System

Abstract

An object of a system according to an embodiment is to generate an appropriate recommendation on the basis of an input of a user and efficiently guide the user to a service of a group of the user.SOLUTION: A system includes a user input unit, an analysis unit, a recommendation unit, and a guide unit. The user input unit receives an input from a user. The analysis unit analyzes the input received by the user input unit. The recommendation unit generates an appropriate recommendation based on the content analyzed by the analysis unit. The guidance unit performs guidance to the service of the own company group on the basis of the recommendation generated by the recommendation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to generate appropriate recommendations based on user input and efficiently guide users to their own group's services.

[0005] The system according to the embodiment aims to generate appropriate recommendations based on user input and efficiently guide users to services provided by the company's group. [Means for solving the problem]

[0006] The system according to the embodiment includes a user input unit, an analysis unit, a recommendation unit, and a guidance unit. The user input unit accepts user input. The analysis unit analyzes the input accepted by the user input unit. The recommendation unit generates appropriate recommendations based on the content analyzed by the analysis unit. The guidance unit guides users to services of the company's group based on the recommendations generated by the recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment generates appropriate recommendations based on user input, and can efficiently guide users to services provided by the company's group. [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 LINE Concierge system according to an embodiment of the present invention is a system in which, when a user inputs what they want to do or what they want to know, a generation AI analyzes the content and provides appropriate answers and recommendations. As a result, the LINE Concierge system can provide appropriate answers and recommendations based on the user's input, and guide the user to services within the company's group.

[0029] The LINE concierge system according to the embodiment includes a user input unit, an analysis unit, a recommendation unit, and a guidance unit. The user input unit accepts user input. For example, it accepts text input. The user input unit can also accept voice input. For example, it converts voice into text using voice recognition technology. The analysis unit analyzes the input accepted by the user input unit. For example, it analyzes the input content using natural language processing technology. The analysis unit can also analyze the input content using a machine learning algorithm. For example, it learns the user's past dialogue history and generates responses optimized for each individual user. The recommendation unit generates appropriate recommendations based on the content analyzed by the analysis unit. For example, it generates recommendations based on the user's past behavioral data. The recommendation unit can also generate recommendations based on behavioral data of similar users. For example, if a user enters "I want to buy a new smartphone," it suggests related products on Yahoo! Shopping. The guidance unit guides the user to services within the company group based on the recommendations generated by the recommendation unit. For example, it presents links. The guidance unit can also send notifications. For example, if a user inputs "I want to see a movie," a link to purchase movie tickets on Yahoo Shopping is provided. This allows the LINE Concierge system according to the embodiment to generate appropriate recommendations based on the user's input and guide the user to services of its own group.

[0030] The analysis unit can learn the user's past interaction history and generate responses optimized for the user. For example, the analysis unit analyzes the user's past interaction history and learns the user's preferences and interests. For example, for a user who has previously requested restaurant recommendations, restaurants based on the user's preferred cuisine genres are suggested. The analysis unit also generates personalized responses based on the user's past interaction history. For example, for a user who has previously requested information on travel destinations, the analysis unit provides the latest information on areas that the user previously showed interest in. The analysis unit also learns the user's interaction history and generates responses based on the user's behavioral patterns. For example, for a user who frequently requests movie information, the analysis unit provides the latest movie information and ticket purchase links. In this way, the analysis unit can learn the user's past interaction history and generate responses optimized for each individual user.

[0031] The analysis unit can use the user's location information to provide real-time information based on the current location. For example, the analysis unit acquires the user's location information and provides information on restaurants and cafes based on the current location. For example, if the user inputs, "Tell me about nearby cafes," cafes near the current location are suggested. The analysis unit also uses the user's location information to provide real-time traffic information and weather information. For example, if the user inputs, "What's the weather like now?", weather information for the current location is displayed. The analysis unit also provides information on nearby events and tourist spots based on the user's location information. For example, if the user inputs, "Are there any events happening nearby?", event information for the area around the current location is suggested. In this way, the user's location information can be used to provide real-time information based on the current location.

[0032] The user input section supports voice input and can also respond to questions and requests made through voice input. For example, the user input section analyzes voice input and generates a text response to a question or request made in voice. For example, when the user inputs "Tell me a recommended restaurant" in voice, it proposes a restaurant in text. Also, the user input section uses voice recognition technology to convert voice input into text and generates a response based on that text. For example, when the user inputs "Tell me a nearby café" in voice, it provides information about the café in text. Additionally, the user input section supports voice input and generates a voice response to a question or request made in voice. For example, when the user inputs "What's the current weather?" in voice, it provides weather information in voice. This enables it to support voice input and also respond to questions and requests made in voice.

[0033] The user input section supports multiple languages and can also provide services to users who speak different languages. For example, the user input section supports multiple languages and generates a response according to the language input by the user. For example, when the user inputs "Recommend a restaurant" in English, it proposes a restaurant in English. Also, the user input section uses automatic translation technology to handle questions and requests made in different languages. For example, when the user inputs "推荐一个餐?" in Chinese, it proposes a restaurant in Chinese. Additionally, the user input section supports multiple languages and provides personalized responses to users who speak different languages. For example, when the user inputs "Recomienda un restaurante" in Spanish, it proposes a restaurant in Spanish. This enables it to support multiple languages and also provide services to users who speak different languages.

[0034] When analyzing a user's input, the generation AI can refer to a related external database to improve the accuracy of the answer. For example, when analyzing a user's input, the generation AI refers to related news articles to generate an answer. For example, if a user inputs, "Tell me the latest technology news," an answer based on the latest news articles is provided. The generation AI also refers to an expert knowledge base to generate a highly accurate answer to the user's question. For example, if a user inputs, "Tell me about the latest AI technology," an answer is provided based on the expert knowledge base. The generation AI also refers to an external database to generate a detailed answer to the user's input. For example, if a user inputs, "Tell me about the weather this weekend," an answer is provided based on a weather forecast database. This allows the generation AI to refer to a related external database to improve the accuracy of the answer when analyzing a user's input.

