system

Abstract

The system according to this embodiment aims to respond individually to a variety of user questions and requests and to provide appropriate answers and advice. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives questions and requests from users. The analysis unit analyzes the questions and requests received by the reception unit and generates appropriate answers and advice. The provision unit provides the answers and advice generated by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to individually respond to various questions and requests of users, and there is a problem that efficient support is not provided.

[0005] The system according to the embodiment aims to individually respond to various questions and requests of users and provide appropriate answers and advice.

Means for Solving the Problems

[0006] [[ID=]] The system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives questions and requests from users. The analysis unit analyzes the questions and requests received by the reception unit and generates appropriate answers and advice. The provision unit provides the answers and advice generated by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can respond individually to a variety of user questions and requests, and provide appropriate answers and advice. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An entertainment AI service according to an embodiment of the present invention is a system that utilizes generative AI to provide individualized support for the daily problems and questions that each user faces. This system receives questions and requests from users, the generative AI analyzes those questions and requests, and provides accurate answers and advice. For example, it can solve problems and provide educational information in various areas, such as health, beauty, cooking advice, and travel planning support. This mechanism is expected to make it easier for individuals to solve problems and gather information, enabling each person to lead a more fulfilling daily life. First, the system receives questions and requests from users. At this time, the user inputs specific questions and requests. For example, the user may input questions or requests such as "I want to know some healthy meal recipes" or "I want to know some recommended spots for my next travel destination." This information is input into the generative AI. Next, the generative AI analyzes the input questions and requests. The generative AI understands the user's questions and requests and generates appropriate answers and advice. For example, in response to a question asking for healthy meal recipes, the generative AI suggests a nutritionally balanced recipe. Also, in response to a request for recommended spots for the next travel destination, the generative AI suggests tourist spots and activities at the travel destination. The generated answers and advice are provided to the user. For example, recipes or travel destination information suggested by the generating AI are displayed on the user's smartphone or personal computer. This allows users to easily obtain information and solve everyday problems and questions. This system makes personal problem-solving and information gathering more efficient. By utilizing the generating AI, users can obtain quick and accurate answers and advice. This is thought to enable each individual to lead a more fulfilling life. For example, by providing appropriate advice to health-related questions, the generating AI can help users lead healthier lives. Also, by receiving support for travel planning, users can enjoy more fulfilling trips. In this way, entertainment AI services can provide appropriate answers and advice to users' questions and requests.

[0029] The entertainment AI service according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives questions and requests from the user. User questions and requests include, but are not limited to, requests such as "Please tell me a recipe for a healthy meal" or "Please tell me recommended spots for my next travel destination." The reception unit receives questions and requests entered by the user as text data, for example. The reception unit can also receive questions and requests from the user using voice input. For example, the user dictates a question or request using a microphone, and the reception unit converts that voice data into text data. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of receiving questions and requests based on the estimated emotions of the user. For example, if the user is feeling stressed, questions and requests may be received at a time when they can relax. The analysis unit uses a generation AI to analyze the questions and requests received by the reception unit and generate appropriate answers and advice. The analysis unit uses, for example, a generation AI to understand the user's questions and requests and generate appropriate answers and advice. The generation AI generates answers and advice to user questions and requests, for example, using a text generation AI (e.g., LLM). The analysis unit can also analyze questions and requests using natural language processing and extract appropriate information from a database. For example, when the generation AI suggests a healthy meal recipe, it extracts a nutritionally balanced recipe from the database and provides it to the user. The provision unit provides the answers and advice generated by the analysis unit. The provision unit displays the answers and advice generated by the generation AI on the user's smartphone or personal computer. The provision unit can also provide the user with recipes and travel destination information suggested by the generation AI. For example, it can display a recipe suggested by the generation AI on the user's smartphone so that the user can check the recipe. In this way, the entertainment AI service according to the embodiment can provide appropriate answers and advice to user questions and requests. Some or all of the above-described processes in the reception unit, analysis unit, and provision unit may be performed using AI, for example, or without AI.For example, the reception department can input user questions and requests into an AI model, the analysis department can analyze the questions and requests using the AI ​​model, and the service department can provide answers and advice generated using the AI ​​model.

