system

Abstract

The system according to this embodiment aims to provide personalized reservations and content based on user information. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a reservation unit, and a provision unit. The reception unit receives information from the user. The analysis unit analyzes the information received by the reception unit. The reservation unit makes reservations based on the information analyzed by the analysis unit. The provision unit provides personalized content based on the content reserved by the reservation 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 in response 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 prior art, personalized reservations and content provision based on user information have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized reservations and content based on user information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a reservation unit, and a provision unit. The reception unit receives information from the user. The analysis unit analyzes the information received by the reception unit. The reservation unit makes reservations based on the information analyzed by the analysis unit. The provision unit provides personalized content based on the content reserved by the reservation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized reservations and content based on user information. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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) The system according to an embodiment of the present invention is a system that uses an AI agent to transform the content that customers use on a daily basis into a conversational format. In this system, the user inputs information such as travel destination and planned dates, and the AI ​​agent analyzes this information to make reservations for the most suitable flights, hotels, drivers, restaurants, etc. Furthermore, the AI ​​agent personalizes articles based on the user's preferences and provides the most suitable sponsored content. For example, if the user inputs "I want to book a flight from Tokyo to Osaka," the AI ​​agent searches for the most suitable flight and makes the reservation. Also, if the user inputs "I want to book dinner in Osaka," the AI ​​agent searches for the most suitable restaurant based on the user's preferences and makes the reservation. This system allows the user to enjoy a UX where everything for their daily life can be done on a single screen. For example, if the user inputs "Tell me some news topics that might be useful for work today" during their commute, the AI ​​agent will provide the most suitable news. Also, if the user inputs "Tell me some clothes that would suit my girlfriend" while shopping, the AI ​​agent will suggest the most suitable clothes and provide a purchase link. Furthermore, the AI ​​agent automatically generates article headlines, text, and image cropping based on the user's attributes and preferences, transforming the news into something more engaging. By accumulating further learning data through the exchange of explanations and commentaries on articles, the accuracy of the generating AI is improved. In this way, using the AI ​​agent makes customers' daily lives more convenient and efficient, while also creating new revenue opportunities for companies. As a result, the system can make customers' daily lives more convenient and efficient, and provide new revenue opportunities for companies.

[0029] The system according to this embodiment comprises a reception unit, an analysis unit, a reservation unit, and a provision unit. The reception unit receives information from the user. User information includes, but is not limited to, text information, image information, and audio information. The reception unit receives, for example, text information through an input form. The reception unit can also receive image information through an upload function. Furthermore, the reception unit can also receive audio information through a microphone. For example, the reception unit transmits the text information entered by the user to the analysis unit. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using, for example, data mining techniques. Furthermore, the analysis unit can also analyze text information using natural language processing techniques. Furthermore, the analysis unit can also analyze image information using image analysis techniques. For example, the analysis unit analyzes the text information entered by the user and extracts information for making optimal reservations for flights, hotels, drivers, restaurants, etc. The reservation unit makes reservations based on the information analyzed by the analysis unit. The reservation unit makes reservations using, for example, a flight reservation system. Furthermore, the reservation department can also book hotels through the hotel reservation system. In addition, the reservation department can book drivers through the driver reservation system. For example, the reservation department searches for and books the most suitable flight based on information extracted by the analysis department. The delivery department provides personalized content based on the reservations made by the reservation department. For example, the delivery department automatically generates article headlines, text, and image cropping based on the user's past behavior history and preferences. The delivery department can also provide optimal sponsored content based on the user's attributes. Furthermore, the delivery department can personalize news articles based on the user's preferences. For example, the delivery department automatically selects and provides news articles that the user is likely to be interested in. In this way, the system can improve user convenience by receiving and analyzing user information, making reservations, and providing personalized content.

[0030] The reception unit receives information from users. This information may include, but is not limited to, text information, image information, and audio information. For example, the reception unit receives text information through an input form. The input form is placed on a web page or mobile application and is designed to be easily accessible to users. Users can send information to the reception unit by entering the required information into the form and pressing the submit button. The reception unit can also receive image information through an upload function. The upload function allows users to select and send image files from their devices via drag-and-drop or a file selection dialog. Furthermore, the reception unit can also receive audio information via a microphone. Receiving audio information requires microphone access permissions for the web browser or mobile application, allowing users to record and send audio using the microphone. For example, the reception unit sends text information entered by the user to the analysis unit. The reception unit formats the received information appropriately and performs preprocessing to enable the analysis unit to process it efficiently. This includes normalizing text information, resizing image information, and denoising audio information. This allows the reception unit to efficiently receive user information in various formats and transmit it to the analysis unit.

[0031] The analysis unit analyzes information received by the reception unit. For example, the analysis unit uses data mining techniques to analyze the information. Data mining techniques are methods for extracting useful patterns and relationships from large amounts of data, and are useful for extracting important features from user input information. The analysis unit can also analyze text information using natural language processing techniques. Natural language processing techniques are for understanding text data and analyzing its meaning, and can accurately grasp the intentions and requests from the text information entered by the user. Furthermore, the analysis unit can also analyze image information using image analysis techniques. Image analysis techniques are for extracting features from image data and understanding its content, and are useful for extracting necessary information from images uploaded by the user. For example, the analysis unit analyzes text information entered by the user and extracts information for making optimal reservations for flights, hotels, drivers, restaurants, etc. The analysis unit understands the user's requests and generates information to provide appropriate services based on those requests. This includes personalized analysis that takes into account the user's preferences and past behavioral history. The analysis unit is required to process information in real time and generate results quickly. This allows the analysis unit to respond quickly and accurately to user requests.

[0032] The reservation department makes reservations based on information analyzed by the analysis department. For example, the reservation department makes reservations through a flight reservation system. The flight reservation system is linked to airline databases and can obtain real-time seat availability and fare information. The reservation department searches for the most suitable flight based on the user's desired date, time, and destination and makes the reservation. The reservation department can also make hotel reservations through a hotel reservation system. The hotel reservation system is linked to databases of multiple hotel chains and accommodations, and can search for and reserve accommodations that meet the user's desired conditions. Furthermore, the reservation department can also reserve drivers through a driver reservation system. The driver reservation system is linked to databases of taxi companies and rideshare services, and can search for and reserve the most suitable driver based on the user's current location and destination. For example, the reservation department searches for the most suitable flight based on information extracted by the analysis department and makes the reservation. The reservation department can make multiple reservations simultaneously according to the user's requests, improving user convenience. The reservation department also provides functions such as reservation confirmation, modification, and cancellation, allowing users to flexibly manage their reservations. This enables the reservation department to respond to user requests quickly and accurately.