[0035] Generative AI can generate more personalized answers by taking into account a user's past behavioral patterns. For example, generative AI analyzes a user's past behavioral patterns to generate personalized answers. For example, a user who has previously requested information on travel destinations can be provided with the latest information on areas that the user previously showed interest in. Generative AI also generates individualized responses based on a user's past behavioral patterns. For example, a user who frequently requests restaurant information can be suggested restaurants based on the user's preferred cuisine genres. Generative AI also takes into account a user's past behavioral patterns to generate answers that meet the user's needs. For example, a user who has previously requested movie information can be provided with the latest movie information and ticket purchase links. This makes it possible to generate more personalized answers by taking into account a user's past behavioral patterns.

[0036] When analyzing user input, the generation AI can also include at least one multimedia data, such as images or videos, in its analysis. For example, when analyzing user input, the generation AI can also include image data in its analysis. For example, if a user inputs, "Tell me the location of this image," the generation AI can perform image analysis to identify the location. The generation AI can also include video data in its analysis to generate an answer to the user's question. For example, if a user inputs, "Tell me the restaurant in this video," the generation AI can perform video analysis to identify the restaurant. The generation AI can also analyze multimedia data to generate a detailed answer to the user's input. For example, if a user inputs, "Tell me the building in this image," the generation AI can perform image analysis to identify the building. This allows the generation AI to also include multimedia data, such as images and videos, in its analysis when analyzing user input.

[0037] When analyzing user input, the generative AI can refer to the opinions of experts in different fields to generate answers. For example, the generative AI may refer to the opinions of experts in different fields to generate highly accurate answers to user questions. For example, if a user inputs, "Tell me about the latest AI technology," the generative AI will provide an answer based on the opinions of experts. The generative AI may also refer to the opinions of experts to generate detailed answers to the user's input. For example, if a user inputs, "Tell me about the latest medical technology," the generative AI will provide an answer based on the opinions of medical experts. The generative AI may also refer to the opinions of experts in different fields to generate personalized answers to user questions. For example, if a user inputs, "Tell me about the latest environmental technology," the generative AI will provide an answer based on the opinions of environmental experts. This makes it possible to generate answers by referring to the opinions of experts in different fields when analyzing user input.

[0038] The generation AI can learn from a user's past purchase history and browsing history to make more accurate recommendations. For example, the generation AI can analyze a user's past purchase history and make recommendations based on the user's preferences and interests. For example, it can suggest new products related to products previously purchased. The generation AI can also make personalized recommendations based on the user's browsing history. For example, it can suggest items related to products previously viewed. The generation AI can also integrate a user's purchase history and browsing history to make more accurate recommendations. For example, it can suggest accessories or complementary products related to products previously purchased. This allows the generation AI to learn from a user's past purchase history and browsing history and make more accurate recommendations.

[0039] Generative AI can analyze a user's social media activity and make recommendations based on the user's interests. For example, generative AI can analyze the content of a user's social media posts and make recommendations based on the user's interests. For example, it can suggest products and services that the user is talking about on social media. Generative AI can also make personalized recommendations based on the user's social media following and like history. For example, it can suggest products related to brands and accounts the user follows. Generative AI can also analyze a user's social media activity and make recommendations based on the user's interests. For example, it can provide information related to events and groups the user is participating in. This makes it possible to analyze a user's social media activity and make recommendations based on the user's interests.

[0040] When making recommendations to users, the generation AI can integrate information from different platforms. For example, the generation AI integrates information from different platforms to make recommendations to users. For example, it can suggest products and services based on social media posts and blog articles. The generation AI can also analyze users' social media and blog activity to make personalized recommendations. For example, it can suggest products that users are talking about on social media or services that they are introducing on blogs. The generation AI can also integrate information from different platforms to make recommendations based on the user's interests and concerns. For example, it can suggest products related to social media accounts and blogs that the user follows. This makes it possible to integrate information from different platforms to make recommendations.

[0041] When making recommendations for a user, the generation AI can take into account the opinions of the user's friends and family. For example, the generation AI analyzes the opinions of the user's friends and family to make recommendations for the user. For example, it can suggest products and services that the user's friends recommend on social media. The generation AI also makes personalized recommendations based on the purchase and browsing history of the user's friends and family. For example, it can suggest items related to products purchased by the user's family. The generation AI also takes into account the opinions of the user's friends and family to make recommendations based on the user's interests. For example, it can provide information related to events and groups in which the user's friends are participating. This allows the generation AI to make recommendations based on the opinions of the user's friends and family.

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

[0043] The user input unit accepts user input. For example, it accepts text input. The user input unit can also accept voice input. For example, it converts voice into text using voice recognition technology. The analysis unit analyzes the input accepted by the user input unit. For example, it analyzes the input content using natural language processing technology. The analysis unit can also analyze the input content using a machine learning algorithm. For example, it learns the user's past dialogue history and generates responses optimized for each individual user. The recommendation unit generates appropriate recommendations based on the content analyzed by the analysis unit. For example, it generates recommendations based on the user's past behavioral data. The recommendation unit can also generate recommendations based on behavioral data of similar users. For example, if a user enters "I want to buy a new smartphone," it suggests related products on Yahoo! Shopping. The guidance unit guides the user to services of the company group based on the recommendations generated by the recommendation unit. For example, it presents links. The guidance unit can also send notifications. For example, if a user inputs "I want to see a movie," a link to purchase movie tickets on Yahoo Shopping is provided. This allows the LINE Concierge system according to the embodiment to generate appropriate recommendations based on the user's input and guide the user to services of the company's group.

