides an explanation according to the user's emotional state, building a system that elicits positive emotions. For example, if the user is confused, the explanation unit provides a concise and easy-to-understand explanation. The explanation unit also provides an explanation that matches the user's emotional state based on the emotion estimation data. For example, if the user is relaxed, the explanation is provided in a relaxed tone. In this way, the user's emotions are analyzed and an explanation that elicits positive emotions is provided, thereby deepening the user's understanding.

[0080] The communication unit can refer to the user's past dialogue history and provide a more personalized response. For example, the communication unit analyzes the user's past dialogue history and provides a personalized response based on the user's preferences and interests. For example, it provides information related to topics the user has previously discussed. The communication unit also generates a character with a speaking style and tone that the user prefers based on the past dialogue history. For example, if the user prefers a relaxed atmosphere, it creates a character that speaks calmly. The communication unit also uses the user's dialogue history to provide a personalized response tailored to specific events or promotions. For example, it provides information tailored to seasonal events. In this way, by referring to the user's past dialogue history and providing a personalized response, user satisfaction is improved.

[0081] The communication unit can provide special content based on the user's preferences and interests. For example, the communication unit analyzes the user's preferences and interests and provides special content based on them. For example, if the user likes music, it provides information about music. The communication unit also builds a system that provides special content based on the user's interests. For example, if the user likes traveling, it provides information about travel. The communication unit also provides special content according to the user's preferences and interests, improving user satisfaction. For example, if the user likes cooking, it provides information about cooking. In this way, by providing special content based on the user's preferences and interests, user satisfaction is improved.

[0082] The communication unit uses the emotion estimation function to communicate according to the user's emotions, thereby improving user satisfaction. The communication unit, for example, uses the emotion estimation function to analyze the user's emotional state and communicate accordingly. For example, if the user is excited, the communication unit provides a detailed explanation. The communication unit also builds a system that communicates according to the user's emotions and improves satisfaction. For example, if the user is confused, the communication unit provides a concise and easy-to-understand explanation. The communication unit also communicates according to the user's emotional state based on the emotion estimation data. For example, if the user is relaxed, the communication unit provides an explanation in a relaxed tone. In this way, communication according to the user's emotions improves user satisfaction.

[0083] The communication unit can add a voice dialogue function to achieve more natural communication. For example, the communication unit can add a voice dialogue function to communication with a user's individual AI character to achieve more natural communication. For example, when the user speaks, the character responds by voice. The communication unit also uses the voice dialogue function to analyze the user's tone of voice and emotions and responds accordingly. For example, if the user is excited, the communication unit provides a detailed explanation. The communication unit also builds a system that provides personalized responses based on the user's voice dialogue data. For example, if the user wants to relax, the communication unit responds in a calm tone. In this way, adding the voice dialogue function achieves more natural communication.

[0084] The communication unit can incorporate game elements to increase user engagement. For example, the communication unit can incorporate game elements into communication with each user's individual AI character to increase user engagement. For example, a system can be introduced that allows points to be earned through dialogue with the character. The communication unit can also use game elements to build a system that allows users to communicate with characters while having fun. For example, users can interact with characters through quizzes or mini-games. The communication unit can also introduce rewards and perks for communication with characters to increase user engagement. For example, a system can be introduced that allows users to receive perks when they complete certain missions. In this way, incorporating game elements increases user engagement.

[0085] The communication unit can use the emotion estimation function to analyze the emotions of the user when communicating and take measures to elicit positive emotions. The communication unit, for example, uses the emotion estimation function to analyze the emotions of the user when communicating in real time. For example, if the user is excited, the communication unit provides a detailed explanation. The communication unit also takes measures according to the user's emotional state and builds a system that elicits positive emotions. For example, if the user is confused, the communication unit provides a concise and easy-to-understand explanation. The communication unit also takes measures based on the emotion estimation data that are tailored to the user's emotional state. For example, if the user is relaxed, the communication unit provides explanations in a relaxed tone. In this way, the user's emotions are analyzed and measures are taken to elicit positive emotions, thereby improving user satisfaction.