[0033] The content delivery department provides personalized content based on reservations made by the reservation department. For example, the delivery department automatically generates article headlines, text, and image cropping based on the user's past browsing history and preferences. The delivery department analyzes user attributes and past browsing history to select content that the user is likely to be interested in. For example, it automatically selects and provides highly relevant articles based on articles the user has previously viewed and searched. The delivery department can also provide optimal sponsored content based on user attributes. Sponsored content is content provided by advertisers and is displayed based on the user's interests. The delivery department analyzes user attributes and browsing history to display sponsored content at the optimal time. Furthermore, the delivery department can personalize news articles based on user preferences. For example, the delivery department automatically selects and provides news articles that the user is likely to be interested in. The delivery department customizes the headlines and content of news articles to match the user's preferences and provides them in a format that is easy for the user to engage with. In this way, the delivery department can provide personalized content based on the user's interests and preferences, thereby improving user satisfaction. Furthermore, the service provider can collect user feedback and continuously improve the quality and delivery methods of the content. This allows the service provider to respond quickly and accurately to user requests.

[0034] The reception unit can receive information such as travel destinations and planned dates. For example, the reception unit can receive the name of a travel destination and planned dates entered by the user. The reception unit can also receive the name of a travel destination and planned dates selected by the user. Furthermore, the reception unit can receive the name of a travel destination and planned dates entered by the user by voice. For example, the reception unit sends the name of the travel destination entered by the user to the analysis unit. In this way, the reception unit can support the user's travel planning by receiving information such as travel destinations and planned dates. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the name of the travel destination entered by the user into a generation AI and have the generation AI perform analysis of the name of the travel destination.

[0035] The analysis unit analyzes the information received by the reception unit and can make optimal reservations for flights, hotels, drivers, restaurants, etc. For example, the analysis unit can analyze the destination name and planned dates entered by the user and search for the best flight. The analysis unit can also analyze the destination name and planned dates entered by the user and search for the best hotel. Furthermore, the analysis unit can analyze the destination name and planned dates entered by the user and search for the best driver. For example, the analysis unit can analyze the destination name and planned dates entered by the user and search for the best restaurant. In this way, the analysis unit can improve user convenience by analyzing the received information and making optimal reservations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the destination name and planned dates entered by the user into a generating AI and have the generating AI execute reservations for optimal flights, hotels, drivers, restaurants, etc.

[0036] The reservation unit can make reservations based on information analyzed by the analysis unit. For example, the reservation unit can reserve the optimal flight based on the information analyzed by the analysis unit. The reservation unit can also reserve the optimal hotel based on the information analyzed by the analysis unit. Furthermore, the reservation unit can reserve the optimal driver based on the information analyzed by the analysis unit. For example, the reservation unit can reserve the optimal restaurant based on the information analyzed by the analysis unit. In this way, the reservation unit can improve user convenience by making reservations based on analyzed information. Some or all of the above processes in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the information analyzed by the analysis unit into a generating AI and have the generating AI execute reservations for optimal flights, hotels, drivers, restaurants, etc.

[0037] The service provider can provide personalized content based on the reservations made by the reservation provider. For example, the service provider can provide the user with the most suitable travel guide based on the flight information reserved by the reservation provider. The service provider can also provide the user with the most suitable sightseeing spots based on the hotel information reserved by the reservation provider. Furthermore, the service provider can provide the user with the most suitable mode of transportation based on the driver information reserved by the reservation provider. For example, the service provider can provide the user with the most suitable menu based on the restaurant information reserved by the reservation provider. In this way, the service provider can improve user convenience by providing personalized content based on the reservations made. 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 reservation details made by the reservation provider into a generation AI and have the generation AI perform the generation of personalized content.

[0038] The service provider can automatically generate article headlines, text, and image cropping based on user attributes and preferences. For example, the service provider can automatically generate article headlines based on the user's age and gender. It can also automatically generate article text based on the user's past browsing history. Furthermore, it can automatically generate image cropping based on user preferences. For example, the service provider can generate article headlines based on user attributes and preferences and provide them to the user. In this way, the service provider can provide content that will interest the user by automatically generating content based on the user's attributes and preferences. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user attributes and preferences into a generation AI and have the generation AI perform the generation of article headlines, text, and image cropping.

[0039] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display travel destinations and scheduled dates that the user has frequently entered in the past as suggestions. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest travel destinations and scheduled dates to be used during specific time periods based on the user's past input history. For example, the reception unit sends the travel destinations and scheduled dates that the user has entered in the past to the analysis unit. This allows the reception unit to analyze the past input history and suggest the optimal input method to the user. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past input history into a generating AI and have the generating AI suggest the optimal input method.

[0040] The reception unit can simplify input by automatically acquiring the user's current location information when they enter their travel destination and planned dates. For example, when a user opens the app, the reception unit automatically acquires their current location and sets it as the departure point. The reception unit can also suggest optimal destinations by considering the distance from the current location when the user enters their travel destination. Furthermore, if the user uses the app while traveling, the reception unit can update their current location in real time and reflect it as the departure point. For example, the reception unit sends the user's entered current location information to the analysis unit. This allows the reception unit to simplify input by automatically acquiring the current location information. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's current location information into a generating AI and have the generating AI perform the input simplification.

[0041] The reception unit can automatically suggest potential destinations based on the user's past travel history when the user enters their travel destination and planned dates. For example, the reception unit can automatically display places the user has frequently visited in the past as potential destinations. The reception unit can also predict places the user will visit on specific days of the week or at specific times and suggest them as potential destinations. Furthermore, the reception unit can analyze the user's past travel patterns and suggest the most suitable potential destinations. For example, the reception unit sends the user's entered past travel history to the analysis unit. This allows the reception unit to automatically suggest potential destinations based on the past travel history. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's past travel history into a generating AI and have the generating AI perform the automatic suggestion of potential destinations.

