system

Abstract

The system according to this embodiment aims to enable foreign tourists to smoothly plan their trips and enhance the value of their sightseeing experience. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, and a reservation unit. The analysis unit analyzes the user's selection information. The generation unit generates a travel plan based on the information analyzed by the analysis unit. The reservation unit makes reservations for hotels and restaurants based on the travel plan generated by the generation unit.

Description

Technical Field

[0004] ,

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[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 performed by at least one processor, the method including 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 conventional technology, it is difficult for foreign tourists to smoothly make travel plans, and there is room for improvement in terms of improving the value of the tourism experience.

[0005] The system according to the embodiment aims to enable foreign tourists to smoothly make travel plans and improve the value of the tourism experience.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and a reservation unit. The analysis unit analyzes the selection information of the user. The generation unit generates a travel plan based on the information analyzed by the analysis unit. The reservation unit makes reservations for hotels and restaurants based on the travel plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows foreign tourists to smoothly plan their trips and enhance the value of their sightseeing experience. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 tourism support system according to an embodiment of the present invention is a system that utilizes generative AI to provide services that enable foreign tourists to enjoy sightseeing more smoothly. This tourism support system allows users to select images of places they want to visit and types of food they enjoy, and the generative AI then proposes a personalized travel plan. The travel plans offer a variety of options, from those that fill a few hours of free time to multi-day plans that cover the entire trip. Furthermore, if needed, the system provides seamless services from hotel and restaurant reservations to guidance. This enables personalized travel guidance and enhances the value of the sightseeing experience. As a result, the tourism support system can provide services that enable foreign tourists to enjoy sightseeing more smoothly.

[0029] The tourism support system according to this embodiment comprises an analysis unit, a generation unit, and a reservation unit. The analysis unit analyzes user selection information. User selection information includes, but is not limited to, examples of tourist destinations, food, and activities. The analysis unit analyzes user selection information using, for example, data mining techniques. The analysis unit can also analyze user selection information using statistical analysis techniques. The analysis unit can also analyze user selection information using machine learning algorithms. For example, the analysis unit uses data mining techniques to analyze the popularity of tourist destinations from user selection information. Statistical analysis techniques analyze ratings and reviews of tourist destinations based on user selection information. Machine learning algorithms learn user selection information and estimate the optimal tourist destination for each individual user. The generation unit generates a travel plan based on the information analyzed by the analysis unit. The travel plan includes, for example, itinerary, destinations, and activities, but is not limited to these examples. The generation unit generates a travel plan using, for example, generation AI. The generation unit can also generate a travel plan that combines tourist destinations, restaurants, accommodations, etc., based on user selection information. Furthermore, the generation unit can use generation AI to generate plans ranging from those that fill a few hours of free time to multi-day travel plans. For example, the generation unit can use generation AI to generate a one-day plan combining tourist destinations and nearby restaurants based on the user's selection information. The generation unit can also use generation AI to generate plans for entire multi-day trips. The booking unit makes hotel and restaurant reservations based on the travel plans generated by the generation unit. Reservations include, but are not limited to, online reservations, telephone reservations, and reservation confirmation procedures. For example, the booking unit makes hotel and restaurant reservations using an online reservation system. The booking unit can also make hotel and restaurant reservations using telephone reservations. The booking unit can also provide reservation confirmation procedures so that users can check their reservation details. For example, the booking unit uses an online reservation system to book a hotel based on a suggested plan. The booking unit can also book a restaurant based on a suggested plan using telephone reservations.The reservation section provides a reservation confirmation procedure, allowing users to verify their reservation details. This enables the tourism support system according to the embodiment to analyze user selection information, generate an optimal travel plan, and make reservations, thereby providing a seamless travel experience.

[0030] The analysis unit analyzes user selection information. User selection information includes, but is not limited to, examples such as tourist destinations, food, and activities. The analysis unit analyzes user selection information using, for example, data mining techniques. Data mining techniques are techniques for extracting useful patterns and relationships from large amounts of data, and can be used to analyze the popularity and trends of tourist destinations from user selection information. For example, it can analyze which season a particular tourist destination is most popular in based on past user data. The analysis unit can also analyze user selection information using statistical analysis techniques. Statistical analysis techniques are techniques for analyzing the distribution and correlation of data, and can be used to analyze evaluations and reviews of tourist destinations based on user selection information. For example, it can analyze the satisfaction level and repeat visit rate of a particular tourist destination based on user evaluation data. Furthermore, the analysis unit can also analyze user selection information using machine learning algorithms. Machine learning algorithms are techniques that learn from data and perform predictions and classifications, and can learn user selection information to estimate the optimal tourist destination for each individual user. For example, it can recommend a tourist destination to visit next based on a user's past selection information. This allows the analysis unit to utilize data mining techniques, statistical analysis techniques, and machine learning algorithms to comprehensively analyze user selection information and propose the most suitable tourist destinations and activities for the user.

[0031] The generation unit generates a travel plan based on the information analyzed by the analysis unit. The travel plan includes, but is not limited to, iterations, destinations, and activities. The generation unit can, for example, use a generation AI to generate travel plans. The generation AI is a technology that automatically generates the optimal travel plan based on user selections and analysis results. For example, the generation AI can generate a travel plan combining tourist destinations, restaurants, and accommodations, taking into account the user's preferences and past selections. The generation unit can also generate a travel plan combining tourist destinations, restaurants, and accommodations based on user selections. For example, it can generate a one-day plan combining a tourist destination selected by the user with nearby restaurants. Furthermore, the generation unit can use the generation AI to generate plans ranging from those filling a few hours of free time to multi-day travel plans. For example, if a user has a few hours of free time, the generation AI can suggest tourist destinations and activities that can be enjoyed within that time. Also, if a user is planning a multi-day trip, the generation AI can generate a plan for the entire trip, optimally arranging the itinerary, destinations, and activities. This allows the generation unit to provide flexible travel plans tailored to the user's needs and preferences, enabling them to have the optimal travel experience.