[0086] The communication unit can refer to the user's past interaction data in communication between characters and provide the user with optimal information. For example, the communication unit analyzes the user's past interaction data in communication between characters and provides the user with optimal information. For example, the communication unit provides information related to topics in which the user has shown interest in the past. The communication unit also generates conversations between characters with topics and tones that the user prefers based on the past interaction data. For example, if the user prefers a relaxed atmosphere, the communication unit creates characters that have calm conversations. The communication unit also uses the user's interaction data to generate conversations between characters that provide information tailored to specific events or promotions. For example, the communication unit provides information tailored to seasonal events. In this way, the communication unit can refer to the user's past interaction data and provide optimal information, thereby improving user satisfaction.

[0087] The communication unit can reflect the latest store information and event information in real time in communication between characters. The communication unit, for example, builds a system that reflects the latest store information and event information in real time in communication between characters. For example, information about new products is introduced in conversation between characters. The communication unit also generates conversations between characters based on store information that is updated in real time. For example, characters communicate information about events that are currently being held in conversation. The communication unit also reflects the latest store information and event information in real time and generates conversations between characters that provide new information to users. For example, characters communicate information about special sales in conversation. In this way, the latest store information and event information is reflected in real time, providing users with the latest information.

[0088] The communication unit uses the emotion estimation function to generate conversations between characters that correspond to the user's emotions, thereby attracting the user's interest. The communication unit, for example, uses the emotion estimation function to analyze the user's emotional state and generate conversations between characters that correspond to the user's emotions. For example, if the user is excited, a conversation that provides detailed information is generated. The communication unit also generates conversations between characters that correspond to the user's emotions, building a system that attracts the user's interest. For example, if the user is confused, a concise and easy-to-understand conversation is generated. The communication unit also generates conversations between characters that match the user's emotional state based on the emotion estimation data. For example, if the user is relaxed, a character that speaks in a relaxed tone is generated. In this way, conversations between characters that correspond to the user's emotions are generated, attracting the user's interest.

[0089] The communication unit allows characters to share information between different stores in communication with each other, thereby providing users with new discoveries. The communication unit, for example, builds a system for sharing information between different stores in communication between characters. For example, characters communicate promotional information about adjacent stores in conversation. The communication unit also generates conversations between characters that share information about different stores and provide users with new discoveries. For example, characters introduce information about stores that the user has never visited in conversation. The communication unit also shares information between stores and generates conversations between characters that provide users with new information. For example, characters communicate information about special offers at affiliated stores in conversation. In this way, information is shared between different stores, providing users with new discoveries.

[0090] The communication unit can hold interactive events in which users can participate in communication between characters. The communication unit, for example, builds a system for holding interactive events in which users can participate in communication between characters. For example, a character asks a quiz and the user answers it. The communication unit also introduces a mechanism that allows users to participate in conversations between characters through interactive events. For example, a user can pose questions to a character. The communication unit also holds interactive events in which users can participate, increasing user engagement through conversations between characters. For example, a character plays a game together with the user. In this way, user engagement is increased by holding interactive events in which users can participate.

[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 QR code reader can add a function that allows a character to be called by the user's voice. For example, when reading a QR code, voice recognition technology can be used to add a function that allows a user to call a character by speaking a specific keyword. For example, a character will appear when the user says "hello." The QR code reader can also add a function that allows a user to control the character's behavior using voice commands when reading a QR code. For example, if the user says "tell me the menu," the character will display the menu. The QR code reader can also use voice recognition technology to analyze the tone and emotion of the user's voice and display a character that corresponds to that. For example, if the user is excited, a lively character will appear. This allows a character to be called by the user's voice using voice recognition technology.