[0042] The reception unit can refer to the user's calendar information when they input their travel destination and planned dates, and make suggestions based on those plans. For example, the reception unit can refer to the events registered in the user's calendar and automatically set the travel destination and planned dates. The reception unit can also suggest locations related to specific events as candidate destinations based on the user's calendar information. Furthermore, the reception unit can suggest the optimal route tailored to the plan based on the user's calendar information. For example, the reception unit sends the calendar information entered by the user to the analysis unit. This allows the reception unit to make suggestions based on the plan by referring to the calendar information. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's calendar information into a generating AI and have the generating AI execute suggestions based on the plan.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on important information and provide multiple options. Alternatively, it can perform a simplified analysis on less important information and provide only one optimal option. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information, prioritizing the analysis of important information. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the information entered by the user. This allows the analysis unit to improve the efficiency of the analysis by adjusting the level of detail based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information entered by the user into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, for flight information, the analysis unit can apply an algorithm to find the best flight. Similarly, for hotel information, it can apply an algorithm to find the best hotel. Furthermore, for restaurant information, it can apply an algorithm to find the best restaurant. For example, the analysis unit applies different analysis algorithms depending on the category of information entered by the user. This allows the analysis unit to improve the accuracy of the analysis by applying different analysis algorithms depending on the category of information. 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 category of information entered by the user into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit can prioritize the analysis of the latest information and provide results quickly. The analysis unit can also lower the priority of analysis for older information and postpone it. Furthermore, the analysis unit can adjust the analysis schedule according to the timing of information submission to perform analysis efficiently. For example, the analysis unit can determine the priority of analysis based on the timing of information submission entered by the user. This allows the analysis unit to improve the efficiency of analysis by determining the priority of analysis based on the timing of information submission. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of information submission entered by the user into a generating AI and have the generating AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information and provide results quickly. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the analysis schedule according to the relevance of the information to perform the analysis efficiently. For example, the analysis unit can adjust the order of analysis based on the relevance of the information entered by the user. In this way, the analysis unit can improve the efficiency of the analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the information entered by the user into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The reservation unit can select the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation unit can suggest the optimal reservation method based on the reservation method the user has used in the past. The reservation unit can also suggest a reservation method that avoids congestion based on the user's past reservation history. Furthermore, the reservation unit can analyze the user's past reservation history and suggest the most efficient reservation method. For example, the reservation unit selects the optimal reservation method based on the past reservation history entered by the user. In this way, the reservation unit can select the optimal reservation method by referring to past reservation history. Some or all of the above processes in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input the user's past reservation history into a generating AI and have the generating AI perform the selection of the optimal reservation method.

[0048] The reservation system can customize the reservation process based on the user's current situation at the time of booking. For example, if the user is on the go, the reservation system can provide a reservation method optimized for mobile devices. It can also provide a reservation method optimized for large screens if the user is using a desktop computer. Furthermore, if the user is in a hurry, the reservation system can provide a simple reservation method for quick booking. For example, the reservation system customizes the reservation process based on the user's current situation. This allows the reservation system to improve user convenience by customizing the reservation process based on the current situation. Some or all of the above processes in the reservation system may be performed using AI, for example, or not. For example, the reservation system can input the user's current situation into a generating AI and have the generating AI perform the customization of the reservation process.

[0049] The reservation unit can select the optimal reservation method by considering the user's geographical location information at the time of reservation. For example, the reservation unit may prioritize reservations for locations close to the user's current location. Furthermore, if the user is in a specific region, the reservation unit can provide the most suitable reservation method for that region. Additionally, if the user is on the move, the reservation unit can provide the most suitable reservation method for their destination. For example, the reservation unit selects the optimal reservation method based on the geographical location information entered by the user. This allows the reservation unit to select the optimal reservation method by considering geographical location information. Some or all of the above processing in the reservation unit may be performed using AI, or not. For example, the reservation unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal reservation method.

[0050] The reservation unit can analyze the user's social media activity and suggest reservation methods at the time of reservation. For example, the reservation unit can suggest the optimal reservation method based on places the user has shared on social media. The reservation unit can also predict the user's preferred places from their social media activity and suggest reservation methods. Furthermore, the reservation unit can analyze the user's social media activity and suggest reservation methods based on trends. For example, the reservation unit can suggest reservation methods based on the social media activity entered by the user. In this way, the reservation unit can suggest reservation methods based on the user's preferences by analyzing social media activity. Some or all of the above processing in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input the user's social media activity into a generating AI and have the generating AI perform the task of suggesting reservation methods.

[0051] The content delivery unit can provide the most suitable content by referring to the user's past content usage history at the time of delivery. For example, the delivery unit can suggest the most suitable content based on the content the user has frequently used in the past. The delivery unit can also predict and provide the user's preferred content based on their past content usage history. Furthermore, the delivery unit can analyze the user's past content usage history and provide the content that is of most interest to them. For example, the delivery unit can provide the most suitable content based on the past content usage history entered by the user. In this way, the delivery unit can provide the most suitable content to the user by referring to their past content usage history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past content usage history into a generating AI and have the generating AI perform the task of providing the most suitable content.

[0052] The service provider can customize the content based on the user's current situation at the time of delivery. For example, if the user is on the move, the service provider can provide content optimized for a mobile device. Alternatively, if the user is using a desktop, the service provider can provide content optimized for a large screen. Furthermore, if the user is in a hurry, the service provider can provide concise content to allow for quick access to information. For example, the service provider can customize the content based on the user's current situation. This allows the service provider to improve user convenience by customizing the content based on the current situation. 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 current situation into a generating AI and have the generating AI perform the content customization.

[0053] The content provider can provide optimal content by considering the user's geographical location information at the time of delivery. For example, the provider can prioritize providing content related to locations close to the user's current location. Furthermore, if the user is in a specific region, the provider can provide content related to that region. Additionally, if the user is on the move, the provider can provide content related to their destination. For example, the provider can provide optimal content based on the geographical location information entered by the user. This allows the provider to provide optimal content to the user by considering geographical location information. Some or all of the above processing in the content provider may be performed using AI, for example, or without AI. For example, the provider can input the user's geographical location information into a generating AI and have the generating AI provide optimal content.

[0054] The content provider can analyze the user's social media activity and suggest content at the time of delivery. For example, the provider can suggest the most suitable content based on what the user has shared on social media. The provider can also predict and provide content that the user will like based on their social media activity. Furthermore, the provider can analyze the user's social media activity and provide content based on trends. For example, the provider can suggest content based on the social media activity entered by the user. In this way, the provider can provide content based on the user's preferences by analyzing social media activity. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's social media activity into a generating AI and have the generating AI suggest content.