[0030] The reception desk receives user questions and requests. These questions and requests may include, but are not limited to, requests for healthy meal recipes or recommendations for places to visit on a future trip. The reception desk can, for example, receive user-entered questions and requests as text data. Specifically, it can receive text data entered by users through smartphone or personal computer interfaces in real time. The reception desk can also accept user questions and requests using voice input. For example, a user might dictate a question or request using a microphone, and the reception desk converts that voice data into text. Using speech recognition technology, the reception desk can accurately transcribe user speech, enabling smooth interaction. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of receiving questions and requests based on the estimated emotions. For example, if a user is feeling stressed, the reception desk can accept questions and requests during times when the user is likely to relax. Emotion estimation can utilize technologies that analyze voice tone, text content, and even the user's facial expressions. This allows for flexible responses tailored to the user's psychological state, enabling a more personalized service. The reception desk integrates these functions and provides a powerful interface to meet the diverse needs of users.

[0031] The analysis unit uses generative AI to analyze questions and requests received by the reception unit and generate appropriate answers and advice. For example, the generative AI understands the user's questions and requests and generates appropriate answers and advice. The generative AI uses, for example, text generation AI (e.g., LLM) to generate answers and advice to the user's questions and requests. Specifically, the generative AI analyzes the user's input text and generates the optimal answer based on its content. For example, if a user inputs "I want a recipe for a healthy meal," the generative AI will suggest a recipe that takes into account nutritional balance, calories, and types of ingredients. The analysis unit can also use natural language processing to analyze questions and requests and extract appropriate information from the database. For example, when the generative AI suggests a healthy meal recipe, it extracts a nutritionally balanced recipe from the database and provides it to the user. Furthermore, the analysis unit can generate more personalized answers based on the user's past questions, requests, and usage history. For example, if it is known that the user has liked a particular ingredient in the past, it will prioritize suggesting recipes that include that ingredient. This allows the analysis unit to provide highly accurate answers and advice tailored to the user's needs.

[0032] The service provider provides answers and advice generated by the analysis unit. For example, the service provider displays answers and advice generated by the generation AI on the user's smartphone or personal computer. Specifically, the generated answers and advice are displayed on the screen of the user's device in the form of text, images, videos, etc. The service provider can also provide users with recipes and travel destination information suggested by the generation AI. For example, it can display recipes suggested by the generation AI on the user's smartphone, allowing the user to review them. Furthermore, the service provider can collect user feedback and send it to the analysis unit in order to provide information tailored to the user's preferences. For example, the user can evaluate the suggested recipes, and the analysis unit can use that evaluation to improve future suggestions. The service provider can also monitor user usage and provide information at the appropriate time. For example, if a user tends to search for recipes at a specific time of day, the service provider can suggest new recipes to match that time. In this way, the service provider can always provide users with the most optimal information and improve user satisfaction.

[0033] The analysis unit can analyze questions and requests using generative AI and generate appropriate answers and advice. For example, the analysis unit uses generative AI to understand the user's questions and requests and generate appropriate answers and advice. The generative AI uses, for example, text generation AI (e.g., LLM) to generate answers and advice to the user's questions and requests. The analysis unit can also use natural language processing to analyze questions and requests and extract appropriate information from a database. For example, when the generative AI suggests a healthy meal recipe, it extracts a nutritionally balanced recipe from the database and provides it to the user. This improves the accuracy of question and request analysis by using generative AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's questions and requests into an AI model, use the AI ​​model to analyze the questions and requests, and generate appropriate answers and advice.

[0034] The service provider can display the answers and advice generated by the generation AI on the user's smartphone or personal computer. For example, the service provider can provide the user with recipes or travel destination information suggested by the generation AI. The service provider can display the answers and advice generated by the generation AI on the user's smartphone or personal computer. For example, it can display a recipe suggested by the generation AI on the user's smartphone so that the user can check the recipe. It can also display travel destination information suggested by the generation AI on the user's personal computer so that the user can check the information. In this way, the service provider can provide the user with the answers and advice generated by the generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the answers and advice generated by the generation AI into an AI model and use the AI ​​model to display them on the user's smartphone or personal computer.

[0035] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk can prioritize suggesting question formats that the user has frequently used in the past. The reception desk can accept questions during times that the user has preferred to use in the past. The reception desk can also automatically suggest relevant questions based on the content of the user's past questions. In this way, the optimal reception method can be selected by analyzing the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past question history into an AI model and use the AI ​​model to select the optimal reception method.

[0036] The reception desk can filter questions and requests based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, the reception desk can prioritize health-related questions. If the user is planning a trip, the reception desk can prioritize travel-related questions. The reception desk can also prioritize simple questions if the user is busy. By filtering questions and requests based on the user's lifestyle and areas of interest, the reception desk can receive more relevant questions and requests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the user's lifestyle and areas of interest into an AI model and use the AI ​​model to filter questions and requests.