[0093] The QR code reader can use AR technology to make a character appear in real space. For example, when reading a QR code, AR technology is used to make a character appear in real space. For example, a character may be displayed as standing on a table using a smartphone camera. The QR code reader also uses AR technology to provide an interactive experience in which a character walks around the user. For example, the character may approach the user and talk to them. When reading a QR code, the QR code reader also uses AR technology to make the character interact with a real object. For example, the character may bring a menu. In this way, the user's experience is improved by using AR technology to make a character appear in real space.

[0094] The QR code reader uses an emotion estimation function to display a character that corresponds to the user's current emotional state and can respond according to the user's mood. For example, when reading a QR code, the smartphone camera is used to analyze the user's facial expression and estimate the user's emotional state. For example, if the user is smiling, a bright and cheerful character appears. The QR code reader also uses the emotion estimation function to analyze the user's voice tone and display a character that corresponds to the user's emotional state. For example, if the user is tired, a relaxing character appears. The QR code reader also dynamically changes the character's response based on the user's emotional state. For example, if the user is sad, a character that sends an encouraging message appears. In this way, displaying a character that corresponds to the user's emotional state improves user satisfaction.

[0095] The QR code reader can refer to the user's past visit history and display a special character according to the frequency of visits. For example, when reading a QR code, the user's past visit history can be referenced and a special character according to the frequency of visits can be displayed. For example, a character that gives a special greeting can appear for regular customers. The QR code reader can also display a character that offers special promotions to users who have visited multiple times within a specific period based on the user's visit history. For example, a character that provides information about special offers can appear on the third visit. The QR code reader can also use the user's past visit history to generate a character that displays a personalized message to the user. For example, a character that provides information related to a product purchased on the previous visit can appear. In this way, by referencing the user's past visit history and displaying a special character, user satisfaction can be improved.

[0096] The QR code reader can use the user's location information to automatically obtain information about the nearest store and display the most appropriate AI character. For example, when a user scans a QR code, the smartphone's GPS function is used to obtain the user's current location and automatically obtain information about the nearest store. For example, if the user scans a QR code inside a shopping mall, the most appropriate AI character is displayed based on the store information within the mall. The QR code reader also displays promotional information for nearby stores based on the user's location information. For example, if the user scans a QR code in front of a station, an AI character with special offer information for stores around the station will appear. The QR code reader also uses the user's location information to display characters limited to a specific area. For example, if the QR code is scanned at a tourist spot, a character providing tourist information for that area will appear. This improves user convenience by displaying the most appropriate AI character using the user's location information.

[0097] The character generation unit uses the emotion estimation function to generate a character with a personality that corresponds to the user's emotions and can respond in a way that is sensitive to the user's emotions. For example, the emotion estimation function is used to analyze the user's emotional state and generate a character with a personality that corresponds to that state. For example, if the user is tired, a character with a relaxing personality is created. The character generation unit also generates a character with a personality that corresponds to the user's emotions and responds in a way that is sensitive to the user's emotions. For example, if the user is sad, a character that sends an encouraging message is created. The character generation unit also uses the emotion estimation function to dynamically change the character's personality based on the user's emotional state. For example, a character that tells jokes that make the user smile is created. In this way, a character with a personality that corresponds to the user's emotions and responds in a way that is sensitive to the user's emotions improves user satisfaction.

[0098] The character generation unit can use the user's past interaction data to generate a character with a personality that is optimal for the user. For example, the character generation unit analyzes the user's past interaction data to generate a character with a personality based on the user's preferences and interests. For example, the character's personality is determined based on information about stores the user frequently visits. The character generation unit also uses the past interaction data to generate a character with a speaking style and tone that the user prefers. For example, if the user prefers a relaxed atmosphere, the character generation unit creates a character that speaks calmly. The character generation unit also generates a character with a personality that matches a specific event or promotion based on the user's interaction data. For example, a character that suits a seasonal event is created. In this way, the user's past interaction data is used to generate a character with an optimal personality, thereby improving user satisfaction.