[0032] The Reservations Department makes reservations for hotels and restaurants based on travel plans generated by the Generation Department. Reservations include, but are not limited to, online reservations, telephone reservations, and reservation confirmation procedures. For example, the Reservations Department can use an online reservation system to make reservations for hotels and restaurants. The online reservation system allows users to check reservation availability in real time via the internet and make reservations based on their desired date, time, and conditions. The Reservations Department can also make reservations for hotels and restaurants using telephone reservations. Telephone reservations are useful when online reservations are unavailable or when there are special requests, allowing users to contact the establishment directly to make a reservation. Furthermore, the Reservations Department can provide reservation confirmation procedures, enabling users to verify their reservations. For example, when a user makes a hotel reservation based on a suggested plan using the online reservation system, the Reservations Department can send a reservation confirmation email to allow the user to verify their reservation. Similarly, when a user makes a restaurant reservation based on a suggested plan using telephone reservations, the Reservations Department can provide reservation confirmation procedures to allow the user to verify their reservation. This allows the Reservations Department to provide users with a seamless reservation experience and support the realization of their travel plans. Furthermore, the reservations department can handle changes and cancellations to reservations, allowing for flexible responses to user needs. For example, if a user changes their plans, the reservations department can quickly modify or cancel the reservation through the online reservation system or by phone. This enables the reservations department to provide users with a convenient and flexible reservation service, making the travel experience more comfortable.

[0033] The analysis unit can analyze information on tourist destinations and food selected by the user. For example, the analysis unit can analyze information on tourist destinations selected by the user. The analysis unit can also analyze information on food selected by the user. For example, the analysis unit can combine and analyze information on tourist destinations and food selected by the user. This allows the system to provide individual users with optimal travel plans by analyzing information on tourist destinations and food based on their selections. Information on tourist destinations and food includes, but is not limited to, place names, dish names, ratings, and reviews. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input information on tourist destinations selected by the user into a generative AI and have the generative AI perform an analysis of the ratings and reviews of the tourist destinations.

[0034] The generation unit can generate plans ranging from those that fill a few hours of free time to travel plans spanning several days, based on the analyzed information. For example, the generation unit can generate a plan that fills a few hours of free time. The generation unit can also generate a travel plan that spans several days, for example. The generation unit can also generate a combination of a plan that fills a few hours of free time and a travel plan that spans several days, for example. This allows for the provision of diverse travel plans tailored to the user's needs. Plans that fill a few hours of free time may include, but are not limited to, short-term sightseeing spots and activities. Travel plans that span several days may include, but are not limited to, accommodations, itinerary, and destinations. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the analyzed information into a generation AI and have the generation AI generate a plan that fills a few hours of free time.

[0035] The reservation unit can make hotel and restaurant reservations based on the generated travel plan. For example, the reservation unit can make hotel reservations based on the generated travel plan. The reservation unit can also make restaurant reservations based on the generated travel plan. The reservation unit can also make hotel and restaurant reservations based on the generated travel plan. This improves user convenience by allowing seamless reservations based on the travel plan. Hotel and restaurant reservations include, but are not limited to, reservations made through reservation websites, telephone reservations, and reservation confirmation procedures. Some or all of the above processing in the reservation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the reservation unit can input the generated travel plan into a generation AI and have the generation AI execute hotel and restaurant reservations.

[0036] The reservation department can provide travel information tailored to the user's preferences. For example, the reservation department can provide information on tourist destinations tailored to the user's preferences. The reservation department can also provide information on restaurants tailored to the user's preferences. The reservation department can also provide information on accommodations tailored to the user's preferences. By providing travel information tailored to the user's preferences, the value of the travel experience can be enhanced. User preferences include, but are not limited to, past selection history, survey results, and social media activity. Some or all of the above processing in the reservation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation department can input information about the user's preferences into a generative AI and have the generative AI perform the task of providing travel information.

[0037] The analysis unit can analyze the user's past selection history and optimize the analysis algorithm. For example, the analysis unit can analyze trends in tourist destinations previously selected by the user and prioritize analyzing similar destinations. For example, the analysis unit can analyze trends in food previously selected by the user and prioritize analyzing restaurants that offer similar food. For example, the analysis unit can predict tourist destinations to visit in a particular season based on the user's past selection history and optimize the analysis algorithm. By optimizing the analysis algorithm based on the user's past selection history, it is possible to provide more accurate analysis results. Past selection history includes, but is not limited to, previously selected tourist destinations, food, and activities. Optimization of the analysis algorithm includes, but is not limited to, parameter adjustment and the introduction of feedback loops. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the user's past selection history into a generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0038] The analysis unit can adjust the analysis results by taking into account the user's current location information during the analysis. For example, the analysis unit may prioritize analyzing tourist destinations close to the user's current location. The analysis unit may also prioritize analyzing restaurants that are easily accessible from the user's current location. The analysis unit may also analyze the optimal tourist destination by taking into account the travel time from the user's current location. This allows for the provision of more appropriate analysis results by taking into account the user's current location information. Current location information includes, but is not limited to, GPS data and the use of location information services. Adjusting the analysis results includes, but is not limited to, filtering based on location information and changing priorities. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's current location information into a generative AI and have the generative AI perform the adjustment of the analysis results.

[0039] The analysis unit can analyze the user's social media activity during analysis and reflect relevant information in the analysis. For example, the analysis unit can reflect information about tourist destinations that the user has shared on social media. The analysis unit can also reflect information about restaurants that the user has "liked" on social media. The analysis unit can also reflect information about tourist destinations that the user follows on social media. By reflecting the user's social media activity, it is possible to provide more relevant analysis results. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's social media activity data into a generative AI and have the generative AI perform the analysis of the relevant information.

[0040] The analysis unit can provide analysis results in multiple languages, taking into account the user's language settings during analysis. For example, the analysis unit can automatically translate analysis results based on the language settings of the user's device. The analysis unit can also provide a language switching function if the user uses multiple languages. For example, if the user selects a specific language, the analysis unit can provide analysis results in that language. This improves convenience by providing analysis results in multiple languages ​​according to the user's language settings. Language settings include, but are not limited to, the user's device settings and application settings. Providing results in multiple languages ​​includes, but are not limited to, translation algorithms and language selection options. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's language setting data into a generative AI and have the generative AI perform the multilingual provision of analysis results.