[0044] The analysis unit can learn the user's past interaction history and generate responses optimized for the user. For example, if a user has previously requested restaurant recommendations, restaurants based on the user's preferred cuisine genre will be suggested. The analysis unit also generates personalized responses based on the user's past interaction history. For example, if a user has previously requested information on travel destinations, the analysis unit will provide the user with the latest information on areas in which the user previously showed interest. The analysis unit also learns the user's interaction history and generates responses based on the user's behavioral patterns. For example, if a user frequently requests movie information, the analysis unit will provide the user with the latest movie information and ticket purchase links. This makes it possible to learn the user's past interaction history and generate responses optimized for each individual user.

[0045] The analysis unit can use the user's location information to provide real-time information based on the current location. For example, it acquires the user's location information and provides information on restaurants and cafes based on the current location. For example, if the user inputs, "Tell me about nearby cafes," cafes near the current location are suggested. The analysis unit also uses the user's location information to provide real-time traffic information and weather information. For example, if the user inputs, "What's the weather like now?", weather information for the current location is displayed. The analysis unit also provides information on nearby events and tourist spots based on the user's location information. For example, if the user inputs, "Are there any events happening nearby?", event information for the area around the current location is suggested. This makes it possible to use the user's location information to provide real-time information based on the current location.

[0046] The user input unit also supports voice input and can respond to questions and requests made through voice input. For example, it analyzes the voice input and generates a text response to the voice question or request. For example, if a user voice inputs, "Tell me about some recommended restaurants," the system will suggest restaurants in text. The user input unit also uses voice recognition technology to convert the voice input into text and generates a response based on that text. For example, if a user voice inputs, "Tell me about some nearby cafes," the system will provide information about cafes in text. The user input unit also supports voice input and generates a voice response to questions and requests made through voice. For example, if a user voice inputs, "What's the weather like now?" the system will provide weather information in voice. This allows the system to support voice input and respond to questions and requests made through voice.

[0047] The user input part supports multiple languages and can provide services to users who speak different languages. For example, it supports multiple languages and generates responses according to the language input by the user. For example, when the user inputs "Recommend a restaurant" in English, a restaurant will be recommended in English. Also, the user input part uses automatic translation technology to handle questions and requests in different languages. For example, when the user inputs "推荐一个餐?" in Chinese, a restaurant will be recommended in Chinese. Additionally, the user input part supports multiple languages and provides personalized responses to users who speak different languages. For example, when the user inputs "Recomienda un restaurante" in Spanish, a restaurant will be recommended in Spanish. Thus, it is possible to provide services to users who speak different languages and support multiple languages.

[0048] When analyzing the user's input content, the generation AI can refer to relevant external databases to improve the accuracy of the answers. For example, when analyzing the user's input content, it refers to relevant news articles to generate answers. For example, when the user inputs "Tell me the latest technology news", an answer is provided based on the latest news articles. Also, the generation AI refers to the expertise base to generate highly accurate answers to the user's questions. For example, when the user inputs "Tell me about the latest technologies in AI", an answer is provided based on the expertise base. Additionally, the generation AI refers to external databases and generates detailed answers to the user's input content. For example, when the user inputs "Tell me the weather this weekend", an answer is provided based on the weather forecast database. Thus, when analyzing the user's input content, it is possible to refer to relevant external databases to improve the accuracy of the answers.

[0049] Generative AI can generate more personalized answers by taking into account a user's past behavioral patterns. For example, it can analyze a user's past behavioral patterns and generate personalized answers. For example, a user who has previously requested information on travel destinations can be provided with the latest information on areas they previously showed interest in. Generative AI can also generate individualized responses based on a user's past behavioral patterns. For example, a user who frequently requests restaurant information can be suggested restaurants based on their preferred cuisine genres. Generative AI can also generate answers that meet the user's needs by taking into account a user's past behavioral patterns. For example, a user who has previously requested movie information can be provided with the latest movie information and ticket purchase links. This makes it possible to generate more personalized answers by taking into account a user's past behavioral patterns.

[0050] When analyzing user input, the generation AI can also include at least one multimedia data, such as images or videos, in its analysis. For example, when analyzing user input, image data can also be included in its analysis. For example, if a user inputs, "Tell me the location of this image," image analysis can be performed to identify the location. The generation AI can also include video data in its analysis to generate an answer to the user's question. For example, if a user inputs, "Tell me the restaurant in this video," video analysis can be performed to identify the restaurant. The generation AI can also analyze multimedia data to generate a detailed answer to the user's input. For example, if a user inputs, "Tell me the building in this image," image analysis can be performed to identify the building. This allows multimedia data, such as images and videos, to be included in its analysis when analyzing user input.

[0051] When analyzing user input, the generative AI can refer to the opinions of experts in different fields to generate answers. For example, by referring to the opinions of experts in different fields, it can generate highly accurate answers to user questions. For example, if a user inputs, "Tell me about the latest AI technology," the generative AI will provide an answer based on the opinions of experts. The generative AI also refers to the opinions of experts to generate detailed answers to the user input. For example, if a user inputs, "Tell me about the latest medical technology," the generative AI will provide an answer based on the opinions of medical experts. The generative AI also refers to the opinions of experts in different fields to generate personalized answers to user questions. For example, if a user inputs, "Tell me about the latest environmental technology," the generative AI will provide an answer based on the opinions of environmental experts. This makes it possible to generate answers by referring to the opinions of experts in different fields when analyzing user input.

[0052] Generative AI can learn from a user's past purchase history and browsing history to make more accurate recommendations. For example, it can analyze a user's past purchase history and make recommendations based on the user's preferences and interests. For example, it can suggest new products related to products previously purchased. Generative AI can also make personalized recommendations based on a user's browsing history. For example, it can suggest items related to products previously viewed. Generative AI can also integrate a user's purchase history and browsing history to make more accurate recommendations. For example, it can suggest accessories or complementary products related to products previously purchased. This allows it to learn from a user's past purchase history and browsing history and make more accurate recommendations.