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

[0056] The reception desk can analyze a user's past travel history and suggest optimal travel destinations and dates based on places the user has visited and services they have used in the past. For example, it can display cities and regions the user has frequently visited in the past as options. The reception desk can also suggest similar services based on information about flights and hotels the user has used in the past. Furthermore, the reception desk can suggest travel destinations related to specific seasons or events based on the user's past travel history. In this way, the reception desk can provide more personalized suggestions for travel destinations and dates by utilizing the user's past travel history.

[0057] The booking system can suggest the most suitable booking method based on the user's current location. For example, it can prioritize suggesting flights and hotels close to the user's current location. Furthermore, if the user is in a specific region, it can suggest the best restaurants and drivers for that area. Additionally, if the user is on the move, it can suggest the most suitable booking method for their destination. In this way, the booking system can suggest more appropriate booking methods by utilizing the user's current location information.

[0058] The content delivery department can analyze a user's past content viewing history and suggest the most suitable content based on the content the user has previously been interested in. For example, it can suggest similar content based on news articles and videos the user has frequently viewed in the past. It can also suggest content related to specific topics based on the user's past content viewing history. Furthermore, it can analyze the user's past content viewing history and suggest new content that the user might be interested in. In this way, the content delivery department can provide more personalized content by utilizing the user's past content viewing history.

[0059] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on important information and provide multiple options. Conversely, it can perform a simplified analysis on less important information and provide only one optimal option. Furthermore, it can determine the priority of the analysis according to the importance of the information, prioritizing the analysis of important information. In this way, the analysis unit can improve the efficiency of the analysis by adjusting the level of detail based on the importance of the information.

[0060] The service provider can customize content based on the user's current situation. For example, if the user is on the go, they can provide content optimized for mobile devices. If the user is using a desktop, they can provide content optimized for larger screens. Furthermore, if the user is in a hurry, they can provide concise content to allow for quick access to information. This allows the service provider to improve user convenience by customizing content based on the user's current situation.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives information from the user. This information includes text, images, and audio. The reception desk can receive text information through an input form, image information through an upload function, and audio information through a microphone. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit can analyze text information using data mining and natural language processing technologies, and image information using image analysis technologies. For example, the analysis unit analyzes the text information entered by the user and extracts information for making optimal reservations for flights, hotels, drivers, restaurants, etc. Step 3: The reservation department makes reservations based on the information analyzed by the analysis department. The reservation department can make reservations for flights through the flight reservation system, hotels through the hotel reservation system, or drivers through the driver reservation system. For example, the reservation department searches for the optimal flight and makes a reservation based on the information extracted by the analysis department. Step 4: The delivery department provides personalized content based on the reservations made by the reservation department. The delivery department automatically generates article headlines, text, and image cropping based on the user's past behavior history and preferences. The delivery department can also provide the most suitable sponsored content based on the user's attributes. Furthermore, the delivery department can personalize news articles based on the user's preferences. For example, the delivery department can automatically select and provide news articles that the user is likely to be interested in.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a system that uses an AI agent to transform the content that customers use on a daily basis into a conversational format. In this system, the user inputs information such as travel destination and planned dates, and the AI ​​agent analyzes this information to make reservations for the most suitable flights, hotels, drivers, restaurants, etc. Furthermore, the AI ​​agent personalizes articles based on the user's preferences and provides the most suitable sponsored content. For example, if the user inputs "I want to book a flight from Tokyo to Osaka," the AI ​​agent searches for the most suitable flight and makes the reservation. Also, if the user inputs "I want to book dinner in Osaka," the AI ​​agent searches for the most suitable restaurant based on the user's preferences and makes the reservation. This system allows the user to enjoy a UX where everything for their daily life can be done on a single screen. For example, if the user inputs "Tell me some news topics that might be useful for work today" during their commute, the AI ​​agent will provide the most suitable news. Also, if the user inputs "Tell me some clothes that would suit my girlfriend" while shopping, the AI ​​agent will suggest the most suitable clothes and provide a purchase link. Furthermore, the AI ​​agent automatically generates article headlines, text, and image cropping based on the user's attributes and preferences, transforming the news into something more engaging. By accumulating further learning data through the exchange of explanations and commentaries on articles, the accuracy of the generating AI is improved. In this way, using the AI ​​agent makes customers' daily lives more convenient and efficient, while also creating new revenue opportunities for companies. As a result, the system can make customers' daily lives more convenient and efficient, and provide new revenue opportunities for companies.

[0064] The system according to this embodiment comprises a reception unit, an analysis unit, a reservation unit, and a provision unit. The reception unit receives information from the user. User information includes, but is not limited to, text information, image information, and audio information. The reception unit receives, for example, text information through an input form. The reception unit can also receive image information through an upload function. Furthermore, the reception unit can also receive audio information through a microphone. For example, the reception unit transmits the text information entered by the user to the analysis unit. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using, for example, data mining techniques. Furthermore, the analysis unit can also analyze text information using natural language processing techniques. Furthermore, the analysis unit can also analyze image information using image analysis techniques. For example, the analysis unit analyzes the text information entered by the user and extracts information for making optimal reservations for flights, hotels, drivers, restaurants, etc. The reservation unit makes reservations based on the information analyzed by the analysis unit. The reservation unit makes reservations using, for example, a flight reservation system. Furthermore, the reservation department can also book hotels through the hotel reservation system. In addition, the reservation department can book drivers through the driver reservation system. For example, the reservation department searches for and books the most suitable flight based on information extracted by the analysis department. The delivery department provides personalized content based on the reservations made by the reservation department. For example, the delivery department automatically generates article headlines, text, and image cropping based on the user's past behavior history and preferences. The delivery department can also provide optimal sponsored content based on the user's attributes. Furthermore, the delivery department can personalize news articles based on the user's preferences. For example, the delivery department automatically selects and provides news articles that the user is likely to be interested in. In this way, the system can improve user convenience by receiving and analyzing user information, making reservations, and providing personalized content.

[0065] The reception unit receives information from users. This information may include, but is not limited to, text information, image information, and audio information. For example, the reception unit receives text information through an input form. The input form is placed on a web page or mobile application and is designed to be easily accessible to users. Users can send information to the reception unit by entering the required information into the form and pressing the submit button. The reception unit can also receive image information through an upload function. The upload function allows users to select and send image files from their devices via drag-and-drop or a file selection dialog. Furthermore, the reception unit can also receive audio information via a microphone. Receiving audio information requires microphone access permissions for the web browser or mobile application, allowing users to record and send audio using the microphone. For example, the reception unit sends text information entered by the user to the analysis unit. The reception unit formats the received information appropriately and performs preprocessing to enable the analysis unit to process it efficiently. This includes normalizing text information, resizing image information, and denoising audio information. This allows the reception unit to efficiently receive user information in various formats and transmit it to the analysis unit.