[0041] The generation unit can generate an optimal plan by referring to the user's past travel history during the generation process. For example, the generation unit can analyze trends in tourist destinations the user has visited in the past and generate a plan that includes similar destinations. The generation unit can also analyze trends in restaurants the user has visited in the past and generate a plan that includes similar restaurants. For example, the generation unit can predict tourist destinations to visit in a particular season based on the user's past travel history and generate a plan. This allows the system to provide a more appropriate travel plan by referring to the user's past travel history. Past travel history includes, but is not limited to, places visited, accommodations, and activities in the past. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's past travel history into a generation AI and have the generation AI generate an optimal plan.

[0042] The generation unit can adjust the plan during generation, taking into account the user's current weather information. For example, in rainy weather, the generation unit can generate a plan that includes indoor tourist attractions. For example, in sunny weather, the generation unit can also generate a plan that includes outdoor tourist attractions. For example, on snowy days, the generation unit can also generate a plan that includes tourist attractions where snow scenery can be enjoyed. This allows the system to provide a more appropriate travel plan by taking into account the user's current weather information. Current weather information includes, but is not limited to, obtaining weather data or using weather forecasting services. Adjustments to the plan include, but is not limited to, changing to indoor activities or changing the order of destinations. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's current weather information into the generation AI and have the generation AI perform the plan adjustments.

[0043] The generation unit can select the most suitable tourist destination by considering the user's geographical location information during the generation process. For example, the generation unit may prioritize selecting tourist destinations close to the user's current location. The generation unit may also prioritize selecting tourist destinations that are easily accessible from the user's current location. The generation unit may also select the most suitable tourist destination by considering the travel time from the user's current location. This allows for the selection of a more appropriate tourist destination by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and the use of location information services. The selection of tourist destinations includes, but is not limited to, distance, popularity, and the user's interests. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI select the most suitable tourist destination.

[0044] The generation unit can customize the plan content based on the user's interests during generation. For example, the generation unit can generate a plan that prioritizes tourist destinations that the user is interested in. The generation unit can also generate a plan that includes restaurants that serve food that the user is interested in. The generation unit can also generate a plan that includes activities that the user is interested in. This allows for the provision of more appropriate travel plans by customizing the plan content based on the user's interests. Interests include, but are not limited to, past selection history, survey results, and social media activity. Plan customization includes, but is not limited to, changing destinations, adding or removing activities. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input user interest data into a generation AI and have the generation AI perform plan customization.

[0045] The reservation department can select the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation department can analyze the trends of accommodations the user has used in the past and prioritize reservations for similar accommodations. For example, the reservation department can analyze the trends of restaurants the user has used in the past and prioritize reservations for similar restaurants. For example, the reservation department can predict accommodations the user will use in a particular season based on the user's past reservation history and select a reservation method. In this way, by referring to the user's past reservation history, a more appropriate reservation method can be provided. Past reservation history includes, but is not limited to, hotels and restaurants previously booked and reservation frequency. Optimal reservation methods include, but are not limited to, online reservations, telephone reservations, and reservation confirmation procedures. Some or all of the above processing in the reservation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation department can input the user's past reservation history into a generative AI and have the generative AI select the optimal reservation method.

[0046] The reservation unit can adjust the timing of a reservation, taking into account the user's current schedule. For example, the reservation unit can refer to the user's calendar information and suggest the optimal reservation timing. The reservation unit can also make a reservation during an available time slot based on the user's current schedule. The reservation unit can also adjust the timing of a reservation in response to changes in the user's schedule. This allows for the provision of more appropriate reservation timing by considering the user's current schedule. The current schedule includes, but is not limited to, data from a calendar app or manual input. The timing of a reservation includes, but is not limited to, available time slots or high-priority time slots in the schedule. Some or all of the above processing in the reservation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation unit can input the user's schedule data into a generative AI and have the generative AI adjust the timing of the reservation.

[0047] The reservation unit can provide reservation confirmations in multiple languages, taking into account the user's language settings at the time of reservation. For example, the reservation unit can automatically translate the reservation confirmation based on the language settings of the user's device. The reservation unit can also provide a language switching function if the user uses multiple languages. For example, the reservation unit can provide the reservation confirmation in a specific language if the user selects a particular language. This improves convenience by providing reservation confirmations in multiple languages ​​according to the user's language settings. The reservation confirmation may include, but is not limited to, translation algorithms and language selection options. Some or all of the above processing in the reservation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation unit can input the user's language setting data into a generative AI and have the generative AI perform the task of providing reservation confirmations in multiple languages.

[0048] The reservation unit can suggest the most suitable payment method by referring to the user's payment history at the time of reservation. For example, the reservation unit may prioritize suggesting payment methods the user has used in the past. The reservation unit may also predict and suggest a specific payment method based on the user's payment history. For example, the reservation unit may analyze the trends of payment methods the user has used in the past and suggest the most suitable payment method. This allows the system to provide a more appropriate payment method by referring to the user's payment history. Payment history includes, but is not limited to, past payment methods, payment amounts, and payment frequency. The most suitable payment method includes, but is not limited to, credit cards, debit cards, and electronic money. Some or all of the above processing in the reservation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation unit can input the user's payment history data into a generative AI and have the generative AI suggest the most suitable payment method.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analysis unit can analyze the congestion status of tourist destinations in real time based on the user's selection information. For example, the analysis unit can analyze the current congestion level of a tourist destination and suggest alternative tourist destinations to avoid congestion. The analysis unit can also predict congestion levels at tourist destinations and suggest the optimal time to visit the user. For example, the analysis unit can suggest a visit order to the user based on the congestion level of tourist destinations. This allows the user to avoid congestion and enjoy sightseeing comfortably. The analysis of congestion status includes, but is not limited to, real-time pedestrian flow data, social media posting data, and traffic data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input tourist destination congestion data into a generation AI and have the generation AI perform the congestion status analysis.

[0051] The generation unit can generate travel plans specifically tailored to ecotourism based on user selection information. For example, the generation unit can generate plans that include nature reserves and eco-friendly accommodations. The generation unit can also generate plans that utilize environmentally conscious transportation. The generation unit can also generate plans that include activities to learn about local culture and nature. This allows users to enjoy sightseeing while being mindful of the environment. Ecotourism plans include, but are not limited to, visits to nature reserves, selection of eco-friendly accommodations, and participation in environmental education programs. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input ecotourism-related data into a generation AI and have the generation AI generate ecotourism plans.