[0053] Generative AI can analyze a user's social media activity and make recommendations based on the user's interests. For example, it can analyze the content of a user's social media posts and make recommendations based on the user's interests. For example, it can suggest products and services that the user is talking about on social media. Generative AI can also make personalized recommendations based on the user's social media following and like history. For example, it can suggest products related to brands and accounts the user follows. Generative AI can also analyze a user's social media activity and make recommendations based on the user's interests. For example, it can provide information related to events and groups the user is participating in. This makes it possible to analyze a user's social media activity and make recommendations based on the user's interests.

[0054] When making recommendations to users, the generation AI can integrate information from different platforms. For example, it can integrate information from different platforms to make recommendations to users. For example, it can suggest products and services based on social media posts and blog articles. The generation AI can also analyze users' social media and blog activity to make personalized recommendations. For example, it can suggest products that users are talking about on social media or services that they are introducing on blogs. The generation AI can also integrate information from different platforms to make recommendations based on the user's interests and concerns. For example, it can suggest products related to social media accounts and blogs that the user follows. This makes it possible to integrate information from different platforms to make recommendations.

[0055] When making recommendations to a user, the generation AI can take into account the opinions of the user's friends and family. For example, it can analyze the opinions of the user's friends and family to make recommendations for the user. For example, it can suggest products and services that the user's friends have recommended on social media. The generation AI can also make personalized recommendations based on the purchase and browsing history of the user's friends and family. For example, it can suggest items related to products purchased by the user's family. The generation AI can also take into account the opinions of the user's friends and family to make recommendations based on the user's interests. For example, it can provide information related to events and groups that the user's friends are participating in. This allows the generation AI to make recommendations based on the opinions of the user's friends and family.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The user input unit accepts user input. For example, it can accept text input or voice input. In the case of voice input, voice recognition technology is used to convert the voice into text. Step 2: The analysis unit analyzes the input received by the user input unit. For example, it analyzes the input content using natural language processing technology or machine learning algorithms, learns the user's past dialogue history, and generates responses optimized for each individual user. Step 3: The recommendation unit generates appropriate recommendations based on the content analyzed by the analysis unit. For example, recommendations are generated based on the user's past behavioral data and the behavioral data of similar users. If a user enters "I want to buy a new smartphone," related products from Yahoo! Shopping will be suggested. Step 4: The guidance unit guides users to services within the company group based on the recommendations generated by the recommendation unit. For example, it presents links or sends notifications. For example, if a user types "I want to see a movie," it provides a link to purchase movie tickets on Yahoo Shopping.

[0058] (Example 2) The LINE Concierge system according to an embodiment of the present invention is a system in which, when a user inputs what they want to do or what they want to know, a generation AI analyzes the content and provides appropriate answers and recommendations. As a result, the LINE Concierge system can provide appropriate answers and recommendations based on the user's input, and guide the user to services within the company's group.

[0059] The LINE concierge system according to the embodiment includes a user input unit, an analysis unit, a recommendation unit, and a guidance unit. The user input unit accepts user input. For example, it accepts text input. The user input unit can also accept voice input. For example, it converts voice into text using voice recognition technology. The analysis unit analyzes the input accepted by the user input unit. For example, it analyzes the input content using natural language processing technology. The analysis unit can also analyze the input content using a machine learning algorithm. For example, it learns the user's past dialogue history and generates responses optimized for each individual user. The recommendation unit generates appropriate recommendations based on the content analyzed by the analysis unit. For example, it generates recommendations based on the user's past behavioral data. The recommendation unit can also generate recommendations based on behavioral data of similar users. For example, if a user enters "I want to buy a new smartphone," it suggests related products on Yahoo! Shopping. The guidance unit guides the user to services within the company group based on the recommendations generated by the recommendation unit. For example, it presents links. The guidance unit can also send notifications. For example, if a user inputs "I want to see a movie," a link to purchase movie tickets on Yahoo Shopping is provided. This allows the LINE Concierge system according to the embodiment to generate appropriate recommendations based on the user's input and guide the user to services of its own group.

[0060] The analysis unit can learn the user's past interaction history and generate responses optimized for the user. For example, the analysis unit analyzes the user's past interaction history and learns the user's preferences and interests. For example, for a user who has previously requested restaurant recommendations, restaurants based on the user's preferred cuisine genres are suggested. The analysis unit also generates personalized responses based on the user's past interaction history. For example, for a user who has previously requested information on travel destinations, the analysis unit provides the latest information on areas that the user previously showed interest in. The analysis unit also learns the user's interaction history and generates responses based on the user's behavioral patterns. For example, for a user who frequently requests movie information, the analysis unit provides the latest movie information and ticket purchase links. In this way, the analysis unit can learn the user's past interaction history and generate responses optimized for each individual user.

[0061] The analysis unit can use the user's location information to provide real-time information based on the current location. For example, the analysis unit acquires the user's location information and provides information on restaurants and cafes based on the current location. For example, if the user inputs, "Tell me about nearby cafes," cafes near the current location are suggested. The analysis unit also uses the user's location information to provide real-time traffic information and weather information. For example, if the user inputs, "What's the weather like now?", weather information for the current location is displayed. The analysis unit also provides information on nearby events and tourist spots based on the user's location information. For example, if the user inputs, "Are there any events happening nearby?", event information for the area around the current location is suggested. In this way, the user's location information can be used to provide real-time information based on the current location.

[0062] The analysis unit uses the emotion estimation function to generate a response according to the user's emotional state, thereby improving user satisfaction. The analysis unit, for example, estimates the user's emotion from the user's input and generates a response according to the emotion. For example, if the user inputs "tired," the analysis unit suggests places and activities where the user can relax. The analysis unit also uses the emotion estimation function to provide encouraging or comforting messages according to the user's emotional state. For example, if the user inputs "sad," the analysis unit provides encouraging words or uplifting content. The analysis unit also analyzes the user's emotional state in real time and generates a personalized response according to the emotion. For example, if the user inputs "happy," the analysis unit provides a congratulatory message or related positive information. This allows the analysis unit to generate a response according to the user's emotional state and improve user satisfaction.