[0037] The reception desk can prioritize receiving questions and requests that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize questions related to that region. If the user is traveling, the reception desk can prioritize questions related to their travel destination. Furthermore, if the user is at home, the reception desk can prioritize questions related to their home. In this way, by considering the user's geographical location, the reception desk can prioritize receiving questions and requests that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location into an AI model and use the AI ​​model to prioritize receiving questions and requests that are highly relevant.

[0038] The reception desk can analyze a user's social media activity when receiving questions or requests and receive relevant questions or requests. For example, if a user posts about health on social media, the reception desk can prioritize health-related questions. If a user posts about travel, the reception desk can prioritize travel-related questions. Similarly, if a user posts about cooking, the reception desk can prioritize cooking-related questions. In this way, by analyzing a user's social media activity, it is possible to prioritize receiving relevant questions and requests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into an AI model and use the AI ​​model to receive relevant questions and requests.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the questions and requests during the analysis. For example, the analysis unit can perform a detailed analysis for important questions, a standard analysis for general questions, and a concise analysis for simple questions. By adjusting the level of detail of the analysis based on the importance of the questions and requests, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of questions and requests into an AI model and use the AI ​​model to adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the question or request during analysis. For example, for questions about health, the analysis unit can apply an analysis algorithm using a medical database. For questions about travel, the analysis unit can apply an analysis algorithm using a tourism database. Furthermore, for questions about cooking, the analysis unit can apply an analysis algorithm using a recipe database. By applying different analysis algorithms depending on the category of the question or request, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category data of questions and requests into an AI model and apply different analysis algorithms using the AI ​​model.

[0041] The analysis unit can determine the priority of analysis based on when questions and requests were submitted. For example, the analysis unit can prioritize analysis for urgent questions. For regular questions, it can perform analysis with a standard priority. The analysis unit can also postpone analysis for past questions. By determining the priority of analysis based on when questions and requests were submitted, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data on when questions and requests were submitted into an AI model and use the AI ​​model to determine the priority of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of questions and requests during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant questions. It can also postpone the analysis of less relevant questions. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of questions. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of questions and requests. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of questions and requests into an AI model and use the AI ​​model to adjust the order of analysis.

[0043] The service provider can select the optimal display method by referring to the user's past usage history at the time of service provision. For example, the service provider can prioritize providing display methods that the user has previously preferred. The service provider can prioritize displaying relevant information based on the user's past usage history. The service provider can also analyze the user's past usage history and dynamically select the optimal display method. This allows the service provider to select the optimal display method by referring to the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past usage history data into an AI model and use the AI ​​model to select the optimal display method.

[0044] The information delivery unit can customize the information based on the user's current lifestyle at the time of delivery. For example, if the user is interested in health, the unit can prioritize providing health-related information. If the user is planning a trip, the unit can prioritize providing travel-related information. The unit can also prioritize providing simple information if the user is busy. By customizing the information based on the user's lifestyle, it is possible to provide more relevant information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the user's lifestyle data into an AI model and customize the information using the AI ​​model.

[0045] The information provider can provide optimal information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the information provider can prioritize providing information related to that region. If the user is traveling, the information provider can prioritize providing information related to the travel destination. Furthermore, if the user is at home, the information provider can prioritize providing information relevant to the home. In this way, optimal information can be provided by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into an AI model and use the AI ​​model to provide optimal information.

[0046] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, if the user posts health-related content on social media, the service provider can prioritize providing health-related information. If the user posts travel-related content, the service provider can prioritize providing travel-related information. Furthermore, if the user posts cooking-related content, the service provider can prioritize providing cooking-related information. In this way, by analyzing the user's social media activity, relevant information can be prioritized. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into an AI model and use the AI ​​model to provide relevant information.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] Entertainment AI services can not only provide answers and advice to user questions and requests, but also incorporate predictive capabilities based on the user's behavioral history. For example, if a user has frequently asked health-related questions in the past, the AI ​​can predict that the next question is likely to be health-related and prepare relevant information in advance. Similarly, if a user frequently asks travel-related questions, the AI ​​can gather information on the next travel destination in advance and suggest it before the user asks. Furthermore, if a user tends to ask questions at certain times of the day, the AI ​​can update the information accordingly and provide quicker answers. This allows the AI ​​to learn the user's behavioral patterns and provide faster and more accurate support.

[0049] Entertainment AI services not only provide answers and advice to user questions and requests, but can also adjust the timing of information delivery based on the user's daily routine. For example, if a user tends to ask health-related questions in the morning, health-related information can be prioritized during the morning hours. Similarly, if a user seeks information to help them relax at night, relaxation-related information can be provided during the evening hours. Furthermore, if a user tends to plan trips on weekends, travel-related information can be provided on weekends. This allows for information delivery tailored to the user's lifestyle, resulting in more effective support.