[0052] The booking department can provide travel plans aligned with specific themes based on the user's selection information. For example, the booking department can provide plans that visit historical tourist sites. The booking department can also provide plans that include gourmet tours. The booking department can also provide plans that focus on art and culture. This allows users to enjoy themed travel tailored to their interests. Themed travel plans include, but are not limited to, visits to historical buildings, tours to enjoy local cuisine, and visits to art galleries and museums. Some or all of the above processing in the booking department may be performed using or without a generative AI. For example, the booking department can input data related to themed travel into a generative AI and have the generative AI provide themed travel plans.

[0053] The analysis unit can analyze safety information for tourist destinations based on user selection information. For example, the analysis unit can analyze the current security situation of tourist destinations and suggest safe destinations. The analysis unit can also analyze past crime data for tourist destinations and prioritize suggesting safer destinations. The analysis unit can also analyze the natural disaster risk of tourist destinations and suggest safe times to visit. This allows users to enjoy sightseeing safely. The analysis of safety information includes, but is not limited to, crime data, natural disaster data, and government safety recommendations. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input safety information data into a generating AI and have the generating AI perform the safety information analysis.

[0054] The generation unit can generate travel plans specifically tailored for barrier-free tourism based on user selection information. For example, the generation unit can generate plans that include wheelchair-accessible tourist destinations. The generation unit can also generate plans that include barrier-free accommodations. The generation unit can also generate plans that include activities for people with disabilities. This allows users to enjoy sightseeing in a barrier-free environment. Barrier-free tourism plans include, but are not limited to, visiting wheelchair-accessible tourist destinations, selecting barrier-free accommodations, and participating in activities for people with disabilities. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input barrier-free tourism data into a generation AI and have the generation AI generate barrier-free plans.

[0055] The booking department can provide travel plans tailored to specific events based on the user's selection information. For example, the booking department can provide plans tailored to music festivals. For example, the booking department can also provide plans tailored to sporting events. For example, the booking department can also provide plans tailored to cultural festivals or local festivals. This allows users to utilize the optimal travel plan for enjoying a particular event. Event-tailored plans include, but are not limited to, visiting event venues, participating in event-related activities, and booking accommodations during the event period. Some or all of the above processing in the booking department may be performed using or without a generative AI. For example, the booking department can input event-related data into a generative AI and have the generative AI provide event-tailored plans.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The analysis unit analyzes the user's selection information. This information includes tourist destinations, food, and activities. The analysis unit uses data mining techniques, statistical analysis techniques, and machine learning algorithms to analyze the user's selection information. For example, it uses data mining techniques to analyze the popularity of tourist destinations, statistical analysis techniques to analyze ratings and reviews of tourist destinations, and machine learning algorithms to estimate the most suitable tourist destinations for individual users. Step 2: The generation unit generates a travel plan based on the information analyzed by the analysis unit. The travel plan includes the itinerary, destinations, and activities. The generation unit uses generation AI to generate travel plans, combining tourist attractions, restaurants, accommodations, etc., based on the user's selections. For example, it uses generation AI to generate a one-day plan combining tourist attractions and nearby restaurants, or a plan for an entire trip spanning several days. Step 3: The reservation department makes hotel and restaurant reservations based on the travel plan generated by the generation department. Reservations include online reservations, telephone reservations, and reservation confirmation procedures. The reservation department makes hotel and restaurant reservations using online reservation systems and telephone reservations, and provides reservation confirmation procedures so that users can confirm their reservation details.

[0058] (Example of form 2) The tourism support system according to an embodiment of the present invention is a system that utilizes generative AI to provide services that enable foreign tourists to enjoy sightseeing more smoothly. This tourism support system allows users to select images of places they want to visit and types of food they enjoy, and the generative AI then proposes a personalized travel plan. The travel plans offer a variety of options, from those that fill a few hours of free time to multi-day plans that cover the entire trip. Furthermore, if needed, the system provides seamless services from hotel and restaurant reservations to guidance. This enables personalized travel guidance and enhances the value of the sightseeing experience. As a result, the tourism support system can provide services that enable foreign tourists to enjoy sightseeing more smoothly.

[0059] The tourism support system according to this embodiment comprises an analysis unit, a generation unit, and a reservation unit. The analysis unit analyzes user selection information. User selection information includes, but is not limited to, examples of tourist destinations, food, and activities. The analysis unit analyzes user selection information using, for example, data mining techniques. The analysis unit can also analyze user selection information using statistical analysis techniques. The analysis unit can also analyze user selection information using machine learning algorithms. For example, the analysis unit uses data mining techniques to analyze the popularity of tourist destinations from user selection information. Statistical analysis techniques analyze ratings and reviews of tourist destinations based on user selection information. Machine learning algorithms learn user selection information and estimate the optimal tourist destination for each individual user. The generation unit generates a travel plan based on the information analyzed by the analysis unit. The travel plan includes, for example, itinerary, destinations, and activities, but is not limited to these examples. The generation unit generates a travel plan using, for example, generation AI. The generation unit can also generate a travel plan that combines tourist destinations, restaurants, accommodations, etc., based on user selection information. Furthermore, the generation unit can use generation AI to generate plans ranging from those that fill a few hours of free time to multi-day travel plans. For example, the generation unit can use generation AI to generate a one-day plan combining tourist destinations and nearby restaurants based on the user's selection information. The generation unit can also use generation AI to generate plans for entire multi-day trips. The booking unit makes hotel and restaurant reservations based on the travel plans generated by the generation unit. Reservations include, but are not limited to, online reservations, telephone reservations, and reservation confirmation procedures. For example, the booking unit makes hotel and restaurant reservations using an online reservation system. The booking unit can also make hotel and restaurant reservations using telephone reservations. The booking unit can also provide reservation confirmation procedures so that users can check their reservation details. For example, the booking unit uses an online reservation system to book a hotel based on a suggested plan. The booking unit can also book a restaurant based on a suggested plan using telephone reservations.The reservation section provides a reservation confirmation procedure, allowing users to verify their reservation details. This enables the tourism support system according to the embodiment to analyze user selection information, generate an optimal travel plan, and make reservations, thereby providing a seamless travel experience.