[0063] The user input unit also supports voice input and can respond to questions and requests input via voice. The user input unit, for example, analyzes the voice input and generates a text response to the voice question or request. For example, if a user inputs by voice, "Tell me about a restaurant you recommend," the unit will suggest restaurants in text. The user input unit also uses voice recognition technology to convert the voice input into text and generates a response based on that text. For example, if a user inputs by voice, "Tell me about a nearby cafe," the unit will provide information about cafes in text. The user input unit also supports voice input and generates a voice response to the voice question or request. For example, if a user inputs by voice, "What's the weather like now?" the unit will provide weather information in voice. This allows the unit to support voice input and respond to questions and requests via voice.

[0064] The user input section supports multiple languages and can provide services to users who speak different languages. For example, the user input section supports multiple languages and generates responses according to the language input by the user. For example, if the user inputs "Recommend a restaurant" in English, a restaurant will be recommended in English. Also, the user input section uses automatic translation technology to handle questions and requests in different languages. For example, if the user inputs "推荐一个餐?" in Chinese, a restaurant will be recommended in Chinese. Additionally, the user input section supports multiple languages and provides personalized responses to users who speak different languages. For example, if the user inputs "Recomienda un restaurante" in Spanish, a restaurant will be recommended in Spanish. This enables the provision of services to users who speak different languages and support multiple languages.

[0065] The analysis section can use an emotion estimation function to estimate the emotion of the user in real-time when inputting and make proposals to elicit positive emotions. For example, the analysis section uses an emotion estimation function to analyze the emotion of the user in real-time when inputting and make proposals to elicit positive emotions. For example, when the user inputs "tired", places or activities where they can relax are proposed. Also, the analysis section estimates the emotion from the content input by the user and generates a response to elicit positive emotions. For example, when the user inputs "sad", words of encouragement or motivating content are provided. Additionally, the analysis section estimates the emotion in real-time and makes proposals for the user to have positive emotions. For example, when the user inputs "happy", a blessing message or related positive information is provided. This enables the estimation of the emotion of the user in real-time when inputting and making proposals to elicit positive emotions.

[0066] When analyzing a user's input, the generation AI can refer to a related external database to improve the accuracy of the answer. For example, when analyzing a user's input, the generation AI refers to related news articles to generate an answer. For example, if a user inputs, "Tell me the latest technology news," an answer based on the latest news articles is provided. The generation AI also refers to an expert knowledge base to generate a highly accurate answer to the user's question. For example, if a user inputs, "Tell me about the latest AI technology," an answer is provided based on the expert knowledge base. The generation AI also refers to an external database to generate a detailed answer to the user's input. For example, if a user inputs, "Tell me about the weather this weekend," an answer is provided based on a weather forecast database. This allows the generation AI to refer to a related external database to improve the accuracy of the answer when analyzing a user's input.

[0067] Generative AI can generate more personalized answers by taking into account a user's past behavioral patterns. For example, generative AI analyzes a user's past behavioral patterns to generate personalized answers. For example, a user who has previously requested information on travel destinations can be provided with the latest information on areas that the user previously showed interest in. Generative AI also generates individualized responses based on a user's past behavioral patterns. For example, a user who frequently requests restaurant information can be suggested restaurants based on the user's preferred cuisine genres. Generative AI also takes into account a user's past behavioral patterns to generate answers that meet the user's needs. For example, a user who has previously requested movie information can be provided with the latest movie information and ticket purchase links. This makes it possible to generate more personalized answers by taking into account a user's past behavioral patterns.

[0068] When analyzing user input, the generation AI can also include at least one multimedia data, such as images or videos, in its analysis. For example, when analyzing user input, the generation AI can also include image data in its analysis. For example, if a user inputs, "Tell me the location of this image," the generation AI can perform image analysis to identify the location. The generation AI can also include video data in its analysis to generate an answer to the user's question. For example, if a user inputs, "Tell me the restaurant in this video," the generation AI can perform video analysis to identify the restaurant. The generation AI can also analyze multimedia data to generate a detailed answer to the user's input. For example, if a user inputs, "Tell me the building in this image," the generation AI can perform image analysis to identify the building. This allows the generation AI to also include multimedia data, such as images and videos, in its analysis when analyzing user input.

[0069] When analyzing user input, the generative AI can refer to the opinions of experts in different fields to generate answers. For example, the generative AI may refer to the opinions of experts in different fields to generate highly accurate answers to user questions. For example, if a user inputs, "Tell me about the latest AI technology," the generative AI will provide an answer based on the opinions of experts. The generative AI may also refer to the opinions of experts to generate detailed answers to the user's input. For example, if a user inputs, "Tell me about the latest medical technology," the generative AI will provide an answer based on the opinions of medical experts. The generative AI may also refer to the opinions of experts in different fields to generate personalized answers to user questions. For example, if a user inputs, "Tell me about the latest environmental technology," the generative AI will provide an answer based on the opinions of environmental experts. This makes it possible to generate answers by referring to the opinions of experts in different fields when analyzing user input.

[0070] Generative AI can use emotion estimation to collect emotional responses to user input in real time and improve the accuracy of answers. For example, generative AI can use emotion estimation to collect emotional responses to user input in real time and improve the accuracy of answers based on that data. For example, if a user inputs "tired," the generative AI can suggest places and activities where they can relax. Generative AI can also generate personalized responses based on the user's emotional response data. For example, if a user inputs "sad," the generative AI can provide encouraging words and uplifting content. Generative AI can also use emotion estimation to collect emotional responses to user input in real time and improve the accuracy of answers based on that data. For example, if a user inputs "happy," the generative AI can provide congratulatory messages and related positive information. This allows generative AI to collect emotional responses to user input in real time and improve the accuracy of answers.