[0050] Entertainment AI services can not only provide answers and advice to user questions and requests, but also incorporate personalization features based on the user's past usage history. For example, they can suggest new recipes based on recipes the user has enjoyed using in the past. They can also suggest next travel destinations based on information about places the user has visited in the past. Furthermore, they can provide information tailored to the time of day the user has used the service in the past. This allows for the provision of more personalized services by leveraging the user's past usage history.

[0051] Entertainment AI services can not only provide answers and advice to user questions and requests, but also offer highly relevant information by considering the user's geographical location. For example, if a user is in a specific region, it can prioritize providing information related to that region. If a user is traveling, it can prioritize providing information related to their travel destination. Furthermore, if a user is at home, it can prioritize providing information relevant to their home. This allows for the provision of highly relevant information by considering the user's geographical location.

[0052] Entertainment AI services can not only provide answers and advice to user questions and requests, but also analyze users' social media activity to provide relevant information. For example, if a user posts about health on social media, it can prioritize providing health-related information. If a user posts about travel, it can prioritize providing travel-related information. Similarly, if a user posts about cooking, it can prioritize providing cooking-related information. In this way, by analyzing a user's social media activity, it can prioritize providing relevant information.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The reception desk receives user questions and requests. These questions and requests may include, for example, recipes for healthy meals or recommendations for places to visit on a future trip. The reception desk can receive user-entered questions and requests as text data, as well as via voice input. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of receiving questions and requests based on those emotions. Step 2: The analysis unit uses a generation AI to analyze the questions and requests received by the reception unit and generate appropriate answers and advice. The analysis unit can, for example, use a text generation AI (e.g., LLM) to generate answers and advice to user questions and requests. It can also use natural language processing to analyze questions and requests and extract appropriate information from the database. Step 3: The service provider provides the answers and advice generated by the analysis unit. For example, the service provider displays the answers and advice generated by the generation AI on the user's smartphone or personal computer. It can also provide the user with recipes or travel destination information suggested by the generation AI.

[0055] (Example of form 2) An entertainment AI service according to an embodiment of the present invention is a system that utilizes generative AI to provide individualized support for the daily problems and questions that each user faces. This system receives questions and requests from users, the generative AI analyzes those questions and requests, and provides accurate answers and advice. For example, it can solve problems and provide educational information in various areas, such as health, beauty, cooking advice, and travel planning support. This mechanism is expected to make it easier for individuals to solve problems and gather information, enabling each person to lead a more fulfilling daily life. First, the system receives questions and requests from users. At this time, the user inputs specific questions and requests. For example, the user may input questions or requests such as "I want to know some healthy meal recipes" or "I want to know some recommended spots for my next travel destination." This information is input into the generative AI. Next, the generative AI analyzes the input questions and requests. The generative AI understands the user's questions and requests and generates appropriate answers and advice. For example, in response to a question asking for healthy meal recipes, the generative AI suggests a nutritionally balanced recipe. Also, in response to a request for recommended spots for the next travel destination, the generative AI suggests tourist spots and activities at the travel destination. The generated answers and advice are provided to the user. For example, recipes or travel destination information suggested by the generating AI are displayed on the user's smartphone or personal computer. This allows users to easily obtain information and solve everyday problems and questions. This system makes personal problem-solving and information gathering more efficient. By utilizing the generating AI, users can obtain quick and accurate answers and advice. This is thought to enable each individual to lead a more fulfilling life. For example, by providing appropriate advice to health-related questions, the generating AI can help users lead healthier lives. Also, by receiving support for travel planning, users can enjoy more fulfilling trips. In this way, entertainment AI services can provide appropriate answers and advice to users' questions and requests.

[0056] The entertainment AI service according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives questions and requests from the user. User questions and requests include, but are not limited to, requests such as "Please tell me a recipe for a healthy meal" or "Please tell me recommended spots for my next travel destination." The reception unit receives questions and requests entered by the user as text data, for example. The reception unit can also receive questions and requests from the user using voice input. For example, the user dictates a question or request using a microphone, and the reception unit converts that voice data into text data. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of receiving questions and requests based on the estimated emotions of the user. For example, if the user is feeling stressed, questions and requests may be received at a time when they can relax. The analysis unit uses a generation AI to analyze the questions and requests received by the reception unit and generate appropriate answers and advice. The analysis unit uses, for example, a generation AI to understand the user's questions and requests and generate appropriate answers and advice. The generation AI generates answers and advice to user questions and requests, for example, using a text generation AI (e.g., LLM). The analysis unit can also analyze questions and requests using natural language processing and extract appropriate information from a database. For example, when the generation AI suggests a healthy meal recipe, it extracts a nutritionally balanced recipe from the database and provides it to the user. The provision unit provides the answers and advice generated by the analysis unit. The provision unit displays the answers and advice generated by the generation AI on the user's smartphone or personal computer. The provision unit can also provide the user with recipes and travel destination information suggested by the generation AI. For example, it can display a recipe suggested by the generation AI on the user's smartphone so that the user can check the recipe. In this way, the entertainment AI service according to the embodiment can provide appropriate answers and advice to user questions and requests. Some or all of the above-described processes in the reception unit, analysis unit, and provision unit may be performed using AI, for example, or without AI.For example, the reception department can input user questions and requests into an AI model, the analysis department can analyze the questions and requests using the AI ​​model, and the service department can provide answers and advice generated using the AI ​​model.