[0066] The analysis unit analyzes information received by the reception unit. For example, the analysis unit uses data mining techniques to analyze the information. Data mining techniques are methods for extracting useful patterns and relationships from large amounts of data, and are useful for extracting important features from user input information. The analysis unit can also analyze text information using natural language processing techniques. Natural language processing techniques are for understanding text data and analyzing its meaning, and can accurately grasp the intentions and requests from the text information entered by the user. Furthermore, the analysis unit can also analyze image information using image analysis techniques. Image analysis techniques are for extracting features from image data and understanding its content, and are useful for extracting necessary information from images uploaded by the user. For example, the analysis unit analyzes text information entered by the user and extracts information for making optimal reservations for flights, hotels, drivers, restaurants, etc. The analysis unit understands the user's requests and generates information to provide appropriate services based on those requests. This includes personalized analysis that takes into account the user's preferences and past behavioral history. The analysis unit is required to process information in real time and generate results quickly. This allows the analysis unit to respond quickly and accurately to user requests.

[0067] The reservation department makes reservations based on information analyzed by the analysis department. For example, the reservation department makes reservations through a flight reservation system. The flight reservation system is linked to airline databases and can obtain real-time seat availability and fare information. The reservation department searches for the most suitable flight based on the user's desired date, time, and destination and makes the reservation. The reservation department can also make hotel reservations through a hotel reservation system. The hotel reservation system is linked to databases of multiple hotel chains and accommodations, and can search for and reserve accommodations that meet the user's desired conditions. Furthermore, the reservation department can also reserve drivers through a driver reservation system. The driver reservation system is linked to databases of taxi companies and rideshare services, and can search for and reserve the most suitable driver based on the user's current location and destination. For example, the reservation department searches for the most suitable flight based on information extracted by the analysis department and makes the reservation. The reservation department can make multiple reservations simultaneously according to the user's requests, improving user convenience. The reservation department also provides functions such as reservation confirmation, modification, and cancellation, allowing users to flexibly manage their reservations. This enables the reservation department to respond to user requests quickly and accurately.

[0068] The content delivery department provides personalized content based on reservations made by the reservation department. For example, the delivery department automatically generates article headlines, text, and image cropping based on the user's past browsing history and preferences. The delivery department analyzes user attributes and past browsing history to select content that the user is likely to be interested in. For example, it automatically selects and provides highly relevant articles based on articles the user has previously viewed and searched. The delivery department can also provide optimal sponsored content based on user attributes. Sponsored content is content provided by advertisers and is displayed based on the user's interests. The delivery department analyzes user attributes and browsing history to display sponsored content at the optimal time. Furthermore, the delivery department can personalize news articles based on user preferences. For example, the delivery department automatically selects and provides news articles that the user is likely to be interested in. The delivery department customizes the headlines and content of news articles to match the user's preferences and provides them in a format that is easy for the user to engage with. In this way, the delivery department can provide personalized content based on the user's interests and preferences, thereby improving user satisfaction. Furthermore, the service provider can collect user feedback and continuously improve the quality and delivery methods of the content. This allows the service provider to respond quickly and accurately to user requests.

[0069] The reception unit can receive information such as travel destinations and planned dates. For example, the reception unit can receive the name of a travel destination and planned dates entered by the user. The reception unit can also receive the name of a travel destination and planned dates selected by the user. Furthermore, the reception unit can receive the name of a travel destination and planned dates entered by the user by voice. For example, the reception unit sends the name of the travel destination entered by the user to the analysis unit. In this way, the reception unit can support the user's travel planning by receiving information such as travel destinations and planned dates. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the name of the travel destination entered by the user into a generation AI and have the generation AI perform analysis of the name of the travel destination.

[0070] The analysis unit analyzes the information received by the reception unit and can make optimal reservations for flights, hotels, drivers, restaurants, etc. For example, the analysis unit can analyze the destination name and planned dates entered by the user and search for the best flight. The analysis unit can also analyze the destination name and planned dates entered by the user and search for the best hotel. Furthermore, the analysis unit can analyze the destination name and planned dates entered by the user and search for the best driver. For example, the analysis unit can analyze the destination name and planned dates entered by the user and search for the best restaurant. In this way, the analysis unit can improve user convenience by analyzing the received information and making optimal reservations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the destination name and planned dates entered by the user into a generating AI and have the generating AI execute reservations for optimal flights, hotels, drivers, restaurants, etc.

[0071] The reservation unit can make reservations based on information analyzed by the analysis unit. For example, the reservation unit can reserve the optimal flight based on the information analyzed by the analysis unit. The reservation unit can also reserve the optimal hotel based on the information analyzed by the analysis unit. Furthermore, the reservation unit can reserve the optimal driver based on the information analyzed by the analysis unit. For example, the reservation unit can reserve the optimal restaurant based on the information analyzed by the analysis unit. In this way, the reservation unit can improve user convenience by making reservations based on analyzed information. Some or all of the above processes in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the information analyzed by the analysis unit into a generating AI and have the generating AI execute reservations for optimal flights, hotels, drivers, restaurants, etc.

[0072] The service provider can provide personalized content based on the reservations made by the reservation provider. For example, the service provider can provide the user with the most suitable travel guide based on the flight information reserved by the reservation provider. The service provider can also provide the user with the most suitable sightseeing spots based on the hotel information reserved by the reservation provider. Furthermore, the service provider can provide the user with the most suitable mode of transportation based on the driver information reserved by the reservation provider. For example, the service provider can provide the user with the most suitable menu based on the restaurant information reserved by the reservation provider. In this way, the service provider can improve user convenience by providing personalized content based on the reservations made. 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 reservation details made by the reservation provider into a generation AI and have the generation AI perform the generation of personalized content.

[0073] The service provider can automatically generate article headlines, text, and image cropping based on user attributes and preferences. For example, the service provider can automatically generate article headlines based on the user's age and gender. It can also automatically generate article text based on the user's past browsing history. Furthermore, it can automatically generate image cropping based on user preferences. For example, the service provider can generate article headlines based on user attributes and preferences and provide them to the user. In this way, the service provider can provide content that will interest the user by automatically generating content based on the user's attributes and preferences. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user attributes and preferences into a generation AI and have the generation AI perform the generation of article headlines, text, and image cropping.