[0071] The generation AI can learn from a user's past purchase history and browsing history to make more accurate recommendations. For example, the generation AI can analyze a user's past purchase history and make recommendations based on the user's preferences and interests. For example, it can suggest new products related to products previously purchased. The generation AI can also make personalized recommendations based on the user's browsing history. For example, it can suggest items related to products previously viewed. The generation AI can also integrate a user's purchase history and browsing history to make more accurate recommendations. For example, it can suggest accessories or complementary products related to products previously purchased. This allows the generation AI to learn from a user's past purchase history and browsing history and make more accurate recommendations.

[0072] Generative AI can analyze a user's social media activity and make recommendations based on the user's interests. For example, generative AI can analyze the content of a user's social media posts and make recommendations based on the user's interests. For example, it can suggest products and services that the user is talking about on social media. Generative AI can also make personalized recommendations based on the user's social media following and like history. For example, it can suggest products related to brands and accounts the user follows. Generative AI can also analyze a user's social media activity and make recommendations based on the user's interests. For example, it can provide information related to events and groups the user is participating in. This makes it possible to analyze a user's social media activity and make recommendations based on the user's interests.

[0073] The generation AI uses the emotion estimation function to make recommendations based on the user's emotional state, thereby increasing the user's desire to purchase. For example, the generation AI uses the emotion estimation function to make recommendations based on the user's emotional state. For example, if the user inputs "tired," the generation AI will suggest products and services that will help them relax. The generation AI also analyzes the user's emotional state in real time and makes personalized recommendations based on the emotion. For example, if the user inputs "sad," the generation AI will suggest products and services that will cheer them up. The generation AI also uses the emotion estimation function to make recommendations based on the user's emotional state, thereby increasing the user's desire to purchase. For example, if the user inputs "happy," the generation AI will suggest related products along with a congratulatory message. This makes it possible to make recommendations based on the user's emotional state and increase the user's desire to purchase.

[0074] When making recommendations to users, the generation AI can integrate information from different platforms. For example, the generation AI integrates information from different platforms to make recommendations to users. For example, it can suggest products and services based on social media posts and blog articles. The generation AI can also analyze users' social media and blog activity to make personalized recommendations. For example, it can suggest products that users are talking about on social media or services that they are introducing on blogs. The generation AI can also integrate information from different platforms to make recommendations based on the user's interests and concerns. For example, it can suggest products related to social media accounts and blogs that the user follows. This makes it possible to integrate information from different platforms to make recommendations.

[0075] When making recommendations for a user, the generation AI can take into account the opinions of the user's friends and family. For example, the generation AI analyzes the opinions of the user's friends and family to make recommendations for the user. For example, it can suggest products and services that the user's friends recommend on social media. The generation AI also makes personalized recommendations based on the purchase and browsing history of the user's friends and family. For example, it can suggest items related to products purchased by the user's family. The generation AI also takes into account the opinions of the user's friends and family to make recommendations based on the user's interests. For example, it can provide information related to events and groups in which the user's friends are participating. This allows the generation AI to make recommendations based on the opinions of the user's friends and family.

[0076] The generation AI uses the emotion estimation function to monitor the emotional reactions of users when they receive recommendations in real time, and is able to continuously provide optimal recommendations. For example, the generation AI uses the emotion estimation function to monitor the emotional reactions of users when they receive recommendations in real time, and provides optimal recommendations based on that data. For example, it prioritizes recommendations that make the user feel "happy." The generation AI also continuously provides personalized recommendations based on the user's emotional reaction data. For example, it suggests related products and services based on recommendations that make the user feel "excited." The generation AI also uses the emotion estimation function to monitor the user's emotional reactions in real time, and is able to continuously provide optimal recommendations. For example, it makes the next suggestion based on a recommendation that makes the user feel "satisfied." This allows the generation AI to monitor the emotional reactions of users when they receive recommendations in real time, and is able to continuously provide optimal recommendations.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The user input unit accepts user input. For example, it accepts text input. The user input unit can also accept voice input. For example, it converts voice into text using voice recognition technology. The analysis unit analyzes the input accepted by the user input unit. For example, it analyzes the input content using natural language processing technology. The analysis unit can also analyze the input content using a machine learning algorithm. For example, it learns the user's past dialogue history and generates responses optimized for each individual user. The recommendation unit generates appropriate recommendations based on the content analyzed by the analysis unit. For example, it generates recommendations based on the user's past behavioral data. The recommendation unit can also generate recommendations based on behavioral data of similar users. For example, if a user enters "I want to buy a new smartphone," it suggests related products on Yahoo! Shopping. The guidance unit guides the user to services of the company group based on the recommendations generated by the recommendation unit. For example, it presents links. The guidance unit can also send notifications. For example, if a user inputs "I want to see a movie," a link to purchase movie tickets on Yahoo Shopping is provided. This allows the LINE Concierge system according to the embodiment to generate appropriate recommendations based on the user's input and guide the user to services of the company's group.

[0079] The analysis unit can learn the user's past interaction history and generate responses optimized for the user. For example, if a user has previously requested restaurant recommendations, restaurants based on the user's preferred cuisine genre will be suggested. The analysis unit also generates personalized responses based on the user's past interaction history. For example, if a user has previously requested information on travel destinations, the analysis unit will provide the user with the latest information on areas in which the user previously showed interest. The analysis unit also learns the user's interaction history and generates responses based on the user's behavioral patterns. For example, if a user frequently requests movie information, the analysis unit will provide the user with the latest movie information and ticket purchase links. This makes it possible to learn the user's past interaction history and generate responses optimized for each individual user.

[0080] The analysis unit can use the user's location information to provide real-time information based on the current location. For example, it acquires the user's location information and provides information on restaurants and cafes based on the current location. For example, if the user inputs, "Tell me about nearby cafes," cafes near the current location are suggested. The analysis unit also uses the user's location information to provide real-time traffic information and weather information. For example, if the user inputs, "What's the weather like now?", weather information for the current location is displayed. The analysis unit also provides information on nearby events and tourist spots based on the user's location information. For example, if the user inputs, "Are there any events happening nearby?", event information for the area around the current location is suggested. This makes it possible to use the user's location information to provide real-time information based on the current location.