[0060] The analysis unit analyzes user selection information. User selection information includes, but is not limited to, examples such as tourist destinations, food, and activities. The analysis unit analyzes user selection information using, for example, data mining techniques. Data mining techniques are techniques for extracting useful patterns and relationships from large amounts of data, and can be used to analyze the popularity and trends of tourist destinations from user selection information. For example, it can analyze which season a particular tourist destination is most popular in based on past user data. The analysis unit can also analyze user selection information using statistical analysis techniques. Statistical analysis techniques are techniques for analyzing the distribution and correlation of data, and can be used to analyze evaluations and reviews of tourist destinations based on user selection information. For example, it can analyze the satisfaction level and repeat visit rate of a particular tourist destination based on user evaluation data. Furthermore, the analysis unit can also analyze user selection information using machine learning algorithms. Machine learning algorithms are techniques that learn from data and perform predictions and classifications, and can learn user selection information to estimate the optimal tourist destination for each individual user. For example, it can recommend a tourist destination to visit next based on a user's past selection information. This allows the analysis unit to utilize data mining techniques, statistical analysis techniques, and machine learning algorithms to comprehensively analyze user selection information and propose the most suitable tourist destinations and activities for the user.

[0061] The generation unit generates a travel plan based on the information analyzed by the analysis unit. The travel plan includes, but is not limited to, iterations, destinations, and activities. The generation unit can, for example, use a generation AI to generate travel plans. The generation AI is a technology that automatically generates the optimal travel plan based on user selections and analysis results. For example, the generation AI can generate a travel plan combining tourist destinations, restaurants, and accommodations, taking into account the user's preferences and past selections. The generation unit can also generate a travel plan combining tourist destinations, restaurants, and accommodations based on user selections. For example, it can generate a one-day plan combining a tourist destination selected by the user with nearby restaurants. Furthermore, the generation unit can use the generation AI to generate plans ranging from those filling a few hours of free time to multi-day travel plans. For example, if a user has a few hours of free time, the generation AI can suggest tourist destinations and activities that can be enjoyed within that time. Also, if a user is planning a multi-day trip, the generation AI can generate a plan for the entire trip, optimally arranging the itinerary, destinations, and activities. This allows the generation unit to provide flexible travel plans tailored to the user's needs and preferences, enabling them to have the optimal travel experience.

[0062] The Reservations Department makes reservations for hotels and restaurants based on travel plans generated by the Generation Department. Reservations include, but are not limited to, online reservations, telephone reservations, and reservation confirmation procedures. For example, the Reservations Department can use an online reservation system to make reservations for hotels and restaurants. The online reservation system allows users to check reservation availability in real time via the internet and make reservations based on their desired date, time, and conditions. The Reservations Department can also make reservations for hotels and restaurants using telephone reservations. Telephone reservations are useful when online reservations are unavailable or when there are special requests, allowing users to contact the establishment directly to make a reservation. Furthermore, the Reservations Department can provide reservation confirmation procedures, enabling users to verify their reservations. For example, when a user makes a hotel reservation based on a suggested plan using the online reservation system, the Reservations Department can send a reservation confirmation email to allow the user to verify their reservation. Similarly, when a user makes a restaurant reservation based on a suggested plan using telephone reservations, the Reservations Department can provide reservation confirmation procedures to allow the user to verify their reservation. This allows the Reservations Department to provide users with a seamless reservation experience and support the realization of their travel plans. Furthermore, the reservations department can handle changes and cancellations to reservations, allowing for flexible responses to user needs. For example, if a user changes their plans, the reservations department can quickly modify or cancel the reservation through the online reservation system or by phone. This enables the reservations department to provide users with a convenient and flexible reservation service, making the travel experience more comfortable.

[0063] The analysis unit can analyze information on tourist destinations and food selected by the user. For example, the analysis unit can analyze information on tourist destinations selected by the user. The analysis unit can also analyze information on food selected by the user. For example, the analysis unit can combine and analyze information on tourist destinations and food selected by the user. This allows the system to provide individual users with optimal travel plans by analyzing information on tourist destinations and food based on their selections. Information on tourist destinations and food includes, but is not limited to, place names, dish names, ratings, and reviews. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input information on tourist destinations selected by the user into a generative AI and have the generative AI perform an analysis of the ratings and reviews of the tourist destinations.

[0064] The generation unit can generate plans ranging from those that fill a few hours of free time to travel plans spanning several days, based on the analyzed information. For example, the generation unit can generate a plan that fills a few hours of free time. The generation unit can also generate a travel plan that spans several days, for example. The generation unit can also generate a combination of a plan that fills a few hours of free time and a travel plan that spans several days, for example. This allows for the provision of diverse travel plans tailored to the user's needs. Plans that fill a few hours of free time may include, but are not limited to, short-term sightseeing spots and activities. Travel plans that span several days may include, but are not limited to, accommodations, itinerary, and destinations. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the analyzed information into a generation AI and have the generation AI generate a plan that fills a few hours of free time.

[0065] The reservation unit can make hotel and restaurant reservations based on the generated travel plan. For example, the reservation unit can make hotel reservations based on the generated travel plan. The reservation unit can also make restaurant reservations based on the generated travel plan. The reservation unit can also make hotel and restaurant reservations based on the generated travel plan. This improves user convenience by allowing seamless reservations based on the travel plan. Hotel and restaurant reservations include, but are not limited to, reservations made through reservation websites, telephone reservations, and reservation confirmation procedures. Some or all of the above processing in the reservation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the reservation unit can input the generated travel plan into a generation AI and have the generation AI execute hotel and restaurant reservations.

[0066] The reservation department can provide travel information tailored to the user's preferences. For example, the reservation department can provide information on tourist destinations tailored to the user's preferences. The reservation department can also provide information on restaurants tailored to the user's preferences. The reservation department can also provide information on accommodations tailored to the user's preferences. By providing travel information tailored to the user's preferences, the value of the travel experience can be enhanced. User preferences include, but are not limited to, past selection history, survey results, and social media activity. Some or all of the above processing in the reservation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation department can input information about the user's preferences into a generative AI and have the generative AI perform the task of providing travel information.

[0067] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can adjust the accuracy of the analysis to provide detailed tourist information. For example, if the user is relaxed, the analysis unit can prioritize analyzing relaxing tourist destinations. For example, if the user is stressed, the analysis unit can prioritize analyzing tourist destinations that reduce stress. By adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided. User emotions include, but are not limited to, facial recognition, voice analysis, and text analysis. Accuracy of the analysis includes, but are not limited to, algorithm parameter adjustment and data filtering. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI perform adjustments to the accuracy of the emotion-based analysis.