[0074] The reception unit can estimate the user's emotions and adjust the input method for travel destinations and itineraries based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the reception unit can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow for quick input of travel destinations and itineraries. For example, the reception unit sends the travel destinations and itineraries entered by the user to the analysis unit. This allows the reception unit to improve user convenience by adjusting the input method 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 unit may be performed using AI or not using AI. For example, the reception unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display travel destinations and scheduled dates that the user has frequently entered in the past as suggestions. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest travel destinations and scheduled dates to be used during specific time periods based on the user's past input history. For example, the reception unit sends the travel destinations and scheduled dates that the user has entered in the past to the analysis unit. This allows the reception unit to analyze the past input history and suggest the optimal input method to the user. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past input history into a generating AI and have the generating AI suggest the optimal input method.

[0076] The reception unit can simplify input by automatically acquiring the user's current location information when they enter their travel destination and planned dates. For example, when a user opens the app, the reception unit automatically acquires their current location and sets it as the departure point. The reception unit can also suggest optimal destinations by considering the distance from the current location when the user enters their travel destination. Furthermore, if the user uses the app while traveling, the reception unit can update their current location in real time and reflect it as the departure point. For example, the reception unit sends the user's entered current location information to the analysis unit. This allows the reception unit to simplify input by automatically acquiring the current location information. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's current location information into a generating AI and have the generating AI perform the input simplification.

[0077] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. For example, the reception unit sends the emotion data entered by the user to the analysis unit. This allows the reception unit to improve user convenience by adjusting the design of the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 unit may be performed using AI, or not. For example, the reception unit can input the user's emotion data into the generative AI and have the generative AI adjust the design of the input interface.

[0078] The reception unit can automatically suggest potential destinations based on the user's past travel history when the user enters their travel destination and planned dates. For example, the reception unit can automatically display places the user has frequently visited in the past as potential destinations. The reception unit can also predict places the user will visit on specific days of the week or at specific times and suggest them as potential destinations. Furthermore, the reception unit can analyze the user's past travel patterns and suggest the most suitable potential destinations. For example, the reception unit sends the user's entered past travel history to the analysis unit. This allows the reception unit to automatically suggest potential destinations based on the past travel history. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's past travel history into a generating AI and have the generating AI perform the automatic suggestion of potential destinations.

[0079] The reception unit can refer to the user's calendar information when they input their travel destination and planned dates, and make suggestions based on those plans. For example, the reception unit can refer to the events registered in the user's calendar and automatically set the travel destination and planned dates. The reception unit can also suggest locations related to specific events as candidate destinations based on the user's calendar information. Furthermore, the reception unit can suggest the optimal route tailored to the plan based on the user's calendar information. For example, the reception unit sends the calendar information entered by the user to the analysis unit. This allows the reception unit to make suggestions based on the plan by referring to the calendar information. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's calendar information into a generating AI and have the generating AI execute suggestions based on the plan.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide multiple options. If the user is in a hurry, the analysis unit can perform a rapid analysis and provide only one optimal option. Furthermore, if the user is excited, the analysis unit can provide visually easy-to-understand analysis results. For example, the analysis unit adjusts the analysis method based on the emotion data entered by the user. This allows the analysis unit to improve the accuracy of the analysis by adjusting the analysis method 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the analysis method.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on important information and provide multiple options. Alternatively, it can perform a simplified analysis on less important information and provide only one optimal option. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information, prioritizing the analysis of important information. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the information entered by the user. This allows the analysis unit to improve the efficiency of the analysis by adjusting the level of detail based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information entered by the user into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, for flight information, the analysis unit can apply an algorithm to find the best flight. Similarly, for hotel information, it can apply an algorithm to find the best hotel. Furthermore, for restaurant information, it can apply an algorithm to find the best restaurant. For example, the analysis unit applies different analysis algorithms depending on the category of information entered by the user. This allows the analysis unit to improve the accuracy of the analysis by applying different analysis algorithms depending on the category of information. 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 category of information entered by the user into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. For example, the analysis unit adjusts the display method of the analysis results based on the emotion data entered by the user. In this way, the analysis unit can improve user convenience by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above 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 emotion data into the generative AI and have the generative AI perform the adjustment of the display method of the analysis results.

[0084] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit can prioritize the analysis of the latest information and provide results quickly. The analysis unit can also lower the priority of analysis for older information and postpone it. Furthermore, the analysis unit can adjust the analysis schedule according to the timing of information submission to perform analysis efficiently. For example, the analysis unit can determine the priority of analysis based on the timing of information submission entered by the user. This allows the analysis unit to improve the efficiency of analysis by determining the priority of analysis based on the timing of information submission. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of information submission entered by the user into a generating AI and have the generating AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information and provide results quickly. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the analysis schedule according to the relevance of the information to perform the analysis efficiently. For example, the analysis unit can adjust the order of analysis based on the relevance of the information entered by the user. In this way, the analysis unit can improve the efficiency of the analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the information entered by the user into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The reservation system can estimate the user's emotions and adjust the reservation method based on those emotions. For example, if the user is relaxed, the reservation system can offer detailed reservation options and suggest a customizable reservation method. If the user is in a hurry, the reservation system can also offer a simple reservation method for quick booking. Furthermore, if the user is excited, the reservation system can offer a visually intuitive reservation method. For example, the reservation system can adjust the reservation method based on the emotion data entered by the user. This allows the reservation system to improve user convenience by adjusting the reservation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation system may be performed using AI or not. For example, the reservation system can input user emotion data into a generative AI and have the generative AI adjust the reservation method.

[0087] The reservation unit can select the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation unit can suggest the optimal reservation method based on the reservation method the user has used in the past. The reservation unit can also suggest a reservation method that avoids congestion based on the user's past reservation history. Furthermore, the reservation unit can analyze the user's past reservation history and suggest the most efficient reservation method. For example, the reservation unit selects the optimal reservation method based on the past reservation history entered by the user. In this way, the reservation unit can select the optimal reservation method by referring to past reservation history. Some or all of the above processes in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input the user's past reservation history into a generating AI and have the generating AI perform the selection of the optimal reservation method.

[0088] The reservation system can customize the reservation process based on the user's current situation at the time of booking. For example, if the user is on the go, the reservation system can provide a reservation method optimized for mobile devices. It can also provide a reservation method optimized for large screens if the user is using a desktop computer. Furthermore, if the user is in a hurry, the reservation system can provide a simple reservation method for quick booking. For example, the reservation system customizes the reservation process based on the user's current situation. This allows the reservation system to improve user convenience by customizing the reservation process based on the current situation. Some or all of the above processes in the reservation system may be performed using AI, for example, or not. For example, the reservation system can input the user's current situation into a generating AI and have the generating AI perform the customization of the reservation process.