[0057] The reception desk receives user questions and requests. These questions and requests may include, but are not limited to, requests for healthy meal recipes or recommendations for places to visit on a future trip. The reception desk can, for example, receive user-entered questions and requests as text data. Specifically, it can receive text data entered by users through smartphone or personal computer interfaces in real time. The reception desk can also accept user questions and requests using voice input. For example, a user might dictate a question or request using a microphone, and the reception desk converts that voice data into text. Using speech recognition technology, the reception desk can accurately transcribe user speech, enabling smooth interaction. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of receiving questions and requests based on the estimated emotions. For example, if a user is feeling stressed, the reception desk can accept questions and requests during times when the user is likely to relax. Emotion estimation can utilize technologies that analyze voice tone, text content, and even the user's facial expressions. This allows for flexible responses tailored to the user's psychological state, enabling a more personalized service. The reception desk integrates these functions and provides a powerful interface to meet the diverse needs of users.

[0058] The analysis unit uses generative AI to analyze questions and requests received by the reception unit and generate appropriate answers and advice. For example, the generative AI understands the user's questions and requests and generates appropriate answers and advice. The generative AI uses, for example, text generation AI (e.g., LLM) to generate answers and advice to the user's questions and requests. Specifically, the generative AI analyzes the user's input text and generates the optimal answer based on its content. For example, if a user inputs "I want a recipe for a healthy meal," the generative AI will suggest a recipe that takes into account nutritional balance, calories, and types of ingredients. The analysis unit can also use natural language processing to analyze questions and requests and extract appropriate information from the database. For example, when the generative AI suggests a healthy meal recipe, it extracts a nutritionally balanced recipe from the database and provides it to the user. Furthermore, the analysis unit can generate more personalized answers based on the user's past questions, requests, and usage history. For example, if it is known that the user has liked a particular ingredient in the past, it will prioritize suggesting recipes that include that ingredient. This allows the analysis unit to provide highly accurate answers and advice tailored to the user's needs.

[0059] The service provider provides answers and advice generated by the analysis unit. For example, the service provider displays answers and advice generated by the generation AI on the user's smartphone or personal computer. Specifically, the generated answers and advice are displayed on the screen of the user's device in the form of text, images, videos, etc. The service provider can also provide users with recipes and travel destination information suggested by the generation AI. For example, it can display recipes suggested by the generation AI on the user's smartphone, allowing the user to review them. Furthermore, the service provider can collect user feedback and send it to the analysis unit in order to provide information tailored to the user's preferences. For example, the user can evaluate the suggested recipes, and the analysis unit can use that evaluation to improve future suggestions. The service provider can also monitor user usage and provide information at the appropriate time. For example, if a user tends to search for recipes at a specific time of day, the service provider can suggest new recipes to match that time. In this way, the service provider can always provide users with the most optimal information and improve user satisfaction.

[0060] The analysis unit can analyze questions and requests using generative AI and generate appropriate answers and advice. For example, the analysis unit uses generative AI to understand the user's questions and requests and generate appropriate answers and advice. The generative AI uses, for example, text generation AI (e.g., LLM) to generate answers and advice to the user's questions and requests. The analysis unit can also use natural language processing to analyze questions and requests and extract appropriate information from a database. For example, when the generative AI suggests a healthy meal recipe, it extracts a nutritionally balanced recipe from the database and provides it to the user. This improves the accuracy of question and request analysis by using generative AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's questions and requests into an AI model, use the AI ​​model to analyze the questions and requests, and generate appropriate answers and advice.

[0061] The service provider can display the answers and advice generated by the generation AI on the user's smartphone or personal computer. For example, the service provider can provide the user with recipes or travel destination information suggested by the generation AI. The service provider can display the answers and advice generated by the generation AI on the user's smartphone or personal computer. For example, it can display a recipe suggested by the generation AI on the user's smartphone so that the user can check the recipe. It can also display travel destination information suggested by the generation AI on the user's personal computer so that the user can check the information. In this way, the service provider can provide the user with the answers and advice generated by the generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the answers and advice generated by the generation AI into an AI model and use the AI ​​model to display them on the user's smartphone or personal computer.