[0068] The analysis unit can analyze the user's past selection history and optimize the analysis algorithm. For example, the analysis unit can analyze trends in tourist destinations previously selected by the user and prioritize analyzing similar destinations. For example, the analysis unit can analyze trends in food previously selected by the user and prioritize analyzing restaurants that offer similar food. For example, the analysis unit can predict tourist destinations to visit in a particular season based on the user's past selection history and optimize the analysis algorithm. By optimizing the analysis algorithm based on the user's past selection history, it is possible to provide more accurate analysis results. Past selection history includes, but is not limited to, previously selected tourist destinations, food, and activities. Optimization of the analysis algorithm includes, but is not limited to, parameter adjustment and the introduction of feedback loops. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the user's past selection history into a generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0069] The analysis unit can adjust the analysis results by taking into account the user's current location information during the analysis. For example, the analysis unit may prioritize analyzing tourist destinations close to the user's current location. The analysis unit may also prioritize analyzing restaurants that are easily accessible from the user's current location. The analysis unit may also analyze the optimal tourist destination by taking into account the travel time from the user's current location. This allows for the provision of more appropriate analysis results by taking into account the user's current location information. Current location information includes, but is not limited to, GPS data and the use of location information services. Adjusting the analysis results includes, but is not limited to, filtering based on location information and changing priorities. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's current location information into a generative AI and have the generative AI perform the adjustment of the analysis results.

[0070] 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 excited, the analysis unit may provide a visually stimulating display method. For example, if the user is relaxed, the analysis unit may provide a display method with calming colors. For example, if the user is stressed, the analysis unit may provide a simple and highly visible display method. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display can be provided. Display methods of the analysis results include, but are not limited to, graphical user interfaces, text displays, and voice guidance. Emotion estimation is achieved using an emotion estimation function, for example, 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, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results based on the emotions.

[0071] The analysis unit can analyze the user's social media activity during analysis and reflect relevant information in the analysis. For example, the analysis unit can reflect information about tourist destinations that the user has shared on social media. The analysis unit can also reflect information about restaurants that the user has "liked" on social media. The analysis unit can also reflect information about tourist destinations that the user follows on social media. By reflecting the user's social media activity, it is possible to provide more relevant analysis results. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's social media activity data into a generative AI and have the generative AI perform the analysis of the relevant information.

[0072] The analysis unit can provide analysis results in multiple languages, taking into account the user's language settings during analysis. For example, the analysis unit can automatically translate analysis results based on the language settings of the user's device. The analysis unit can also provide a language switching function if the user uses multiple languages. For example, if the user selects a specific language, the analysis unit can provide analysis results in that language. This improves convenience by providing analysis results in multiple languages ​​according to the user's language settings. Language settings include, but are not limited to, the user's device settings and application settings. Providing results in multiple languages ​​includes, but are not limited to, translation algorithms and language selection options. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's language setting data into a generative AI and have the generative AI perform the multilingual provision of analysis results.

[0073] The generation unit can estimate the user's emotions and adjust the content of the generated plan based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a plan that includes relaxing tourist destinations. For example, if the user is excited, the generation unit can also generate a plan that includes active tourist destinations. For example, if the user is stressed, the generation unit can also generate a plan that includes stress-relieving tourist destinations. This allows for the provision of more appropriate travel plans by adjusting the plan content according to the user's emotions. Plan content may include, but is not limited to, changes to destinations or the addition or deletion of activities. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may include, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion-based adjustments to the plan content.

[0074] The generation unit can generate an optimal plan by referring to the user's past travel history during the generation process. For example, the generation unit can analyze trends in tourist destinations the user has visited in the past and generate a plan that includes similar destinations. The generation unit can also analyze trends in restaurants the user has visited in the past and generate a plan that includes similar restaurants. For example, the generation unit can predict tourist destinations to visit in a particular season based on the user's past travel history and generate a plan. This allows the system to provide a more appropriate travel plan by referring to the user's past travel history. Past travel history includes, but is not limited to, places visited, accommodations, and activities in the past. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's past travel history into a generation AI and have the generation AI generate an optimal plan.

[0075] The generation unit can adjust the plan during generation, taking into account the user's current weather information. For example, in rainy weather, the generation unit can generate a plan that includes indoor tourist attractions. For example, in sunny weather, the generation unit can also generate a plan that includes outdoor tourist attractions. For example, on snowy days, the generation unit can also generate a plan that includes tourist attractions where snow scenery can be enjoyed. This allows the system to provide a more appropriate travel plan by taking into account the user's current weather information. Current weather information includes, but is not limited to, obtaining weather data or using weather forecasting services. Adjustments to the plan include, but is not limited to, changing to indoor activities or changing the order of destinations. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's current weather information into the generation AI and have the generation AI perform the plan adjustments.

[0076] The generation unit can estimate the user's emotions and determine the priority of the plans to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a plan that prioritizes relaxing tourist destinations. For example, if the user is excited, the generation unit can also generate a plan that prioritizes active tourist destinations. For example, if the user is stressed, the generation unit can also generate a plan that prioritizes stress-reducing tourist destinations. This allows for the provision of more appropriate travel plans by determining the priority of plans according to the user's emotions. Plan prioritization includes, but is not limited to, the user's emotion score and importance ratings. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform the determination of plan prioritization based on emotions.

[0077] The generation unit can select the most suitable tourist destination by considering the user's geographical location information during the generation process. For example, the generation unit may prioritize selecting tourist destinations close to the user's current location. The generation unit may also prioritize selecting tourist destinations that are easily accessible from the user's current location. The generation unit may also select the most suitable tourist destination by considering the travel time from the user's current location. This allows for the selection of a more appropriate tourist destination by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and the use of location information services. The selection of tourist destinations includes, but is not limited to, distance, popularity, and the user's interests. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI select the most suitable tourist destination.