[0089] The booking system can estimate the user's emotions and prioritize bookings based on those emotions. For example, if the user is feeling anxious, the system will prioritize important bookings. If the user is relaxed, the system can offer detailed booking options and suggest customizable booking methods. Furthermore, if the user is in a hurry, the system can provide a simple booking method for quick booking. For example, the system can prioritize bookings based on emotion data entered by the user. This allows the system to prioritize important bookings by determining booking priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking system may be performed using AI or not. For example, the booking system can input user emotion data into a generative AI and have the generative AI determine booking priorities.

[0090] The reservation unit can select the optimal reservation method by considering the user's geographical location information at the time of reservation. For example, the reservation unit may prioritize reservations for locations close to the user's current location. Furthermore, if the user is in a specific region, the reservation unit can provide the most suitable reservation method for that region. Additionally, if the user is on the move, the reservation unit can provide the most suitable reservation method for their destination. For example, the reservation unit selects the optimal reservation method based on the geographical location information entered by the user. This allows the reservation unit to select the optimal reservation method by considering geographical location information. Some or all of the above processing in the reservation unit may be performed using AI, or not. For example, the reservation unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal reservation method.

[0091] The reservation unit can analyze the user's social media activity and suggest reservation methods at the time of reservation. For example, the reservation unit can suggest the optimal reservation method based on places the user has shared on social media. The reservation unit can also predict the user's preferred places from their social media activity and suggest reservation methods. Furthermore, the reservation unit can analyze the user's social media activity and suggest reservation methods based on trends. For example, the reservation unit can suggest reservation methods based on the social media activity entered by the user. In this way, the reservation unit can suggest reservation methods based on the user's preferences by analyzing social media activity. Some or all of the above processing in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input the user's social media activity into a generating AI and have the generating AI perform the task of suggesting reservation methods.

[0092] The service provider can estimate the user's emotions and adjust the presentation of the content based on the estimated emotions. For example, if the user is relaxed, the service provider can provide content containing detailed information. If the user is in a hurry, the service provider can also provide concise content that gets straight to the point. Furthermore, if the user is excited, the service provider can provide content with visually stimulating effects. For example, the service provider can adjust the presentation of the content based on emotion data entered by the user. This allows the service provider to improve user convenience by adjusting the presentation of content 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the presentation of the content.

[0093] The content delivery unit can provide the most suitable content by referring to the user's past content usage history at the time of delivery. For example, the delivery unit can suggest the most suitable content based on the content the user has frequently used in the past. The delivery unit can also predict and provide the user's preferred content based on their past content usage history. Furthermore, the delivery unit can analyze the user's past content usage history and provide the content that is of most interest to them. For example, the delivery unit can provide the most suitable content based on the past content usage history entered by the user. In this way, the delivery unit can provide the most suitable content to the user by referring to their past content usage history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past content usage history into a generating AI and have the generating AI perform the task of providing the most suitable content.

[0094] The service provider can customize the content based on the user's current situation at the time of delivery. For example, if the user is on the move, the service provider can provide content optimized for a mobile device. Alternatively, if the user is using a desktop, the service provider can provide content optimized for a large screen. Furthermore, if the user is in a hurry, the service provider can provide concise content to allow for quick access to information. For example, the service provider can customize the content based on the user's current situation. This allows the service provider to improve user convenience by customizing the content based on the current situation. 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 current situation into a generating AI and have the generating AI perform the content customization.

[0095] The service provider can estimate the user's emotions and prioritize the content to be delivered based on those emotions. For example, if the user is stressed, the service provider will prioritize important content. If the user is relaxed, the service provider may also provide content containing detailed information. Furthermore, if the user is in a hurry, the service provider may provide concise content that gets straight to the point. For example, the service provider will prioritize content based on emotion data entered by the user. This allows the service provider to prioritize important content by prioritizing it 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the determination of content priorities.

[0096] The content provider can provide optimal content by considering the user's geographical location information at the time of delivery. For example, the provider can prioritize providing content related to locations close to the user's current location. Furthermore, if the user is in a specific region, the provider can provide content related to that region. Additionally, if the user is on the move, the provider can provide content related to their destination. For example, the provider can provide optimal content based on the geographical location information entered by the user. This allows the provider to provide optimal content to the user by considering geographical location information. Some or all of the above processing in the content provider may be performed using AI, for example, or without AI. For example, the provider can input the user's geographical location information into a generating AI and have the generating AI provide optimal content.

[0097] The content provider can analyze the user's social media activity and suggest content at the time of delivery. For example, the provider can suggest the most suitable content based on what the user has shared on social media. The provider can also predict and provide content that the user will like based on their social media activity. Furthermore, the provider can analyze the user's social media activity and provide content based on trends. For example, the provider can suggest content based on the social media activity entered by the user. In this way, the provider can provide content based on the user's preferences by analyzing social media activity. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's social media activity into a generating AI and have the generating AI suggest content.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can analyze a user's past travel history and suggest optimal travel destinations and dates based on places the user has visited and services they have used in the past. For example, it can display cities and regions the user has frequently visited in the past as options. The reception desk can also suggest similar services based on information about flights and hotels the user has used in the past. Furthermore, the reception desk can suggest travel destinations related to specific seasons or events based on the user's past travel history. In this way, the reception desk can provide more personalized suggestions for travel destinations and dates by utilizing the user's past travel history.

[0100] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is stressed, the analysis unit can perform a quick analysis and provide simple results. If the user is relaxed, the analysis unit can perform a detailed analysis and provide multiple options. Furthermore, if the user is excited, the analysis unit can provide visually easy-to-understand analysis results. In this way, the analysis unit can improve user convenience by adjusting the accuracy of the analysis according to the user's emotions.

[0101] The booking system can suggest the most suitable booking method based on the user's current location. For example, it can prioritize suggesting flights and hotels close to the user's current location. Furthermore, if the user is in a specific region, it can suggest the best restaurants and drivers for that area. Additionally, if the user is on the move, it can suggest the most suitable booking method for their destination. In this way, the booking system can suggest more appropriate booking methods by utilizing the user's current location information.