[0099] The character generation unit can generate characters with a seasonal feel by reflecting seasonal event information for the store. For example, a character with a seasonal feel is generated based on the seasonal event information for the store. For example, a Santa Claus character is created for the Christmas season. The character generation unit also changes the character's clothing and accessories by reflecting seasonal event information. For example, a character wearing a swimsuit is displayed in summer. The character generation unit also generates characters with personalities that match seasonal events. For example, a ghost character is created for Halloween and event information is provided to the user. In this way, the user's experience is improved by generating characters with a seasonal feel by reflecting seasonal event information.

[0100] The explanation unit can refer to the user's past purchase history and recommend optimal products and services to the user. For example, it analyzes the user's past purchase history and recommends optimal products and services based on the user's preferences and purchasing patterns. For example, it recommends new products related to products the user has previously purchased. The explanation unit also identifies products and services that the user is likely to be interested in based on the purchase history and makes personalized recommendations. For example, it prioritizes the recommendation of products in categories that the user frequently purchases. The explanation unit also uses the user's purchase history to recommend products and services tailored to specific events or promotions. For example, it recommends new products related to seasonal products that the user has previously purchased. In this way, the explanation unit can refer to the user's past purchase history and recommend optimal products and services, thereby improving user satisfaction.

[0101] The explanation unit uses the emotion estimation function to provide an explanation according to the user's emotion, thereby deepening the user's understanding. For example, the emotion estimation function is used to analyze the user's emotional state and provide an explanation according to that. For example, if the user is excited, a detailed explanation is provided. The explanation unit also provides an explanation according to the user's emotion, thereby building a system that deepens understanding. For example, if the user is confused, a concise and easy-to-understand explanation is provided. The explanation unit also provides an explanation that matches the user's emotional state based on the emotion estimation data. For example, if the user is relaxed, the explanation is provided in a relaxed tone. In this way, the explanation that matches the user's emotion deepens the user's understanding.

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

[0103] Step 1: The QR code reader reads the QR code. For example, it scans the QR code using a smartphone camera and obtains its information. The QR code reader can also read static and dynamic QR codes. Static QR codes contain fixed information, while dynamic QR codes contain changeable information. Step 2: The character generation unit generates a character based on the information read by the QR code reader. For example, the generation AI inputs the store information into a large-scale language model (LLM, generation AI) to determine the character's personality and appearance. The character generation unit can also generate animated characters and 3D characters. Step 3: In the explanation section, the character generated by the character generation section explains the product or service. For example, the generation AI generates appropriate answers to questions from the user and explains the product or service. The explanation section can also provide text and audio explanations. Text explanations are displayed on the screen, and audio explanations are provided by the character speaking. Step 4: In the communication unit, the character generated by the character generation unit converses with the user. For example, the generation AI responds to the user's questions in real time and engages in a dialogue. The communication unit can also conduct voice and text dialogue. In voice dialogue, the character responds by voice when the user speaks, and in text dialogue, the character responds by text to the text entered 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 (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, 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 robot 414 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 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 QR code reader that reads a QR code; a character generation unit that generates a character based on the information read by the QR code reading unit; an explanation unit in which the character generated by the character generation unit explains products and services; a communication unit in which the character generated by the character generation unit interacts with a user. A system characterized by:

2. The QR code reader is Using the user's location information, the app automatically retrieves information about the nearest store and displays the most suitable AI character.

2. The system of claim 1.

3. The QR code reader is Refer to the user's past visit history and display special characters according to the frequency of visits 2. The system of claim 1.

4. The QR code reader is Displaying a character that corresponds to the user's current emotional state and responding in accordance with the user's mood 2. The system of claim 1.

5. The QR code reader is Add a feature that uses voice recognition technology to call characters using the user's voice.

2. The system of claim 1.

6. The QR code reader is Using AR technology, characters appear in real space.

2. The system of claim 1.

7. The QR code reader is Analyzes the user's emotions when scanning a QR code and displays a character to elicit positive emotions.

2. The system of claim 1.

8. The character generation unit Using the user's past interaction data, we generate a character with the personality that best suits the user.

2. The system of claim 1.

Images