[0078] The generation unit can customize the plan content based on the user's interests during generation. For example, the generation unit can generate a plan that prioritizes tourist destinations that the user is interested in. The generation unit can also generate a plan that includes restaurants that serve food that the user is interested in. The generation unit can also generate a plan that includes activities that the user is interested in. This allows for the provision of more appropriate travel plans by customizing the plan content based on the user's interests. Interests include, but are not limited to, past selection history, survey results, and social media activity. Plan customization includes, but is not limited to, changing destinations, adding or removing activities. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input user interest data into a generation AI and have the generation AI perform plan customization.

[0079] The booking unit can estimate the user's emotions and determine booking priorities based on those emotions. For example, if the user is relaxed, the booking unit will prioritize booking accommodations that promote relaxation. If the user is excited, the booking unit may also prioritize booking accommodations that offer active activities. If the user is stressed, the booking unit may also prioritize booking accommodations that help reduce stress. By prioritizing bookings according to the user's emotions, the system can provide more appropriate bookings. Booking priorities may include, but are not limited to, the user's emotion score or importance rating. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 unit may be performed using, for example, generative AI, or not using generative AI. For example, the booking unit can input user emotion data into a generative AI and have the generative AI perform emotion-based booking priority determination.

[0080] The reservation department can select the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation department can analyze the trends of accommodations the user has used in the past and prioritize reservations for similar accommodations. For example, the reservation department can analyze the trends of restaurants the user has used in the past and prioritize reservations for similar restaurants. For example, the reservation department can predict accommodations the user will use in a particular season based on the user's past reservation history and select a reservation method. In this way, by referring to the user's past reservation history, a more appropriate reservation method can be provided. Past reservation history includes, but is not limited to, hotels and restaurants previously booked and reservation frequency. Optimal reservation methods include, but are not limited to, online reservations, telephone reservations, and reservation confirmation procedures. Some or all of the above processing in the reservation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation department can input the user's past reservation history into a generative AI and have the generative AI select the optimal reservation method.

[0081] The reservation unit can adjust the timing of a reservation, taking into account the user's current schedule. For example, the reservation unit can refer to the user's calendar information and suggest the optimal reservation timing. The reservation unit can also make a reservation during an available time slot based on the user's current schedule. The reservation unit can also adjust the timing of a reservation in response to changes in the user's schedule. This allows for the provision of more appropriate reservation timing by considering the user's current schedule. The current schedule includes, but is not limited to, data from a calendar app or manual input. The timing of a reservation includes, but is not limited to, available time slots or high-priority time slots in the schedule. Some or all of the above processing in the reservation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation unit can input the user's schedule data into a generative AI and have the generative AI adjust the timing of the reservation.

[0082] The reservation unit can estimate the user's emotions and adjust the reservation confirmation method based on the estimated emotions. For example, if the user is nervous, the reservation unit can provide a simple and highly visible confirmation method. For example, if the user is relaxed, the reservation unit can also provide a confirmation method that includes detailed information. For example, if the user is in a hurry, the reservation unit can also provide a concise confirmation method. This allows for the provision of a more appropriate confirmation method by adjusting the reservation confirmation method according to the user's emotions. Reservation confirmation methods include, but are not limited to, email notifications, SMS notifications, and in-app notifications. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the reservation unit may be performed using, for example, generative AI, or not using generative AI. For example, the reservation unit can input user emotion data into a generative AI and have the generative AI adjust the reservation confirmation method based on emotions.

[0083] The reservation unit can provide reservation confirmations in multiple languages, taking into account the user's language settings at the time of reservation. For example, the reservation unit can automatically translate the reservation confirmation based on the language settings of the user's device. The reservation unit can also provide a language switching function if the user uses multiple languages. For example, the reservation unit can provide the reservation confirmation in a specific language if the user selects a particular language. This improves convenience by providing reservation confirmations in multiple languages ​​according to the user's language settings. The reservation confirmation may include, but is not limited to, translation algorithms and language selection options. Some or all of the above processing in the reservation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation unit can input the user's language setting data into a generative AI and have the generative AI perform the task of providing reservation confirmations in multiple languages.

[0084] The reservation unit can suggest the most suitable payment method by referring to the user's payment history at the time of reservation. For example, the reservation unit may prioritize suggesting payment methods the user has used in the past. The reservation unit may also predict and suggest a specific payment method based on the user's payment history. For example, the reservation unit may analyze the trends of payment methods the user has used in the past and suggest the most suitable payment method. This allows the system to provide a more appropriate payment method by referring to the user's payment history. Payment history includes, but is not limited to, past payment methods, payment amounts, and payment frequency. The most suitable payment method includes, but is not limited to, credit cards, debit cards, and electronic money. Some or all of the above processing in the reservation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation unit can input the user's payment history data into a generative AI and have the generative AI suggest the most suitable payment method.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The analysis unit can analyze the congestion status of tourist destinations in real time based on the user's selection information. For example, the analysis unit can analyze the current congestion level of a tourist destination and suggest alternative tourist destinations to avoid congestion. The analysis unit can also predict congestion levels at tourist destinations and suggest the optimal time to visit the user. For example, the analysis unit can suggest a visit order to the user based on the congestion level of tourist destinations. This allows the user to avoid congestion and enjoy sightseeing comfortably. The analysis of congestion status includes, but is not limited to, real-time pedestrian flow data, social media posting data, and traffic data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input tourist destination congestion data into a generation AI and have the generation AI perform the congestion status analysis.

[0087] The generation unit can generate travel plans specifically tailored to ecotourism based on user selection information. For example, the generation unit can generate plans that include nature reserves and eco-friendly accommodations. The generation unit can also generate plans that utilize environmentally conscious transportation. The generation unit can also generate plans that include activities to learn about local culture and nature. This allows users to enjoy sightseeing while being mindful of the environment. Ecotourism plans include, but are not limited to, visits to nature reserves, selection of eco-friendly accommodations, and participation in environmental education programs. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input ecotourism-related data into a generation AI and have the generation AI generate ecotourism plans.

[0088] The booking department can provide travel plans aligned with specific themes based on the user's selection information. For example, the booking department can provide plans that visit historical tourist sites. The booking department can also provide plans that include gourmet tours. The booking department can also provide plans that focus on art and culture. This allows users to enjoy themed travel tailored to their interests. Themed travel plans include, but are not limited to, visits to historical buildings, tours to enjoy local cuisine, and visits to art galleries and museums. Some or all of the above processing in the booking department may be performed using or without a generative AI. For example, the booking department can input data related to themed travel into a generative AI and have the generative AI provide themed travel plans.