[0102] The service provider can estimate the user's emotions and adjust the format of the content provided based on those emotions. For example, if the user is relaxed, it can provide content with detailed information. If the user is in a hurry, it can provide concise content that gets straight to the point. Furthermore, if the user is excited, it can provide content with visually stimulating effects. In this way, the service provider can improve user convenience by adjusting the format of the content according to the user's emotions.

[0103] The content delivery department can analyze a user's past content viewing history and suggest the most suitable content based on the content the user has previously been interested in. For example, it can suggest similar content based on news articles and videos the user has frequently viewed in the past. It can also suggest content related to specific topics based on the user's past content viewing history. Furthermore, it can analyze the user's past content viewing history and suggest new content that the user might be interested in. In this way, the content delivery department can provide more personalized content by utilizing the user's past content viewing history.

[0104] The reception desk can estimate the user's emotions and adjust the input interface design based on those emotions. For example, if the user is stressed, it can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, it can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, it can provide a simple and highly visible interface to facilitate the input process. In this way, the reception desk can improve user convenience by adjusting the input interface design according to the user's emotions.

[0105] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on important information and provide multiple options. Conversely, it can perform a simplified analysis on less important information and provide only one optimal option. Furthermore, it can determine the priority of the analysis according to the importance of the information, prioritizing the analysis of important information. In this way, the analysis unit can improve the efficiency of the analysis by adjusting the level of detail based on the importance of the information.

[0106] The reservation system can estimate the user's emotions and adjust the reservation method based on those emotions. For example, if the user is relaxed, it can offer detailed reservation options and suggest a customizable reservation method. If the user is in a hurry, it can offer a simple reservation method for quick booking. Furthermore, if the user is excited, it can offer a visually easy-to-understand reservation method. In this way, the reservation system can improve user convenience by adjusting the reservation method according to the user's emotions.

[0107] The service provider can customize content based on the user's current situation. For example, if the user is on the go, they can provide content optimized for mobile devices. If the user is using a desktop, they can provide content optimized for larger screens. Furthermore, if the user is in a hurry, they can provide concise content to allow for quick access to information. This allows the service provider to improve user convenience by customizing content based on the user's current situation.

[0108] The service provider can estimate the user's emotions and prioritize the content they deliver based on those emotions. For example, if the user is stressed, important content can be prioritized. If the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, concise content that gets straight to the point can be provided. In this way, the service provider can prioritize important content by determining content priorities according to the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk receives information from the user. This information includes text, images, and audio. The reception desk can receive text information through an input form, image information through an upload function, and audio information through a microphone. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit can analyze text information using data mining and natural language processing technologies, and image information using image analysis technologies. For example, the analysis unit analyzes the text information entered by the user and extracts information for making optimal reservations for flights, hotels, drivers, restaurants, etc. Step 3: The reservation department makes reservations based on the information analyzed by the analysis department. The reservation department can make reservations for flights through the flight reservation system, hotels through the hotel reservation system, or drivers through the driver reservation system. For example, the reservation department searches for the optimal flight and makes a reservation based on the information extracted by the analysis department. Step 4: The delivery department provides personalized content based on the reservations made by the reservation department. The delivery department automatically generates article headlines, text, and image cropping based on the user's past behavior history and preferences. The delivery department can also provide the most suitable sponsored content based on the user's attributes. Furthermore, the delivery department can personalize news articles based on the user's preferences. For example, the delivery department can automatically select and provide news articles that the user is likely to be interested in.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, analysis unit, reservation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives text information, image information, and voice information from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The reservation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes a reservation based on the analyzed information. The provision unit is implemented by the control unit 46A of the smart device 14 and provides personalized content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, reservation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives text information, image information, and voice information from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The reservation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes a reservation based on the analyzed information. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides personalized content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, reservation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives text information, image information, and voice information from the user. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes a reservation based on the analyzed information. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides personalized content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the reception unit, analysis unit, reservation 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 is implemented by the control unit 46A of the robot 414 and receives text information, image information, and voice information from the user. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes a reservation based on the analyzed information. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides personalized content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A reception desk that receives information from users, An analysis unit that analyzes the information received by the reception unit, A reservation unit that makes a reservation based on the information analyzed by the aforementioned analysis unit, The system includes a provisioning unit that provides personalized content based on the content reserved by the reservation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept information such as travel destinations and planned dates. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The information received by the reception desk is analyzed to make optimal reservations for flights, hotels, drivers, restaurants, and other services. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reservation section is, Reservations are made based on the information analyzed by the aforementioned analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Personalized content is provided based on the content reserved by the aforementioned reservation department. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Automatically generates article headlines, text, and image cropping based on user attributes and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts how they input travel destinations and dates based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering travel destinations and planned dates, the system automatically retrieves the user's current location information to simplify input. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter their travel destination and planned dates, the system automatically suggests potential locations based on their past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter their travel destination and planned dates, the system references their calendar information to provide suggestions based on their schedule. 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 analysis method 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 analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reservation section is, It estimates the user's emotions and adjusts the booking method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reservation section is, When a reservation is made, the system will refer to the user's past reservation history to select the most suitable reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reservation section is, When making a reservation, customize the reservation method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reservation section is, When making a reservation, the system will select the most suitable reservation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reservation section is, When making a reservation, we analyze the user's social media activity and suggest a reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing content, we refer to the user's past content usage history to deliver the most suitable content. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing content, customize the content based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the content to be delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing content, we take the user's geographical location into consideration to deliver the most suitable content. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing content, we analyze the user's social media activity and suggest content accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 receives information from users, An analysis unit that analyzes the information received by the reception unit, A reservation unit that makes a reservation based on the information analyzed by the aforementioned analysis unit, The system includes a provisioning unit that provides personalized content based on the content reserved by the reservation unit. A system characterized by the following features.

2. The aforementioned reception unit is We accept information such as travel destinations and planned dates. The system according to feature 1.

3. The aforementioned analysis unit, The information received by the reception desk is analyzed to make optimal reservations for flights, hotels, drivers, restaurants, and other services. The system according to feature 1.

4. The aforementioned reservation section is, Reservations are made based on the information analyzed by the aforementioned analysis unit. The system according to feature 1.

5. The aforementioned supply unit is, Personalized content is provided based on the content reserved by the aforementioned reservation department. The system according to feature 1.

6. The aforementioned supply unit is, Automatically generates article headlines, text, and image cropping based on user attributes and preferences. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts how they input travel destinations and dates based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

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