[0081] The analysis unit uses the emotion estimation function to generate a response according to the user's emotional state, thereby improving user satisfaction. For example, the analysis unit estimates the user's emotion from the user's input and generates a response according to the emotion. For example, if the user inputs "tired," the analysis unit suggests places and activities where the user can relax. The analysis unit also uses the emotion estimation function to provide encouraging or comforting messages according to the user's emotional state. For example, if the user inputs "sad," the analysis unit provides encouraging words and uplifting content. The analysis unit also analyzes the user's emotional state in real time and generates a personalized response according to the emotion. For example, if the user inputs "happy," the analysis unit provides a congratulatory message or related positive information. This allows the analysis unit to generate a response according to the user's emotional state and improve user satisfaction.

[0082] The user input section supports voice input and can also respond to questions and requests made through voice input. For example, it analyzes the voice input and generates a text response to a voice question or request. For example, when the user inputs "Tell me a recommended restaurant" by voice, it proposes a restaurant in text. Also, the user input section uses voice recognition technology to convert voice input into text and generates a response based on that text. For example, when the user inputs "Tell me a nearby café" by voice, it provides information about the café in text. Additionally, the user input section supports voice input and generates a voice response to a voice question or request. For example, when the user inputs "What's the current weather?" by voice, it provides weather information in voice. Thus, it supports voice input and can also respond to questions and requests made through voice.

[0083] The user input section supports multiple languages and can also provide services to users who speak different languages. For example, it supports multiple languages and generates a response according to the language input by the user. For example, when the user inputs "Recommend a restaurant" in English, it proposes a restaurant in English. Also, the user input section uses automatic translation technology to handle questions and requests in different languages. For example, when the user inputs "推荐一个餐?" in Chinese, it proposes a restaurant in Chinese. Additionally, the user input section supports multiple languages and provides personalized responses to users who speak different languages. For example, when the user inputs "Recomienda un restaurante" in Spanish, it proposes a restaurant in Spanish. Thus, it supports multiple languages and can also provide services to users who speak different languages.

[0084] The analysis unit can use the emotion estimation function to estimate the emotion a user is feeling when they enter text in real time and make suggestions that will elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotion a user is feeling when they enter text in real time and make suggestions that will elicit positive emotions. For example, if a user enters "tired," the analysis unit can suggest places and activities where they can relax. The analysis unit can also estimate the emotion from the user's input and generate a response that will elicit positive emotions. For example, if a user enters "sad," the analysis unit can provide encouraging words and uplifting content. The analysis unit can also estimate the emotion in real time and make suggestions that will help the user feel positive emotions. For example, if a user enters "happy," the analysis unit can provide congratulatory messages and related positive information. This makes it possible to estimate the emotion a user is feeling when they enter text in real time and make suggestions that will elicit positive emotions.

[0085] When analyzing a user's input, the generation AI can refer to related external databases to improve the accuracy of the answer. For example, when analyzing a user's input, the generation AI can refer to related news articles to generate an answer. For example, if a user inputs, "Tell me the latest technology news," an answer based on the latest news articles is provided. The generation AI can also refer to an expert knowledge base to generate a highly accurate answer to the user's question. For example, if a user inputs, "Tell me about the latest AI technology," an answer based on the expert knowledge base is provided. The generation AI can also refer to an external database to generate a detailed answer to the user's input. For example, if a user inputs, "Tell me about the weather this weekend," an answer based on a weather forecast database is provided. This allows the generation AI to refer to related external databases to improve the accuracy of the answer when analyzing a user's input.

[0086] Generative AI can generate more personalized answers by taking into account a user's past behavioral patterns. For example, it can analyze a user's past behavioral patterns and generate personalized answers. For example, a user who has previously requested information on travel destinations can be provided with the latest information on areas they previously showed interest in. Generative AI can also generate individualized responses based on a user's past behavioral patterns. For example, a user who frequently requests restaurant information can be suggested restaurants based on their preferred cuisine genres. Generative AI can also generate answers that meet the user's needs by taking into account a user's past behavioral patterns. For example, a user who has previously requested movie information can be provided with the latest movie information and ticket purchase links. This makes it possible to generate more personalized answers by taking into account a user's past behavioral patterns.

[0087] When analyzing user input, the generation AI can also include at least one multimedia data, such as images or videos, in its analysis. For example, when analyzing user input, image data can also be included in its analysis. For example, if a user inputs, "Tell me the location of this image," image analysis can be performed to identify the location. The generation AI can also include video data in its analysis to generate an answer to the user's question. For example, if a user inputs, "Tell me the restaurant in this video," video analysis can be performed to identify the restaurant. The generation AI can also analyze multimedia data to generate a detailed answer to the user's input. For example, if a user inputs, "Tell me the building in this image," image analysis can be performed to identify the building. This allows multimedia data, such as images and videos, to be included in its analysis when analyzing user input.

[0088] When analyzing user input, the generative AI can refer to the opinions of experts in different fields to generate answers. For example, by referring to the opinions of experts in different fields, it can generate highly accurate answers to user questions. For example, if a user inputs, "Tell me about the latest AI technology," the generative AI will provide an answer based on the opinions of experts. The generative AI also refers to the opinions of experts to generate detailed answers to the user input. For example, if a user inputs, "Tell me about the latest medical technology," the generative AI will provide an answer based on the opinions of medical experts. The generative AI also refers to the opinions of experts in different fields to generate personalized answers to user questions. For example, if a user inputs, "Tell me about the latest environmental technology," the generative AI will provide an answer based on the opinions of environmental experts. This makes it possible to generate answers by referring to the opinions of experts in different fields when analyzing user input.