[0062] The reception desk can estimate the user's emotions and adjust the timing of receiving questions and requests based on the estimated emotions. For example, if the user is stressed, the reception desk will accept questions and requests during times when the user can relax. If the user is excited, the reception desk can accept questions and requests immediately. Also, if the user is tired, the reception desk can accept questions and requests after they have rested. In this way, by adjusting the timing of receiving questions and requests according to the user's emotions, questions and requests can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input user emotion data into an AI model, use the AI ​​model to estimate emotions, and adjust the timing of receiving questions and requests.

[0063] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk can prioritize suggesting question formats that the user has frequently used in the past. The reception desk can accept questions during times that the user has preferred to use in the past. The reception desk can also automatically suggest relevant questions based on the content of the user's past questions. In this way, the optimal reception method can be selected by analyzing the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past question history into an AI model and use the AI ​​model to select the optimal reception method.

[0064] The reception desk can filter questions and requests based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, the reception desk can prioritize health-related questions. If the user is planning a trip, the reception desk can prioritize travel-related questions. The reception desk can also prioritize simple questions if the user is busy. By filtering questions and requests based on the user's lifestyle and areas of interest, the reception desk can receive more relevant questions and requests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the user's lifestyle and areas of interest into an AI model and use the AI ​​model to filter questions and requests.

[0065] The reception desk can estimate the user's emotions and determine the priority of questions and requests based on the estimated emotions. For example, if the user has an urgent question, the reception desk will prioritize it. If the user is relaxed, the reception desk can respond with normal priority. The reception desk can also respond quickly if the user is feeling anxious. This allows for more appropriate responses by prioritizing questions and requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI model, use the AI ​​model to estimate emotions, and determine the priority of questions and requests.

[0066] The reception desk can prioritize receiving questions and requests that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize questions related to that region. If the user is traveling, the reception desk can prioritize questions related to their travel destination. Furthermore, if the user is at home, the reception desk can prioritize questions related to their home. In this way, by considering the user's geographical location, the reception desk can prioritize receiving questions and requests that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location into an AI model and use the AI ​​model to prioritize receiving questions and requests that are highly relevant.

[0067] The reception desk can analyze a user's social media activity when receiving questions or requests and receive relevant questions or requests. For example, if a user posts about health on social media, the reception desk can prioritize health-related questions. If a user posts about travel, the reception desk can prioritize travel-related questions. Similarly, if a user posts about cooking, the reception desk can prioritize cooking-related questions. In this way, by analyzing a user's social media activity, it is possible to prioritize receiving relevant questions and requests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into an AI model and use the AI ​​model to receive relevant questions and requests.

[0068] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into an AI model, use the AI ​​model to estimate emotions, and adjust the presentation of the analysis.

[0069] The analysis unit can adjust the level of detail of the analysis based on the importance of the questions and requests during the analysis. For example, the analysis unit can perform a detailed analysis for important questions, a standard analysis for general questions, and a concise analysis for simple questions. By adjusting the level of detail of the analysis based on the importance of the questions and requests, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of questions and requests into an AI model and use the AI ​​model to adjust the level of detail of the analysis.

[0070] The analysis unit can apply different analysis algorithms depending on the category of the question or request during analysis. For example, for questions about health, the analysis unit can apply an analysis algorithm using a medical database. For questions about travel, the analysis unit can apply an analysis algorithm using a tourism database. Furthermore, for questions about cooking, the analysis unit can apply an analysis algorithm using a recipe database. By applying different analysis algorithms depending on the category of the question or request, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category data of questions and requests into an AI model and apply different analysis algorithms using the AI ​​model.

[0071] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into an AI model, use the AI ​​model to estimate emotions, and adjust the length of the analysis.

[0072] The analysis unit can determine the priority of analysis based on when questions and requests were submitted. For example, the analysis unit can prioritize analysis for urgent questions. For regular questions, it can perform analysis with a standard priority. The analysis unit can also postpone analysis for past questions. By determining the priority of analysis based on when questions and requests were submitted, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data on when questions and requests were submitted into an AI model and use the AI ​​model to determine the priority of analysis.

[0073] The analysis unit can adjust the order of analysis based on the relevance of questions and requests during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant questions. It can also postpone the analysis of less relevant questions. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of questions. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of questions and requests. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of questions and requests into an AI model and use the AI ​​model to adjust the order of analysis.