[0089] The generative AI can use its emotion estimation function to collect emotional responses to user input in real time and improve the accuracy of its answers. For example, the emotion estimation function can be used to collect emotional responses to user input in real time and improve the accuracy of its answers based on that data. For example, if a user inputs "tired," the generative AI can suggest places and activities where they can relax. The generative AI can also generate personalized responses based on the user's emotional response data. For example, if a user inputs "sad," the generative AI can provide words of encouragement and uplifting content. The generative AI can also use its emotion estimation function to collect emotional responses to user input in real time and improve the accuracy of its answers based on that data. For example, if a user inputs "happy," the generative AI can provide congratulatory messages and related positive information. This allows the generative AI to collect emotional responses to user input in real time and improve the accuracy of its answers.

[0090] Generative AI can learn from a user's past purchase history and browsing history to make more accurate recommendations. For example, it can analyze a user's past purchase history and make recommendations based on the user's preferences and interests. For example, it can suggest new products related to products previously purchased. Generative AI can also make personalized recommendations based on a user's browsing history. For example, it can suggest items related to products previously viewed. Generative AI can also integrate a user's purchase history and browsing history to make more accurate recommendations. For example, it can suggest accessories or complementary products related to products previously purchased. This allows it to learn from a user's past purchase history and browsing history and make more accurate recommendations.

[0091] Generative AI can analyze a user's social media activity and make recommendations based on the user's interests. For example, it can analyze the content of a user's social media posts and make recommendations based on the user's interests. For example, it can suggest products and services that the user is talking about on social media. Generative AI can also make personalized recommendations based on the user's social media following and like history. For example, it can suggest products related to brands and accounts the user follows. Generative AI can also analyze a user's social media activity and make recommendations based on the user's interests. For example, it can provide information related to events and groups the user is participating in. This makes it possible to analyze a user's social media activity and make recommendations based on the user's interests.

[0092] The generation AI uses the emotion estimation function to make recommendations based on the user's emotional state, increasing their willingness to purchase. For example, the emotion estimation function can be used to make recommendations based on the user's emotional state. For example, if the user inputs "tired," the system will suggest products and services that will help them relax. The generation AI can also analyze the user's emotional state in real time and make personalized recommendations based on the emotion. For example, if the user inputs "sad," the system will suggest products and services that will cheer them up. The generation AI can also use the emotion estimation function to make recommendations based on the user's emotional state, increasing their willingness to purchase. For example, if the user inputs "happy," the system will suggest related products along with a congratulatory message. This makes it possible to make recommendations based on the user's emotional state, increasing their willingness to purchase.

[0093] When making recommendations to users, the generation AI can integrate information from different platforms. For example, it can integrate information from different platforms to make recommendations to users. For example, it can suggest products and services based on social media posts and blog articles. The generation AI can also analyze users' social media and blog activity to make personalized recommendations. For example, it can suggest products that users are talking about on social media or services that they are introducing on blogs. The generation AI can also integrate information from different platforms to make recommendations based on the user's interests and concerns. For example, it can suggest products related to social media accounts and blogs that the user follows. This makes it possible to integrate information from different platforms to make recommendations.

[0094] When making recommendations to a user, the generation AI can take into account the opinions of the user's friends and family. For example, it can analyze the opinions of the user's friends and family to make recommendations for the user. For example, it can suggest products and services that the user's friends have recommended on social media. The generation AI can also make personalized recommendations based on the purchase and browsing history of the user's friends and family. For example, it can suggest items related to products purchased by the user's family. The generation AI can also take into account the opinions of the user's friends and family to make recommendations based on the user's interests. For example, it can provide information related to events and groups that the user's friends are participating in. This allows the generation AI to make recommendations based on the opinions of the user's friends and family.

[0095] The generation AI uses the emotion estimation function to monitor the emotional reactions of users when they receive recommendations in real time, and is able to continuously provide optimal recommendations. For example, the emotion estimation function can be used to monitor the emotional reactions of users when they receive recommendations in real time, and provide optimal recommendations based on that data. For example, recommendations that make the user feel "happy" are provided preferentially. The generation AI also continuously provides personalized recommendations based on the user's emotional reaction data. For example, related products and services are suggested based on recommendations that make the user feel "excited." The generation AI also uses the emotion estimation function to monitor the user's emotional reactions in real time, and is able to continuously provide optimal recommendations. For example, the next suggestion is made based on a recommendation that makes the user feel "satisfied." This makes it possible to monitor the emotional reactions of users when they receive recommendations in real time, and is able to continuously provide optimal recommendations.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The user input unit accepts user input. For example, it can accept text input or voice input. In the case of voice input, voice recognition technology is used to convert the voice into text. Step 2: The analysis unit analyzes the input received by the user input unit. For example, it analyzes the input content using natural language processing technology or machine learning algorithms, learns the user's past dialogue history, and generates responses optimized for each individual user. Step 3: The recommendation unit generates appropriate recommendations based on the content analyzed by the analysis unit. For example, recommendations are generated based on the user's past behavioral data and the behavioral data of similar users. If a user enters "I want to buy a new smartphone," related products from Yahoo! Shopping will be suggested. Step 4: The guidance unit guides users to services within the company group based on the recommendations generated by the recommendation unit. For example, it presents links or sends notifications. For example, if a user types "I want to see a movie," it provides a link to purchase movie tickets on Yahoo Shopping.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The 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.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 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.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0111] 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.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0115] The data processing system 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 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 user input unit that accepts user input; an analysis unit that analyzes the input received by the user input unit; a recommendation unit that generates appropriate recommendations based on the content analyzed by the analysis unit; and a guidance unit that guides users to services of its own group based on the recommendations generated by the recommendation unit. A system characterized by:

2. The analysis unit Learns the user's past interaction history and generates a response optimized for the user 2. The system of claim 1.

3. The analysis unit Using the user's location information, real-time information is provided based on the user's current location.

2. The system of claim 1.

4. The analysis unit Generate a response according to the emotional state of the user, thereby improving the satisfaction of the user.

2. The system of claim 1.

5. The user input unit It also supports voice input and responds to questions and requests entered through voice input.

2. The system of claim 1.

Images