[0074] The service provider can estimate the user's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. By adjusting the way information is displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into an AI model, use the AI ​​model to estimate emotions, and adjust the way information is displayed.

[0075] The service provider can select the optimal display method by referring to the user's past usage history at the time of service provision. For example, the service provider can prioritize providing display methods that the user has previously preferred. The service provider can prioritize displaying relevant information based on the user's past usage history. The service provider can also analyze the user's past usage history and dynamically select the optimal display method. This allows the service provider to select the optimal display method by referring to the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past usage history data into an AI model and use the AI ​​model to select the optimal display method.

[0076] The information delivery unit can customize the information based on the user's current lifestyle at the time of delivery. For example, if the user is interested in health, the unit can prioritize providing health-related information. If the user is planning a trip, the unit can prioritize providing travel-related information. The unit can also prioritize providing simple information if the user is busy. By customizing the information based on the user's lifestyle, it is possible to provide more relevant information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the user's lifestyle data into an AI model and customize the information using the AI ​​model.

[0077] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user needs urgent information, the provider will provide it with priority. If the user is relaxed, the provider can provide it with normal priority. The provider can also provide it quickly if the user is feeling anxious. This allows for more appropriate information to be provided by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into an AI model, use the AI ​​model to estimate emotions, and determine the priority of information.

[0078] The information provider can provide optimal information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the information provider can prioritize providing information related to that region. If the user is traveling, the information provider can prioritize providing information related to the travel destination. Furthermore, if the user is at home, the information provider can prioritize providing information relevant to the home. In this way, optimal information can be provided by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into an AI model and use the AI ​​model to provide optimal information.

[0079] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, if the user posts health-related content on social media, the service provider can prioritize providing health-related information. If the user posts travel-related content, the service provider can prioritize providing travel-related information. Furthermore, if the user posts cooking-related content, the service provider can prioritize providing cooking-related information. In this way, by analyzing the user's social media activity, relevant information can be prioritized. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into an AI model and use the AI ​​model to provide relevant information.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] Entertainment AI services can not only provide answers and advice to user questions and requests, but also incorporate predictive capabilities based on the user's behavioral history. For example, if a user has frequently asked health-related questions in the past, the AI ​​can predict that the next question is likely to be health-related and prepare relevant information in advance. Similarly, if a user frequently asks travel-related questions, the AI ​​can gather information on the next travel destination in advance and suggest it before the user asks. Furthermore, if a user tends to ask questions at certain times of the day, the AI ​​can update the information accordingly and provide quicker answers. This allows the AI ​​to learn the user's behavioral patterns and provide faster and more accurate support.

[0082] Entertainment AI services can not only provide answers and advice to user questions and requests, but also estimate the user's emotions and adjust the tone and content of their responses based on those emotions. For example, if a user is stressed, the AI ​​can provide a calming and gentle response. If a user is excited, it can provide an energetic response. Furthermore, if a user is sad, it can provide a response that includes words of encouragement. This allows for responses that are more empathetic to the user's emotions, resulting in a more satisfying service.

[0083] Entertainment AI services not only provide answers and advice to user questions and requests, but can also adjust the timing of information delivery based on the user's daily routine. For example, if a user tends to ask health-related questions in the morning, health-related information can be prioritized during the morning hours. Similarly, if a user seeks information to help them relax at night, relaxation-related information can be provided during the evening hours. Furthermore, if a user tends to plan trips on weekends, travel-related information can be provided on weekends. This allows for information delivery tailored to the user's lifestyle, resulting in more effective support.

[0084] Entertainment AI services not only provide answers and advice to user questions and requests, but can also estimate the user's emotions and adjust how information is displayed based on those emotions. For example, if the user is nervous, a simple and easy-to-read display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting how information is displayed according to the user's emotions, it becomes possible to provide more appropriate information.

[0085] Entertainment AI services can not only provide answers and advice to user questions and requests, but also incorporate personalization features based on the user's past usage history. For example, they can suggest new recipes based on recipes the user has enjoyed using in the past. They can also suggest next travel destinations based on information about places the user has visited in the past. Furthermore, they can provide information tailored to the time of day the user has used the service in the past. This allows for the provision of more personalized services by leveraging the user's past usage history.

[0086] Entertainment AI services not only provide answers and advice to user questions and requests, but can also estimate the user's emotions and prioritize information based on those emotions. For example, if a user needs urgent information, it will be provided with priority. If the user is relaxed, it can be provided with normal priority. Furthermore, if the user is feeling anxious, it can be provided quickly. This allows for more appropriate information delivery by prioritizing information according to the user's emotions.

[0087] Entertainment AI services can not only provide answers and advice to user questions and requests, but also offer highly relevant information by considering the user's geographical location. For example, if a user is in a specific region, it can prioritize providing information related to that region. If a user is traveling, it can prioritize providing information related to their travel destination. Furthermore, if a user is at home, it can prioritize providing information relevant to their home. This allows for the provision of highly relevant information by considering the user's geographical location.

[0088] Entertainment AI services not only provide answers and advice to user questions and requests, but can also estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. If the user is excited, it can provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, it can provide more appropriate analysis results.

[0089] Entertainment AI services can not only provide answers and advice to user questions and requests, but also analyze users' social media activity to provide relevant information. For example, if a user posts about health on social media, it can prioritize providing health-related information. If a user posts about travel, it can prioritize providing travel-related information. Similarly, if a user posts about cooking, it can prioritize providing cooking-related information. In this way, by analyzing a user's social media activity, it can prioritize providing relevant information.

[0090] Entertainment AI services not only provide answers and advice to user questions and requests, but can also estimate the user's emotions and adjust the length of the analysis based on those emotions. For example, if the user is in a hurry, a short, to-the-point analysis can be performed. If the user is relaxed, a detailed analysis can be performed. If the user is excited, a visually stimulating analysis can be performed. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The reception desk receives user questions and requests. These questions and requests may include, for example, recipes for healthy meals or recommendations for places to visit on a future trip. The reception desk can receive user-entered questions and requests as text data, as well as via voice input. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of receiving questions and requests based on those emotions. Step 2: The analysis unit uses a generation AI to analyze the questions and requests received by the reception unit and generate appropriate answers and advice. The analysis unit can, for example, use a text generation AI (e.g., LLM) to generate answers and advice to user questions and requests. It can also use natural language processing to analyze questions and requests and extract appropriate information from the database. Step 3: The service provider provides the answers and advice generated by the analysis unit. For example, the service provider displays the answers and advice generated by the generation AI on the user's smartphone or personal computer. It can also provide the user with recipes or travel destination information suggested by the generation AI.

[0093] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0094] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0095] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0096] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can receive user questions and requests using the reception device 38 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the questions and requests using generation AI and generates appropriate answers and advice. The provision unit can provide the generated answers and advice to the user using the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0103] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0104] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0105] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0106] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0107] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0109] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0111] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can receive user questions and requests using the microphone 238 of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the questions and requests using generation AI and generates appropriate answers and advice. The provision unit can provide the generated answers and advice to the user using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can receive user questions and requests using the microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes questions and requests using generation AI and generates appropriate answers and advice. The provision unit can provide the generated answers and advice to the user using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] As shown in Figure 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0137] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit can receive user questions and requests using the microphone 238 of the robot 414. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the questions and requests using generation AI and generates appropriate answers and advice. The provision unit can provide the generated answers and advice to the user using the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0146] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0148] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0149] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0150] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0154] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0155] 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.

[0156] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0157] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0158] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0159] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0161] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0162] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0163] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0164] (Note 1) A reception desk that handles user questions and requests, An analysis unit analyzes questions and requests received by the reception unit and generates appropriate answers and advice. The system includes a providing unit that provides answers and advice generated by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Using generative AI, we analyze questions and requests and generate appropriate answers and advice. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, The AI ​​generates answers and advice which are then displayed on the user's smartphone or computer. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving questions and requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving questions or requests, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and prioritizes the questions and requests it receives based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving questions and requests, the system prioritizes those that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving questions or requests, the system analyzes the user's social media activity and selects relevant questions or requests. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During the analysis, the level of detail of the analysis is adjusted based on the importance of the questions and requests. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the question or request. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when questions and requests were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the questions and requests. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing the service, the system will select the optimal display method by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the service, the information will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, we will consider the user's geographical location to provide the most suitable information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that handles user questions and requests, An analysis unit analyzes questions and requests received by the reception unit and generates appropriate answers and advice. The system includes a providing unit that provides answers and advice generated by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, Using generative AI, questions and requests are analyzed to generate appropriate answers and advice. The system according to feature 1.

3. The aforementioned supply unit is, The AI ​​generates answers and advice which are then displayed on the user's smartphone or computer. The system according to feature 1.

4. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving questions and requests based on those estimated emotions. The system according to feature 1.

5. The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system according to feature 1.

6. The aforementioned reception unit is When receiving questions or requests, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and prioritizes the questions and requests it receives based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When receiving questions and requests, the system prioritizes those that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned reception unit is When receiving questions or requests, the system analyzes the user's social media activity and selects relevant questions or requests. The system according to feature 1.

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