[0089] The analysis unit can estimate the user's emotions and provide feedback based on the estimated emotions. For example, if the user is satisfied, the analysis unit provides positive feedback. For example, if the user is dissatisfied, the analysis unit can also suggest areas for improvement. For example, if the user is excited, the analysis unit can also suggest the next steps. This improves the user experience by providing feedback that matches the user's emotions. Feedback includes, but is not limited to, text messages, voice messages, and visual messages. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI provide emotion-based feedback.

[0090] The generation unit can estimate the user's emotions and adjust the difficulty level of the travel plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a plan that includes relaxing tourist destinations. For example, if the user is excited, the generation unit can also generate a plan that includes active tourist destinations. For example, if the user is stressed, the generation unit can also generate a plan that includes stress-relieving tourist destinations. This allows for the provision of more appropriate travel plans by adjusting the difficulty level of the plan according to the user's emotions. The difficulty level of the plan includes, but is not limited to, changes in destinations, additions or deletions of activities, etc. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion-based adjustments to the plan difficulty level.

[0091] The reservation department can estimate the user's emotions and adjust the reservation cancellation policy based on those emotions. For example, if the user is feeling anxious, the reservation department can offer a flexible cancellation policy. If the user is relaxed, the reservation department can offer a standard cancellation policy. If the user is in a hurry, the reservation department can offer a quick cancellation procedure. By adjusting the cancellation policy according to the user's emotions, a more appropriate reservation experience can be provided. Cancellation policies include, but are not limited to, setting cancellation fees and simplifying the cancellation procedure. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the reservation department may be performed using generative AI or not. For example, the reservation department can input user emotion data into generative AI and have the generative AI perform emotion-based cancellation policy adjustments.

[0092] The analysis unit can analyze safety information for tourist destinations based on user selection information. For example, the analysis unit can analyze the current security situation of tourist destinations and suggest safe destinations. The analysis unit can also analyze past crime data for tourist destinations and prioritize suggesting safer destinations. The analysis unit can also analyze the natural disaster risk of tourist destinations and suggest safe times to visit. This allows users to enjoy sightseeing safely. The analysis of safety information includes, but is not limited to, crime data, natural disaster data, and government safety recommendations. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input safety information data into a generating AI and have the generating AI perform the safety information analysis.

[0093] The generation unit can generate travel plans specifically tailored for barrier-free tourism based on user selection information. For example, the generation unit can generate plans that include wheelchair-accessible tourist destinations. The generation unit can also generate plans that include barrier-free accommodations. The generation unit can also generate plans that include activities for people with disabilities. This allows users to enjoy sightseeing in a barrier-free environment. Barrier-free tourism plans include, but are not limited to, visiting wheelchair-accessible tourist destinations, selecting barrier-free accommodations, and participating in activities for people with disabilities. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input barrier-free tourism data into a generation AI and have the generation AI generate barrier-free plans.

[0094] The booking department can provide travel plans tailored to specific events based on the user's selection information. For example, the booking department can provide plans tailored to music festivals. For example, the booking department can also provide plans tailored to sporting events. For example, the booking department can also provide plans tailored to cultural festivals or local festivals. This allows users to utilize the optimal travel plan for enjoying a particular event. Event-tailored plans include, but are not limited to, visiting event venues, participating in event-related activities, and booking accommodations during the event period. Some or all of the above processing in the booking department may be performed using or without a generative AI. For example, the booking department can input event-related data into a generative AI and have the generative AI provide event-tailored plans.

[0095] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit may provide a visually stimulating notification method. For example, if the user is relaxed, the analysis unit may provide a notification method with calming colors. For example, if the user is stressed, the analysis unit may provide a simple and highly visible notification method. By adjusting the notification method of the analysis results according to the user's emotions, more appropriate notifications can be provided. Notification methods include, but are not limited to, graphical user interfaces, text notifications, and voice notifications. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user emotion data into generative AI and have the generative AI perform the adjustment of the notification method based on emotions.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The analysis unit analyzes the user's selection information. This information includes tourist destinations, food, and activities. The analysis unit uses data mining techniques, statistical analysis techniques, and machine learning algorithms to analyze the user's selection information. For example, it uses data mining techniques to analyze the popularity of tourist destinations, statistical analysis techniques to analyze ratings and reviews of tourist destinations, and machine learning algorithms to estimate the most suitable tourist destinations for individual users. Step 2: The generation unit generates a travel plan based on the information analyzed by the analysis unit. The travel plan includes the itinerary, destinations, and activities. The generation unit uses generation AI to generate travel plans, combining tourist attractions, restaurants, accommodations, etc., based on the user's selections. For example, it uses generation AI to generate a one-day plan combining tourist attractions and nearby restaurants, or a plan for an entire trip spanning several days. Step 3: The reservation department makes hotel and restaurant reservations based on the travel plan generated by the generation department. Reservations include online reservations, telephone reservations, and reservation confirmation procedures. The reservation department makes hotel and restaurant reservations using online reservation systems and telephone reservations, and provides reservation confirmation procedures so that users can confirm their reservation details.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the analysis unit, generation unit, and reservation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a travel plan using generation AI. The reservation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and makes reservations for hotels and restaurants using an online reservation system. 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.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the analysis unit, generation unit, and reservation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates a travel plan using generation AI. The reservation unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and makes reservations for hotels and restaurants using an online reservation system. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the analysis unit, generation unit, and reservation unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates travel plans using generation AI. The reservation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and makes reservations for hotels and restaurants using an online reservation system. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the analysis unit, generation unit, and reservation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a travel plan using a generation AI. The reservation unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12 and makes reservations for hotels and restaurants using an online reservation system. 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) An analysis unit that analyzes user selection information, A generation unit generates a travel plan based on the information analyzed by the analysis unit, The system includes a reservation unit that makes reservations for hotels and restaurants based on the travel plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze information on tourist destinations and food selected by the user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the analyzed information, it generates plans ranging from those to fill a few hours of free time to multi-day travel itineraries. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reservation section is, Based on the generated travel plan, make reservations for hotels and restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reservation section is, We provide travel guides tailored to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, Analyze the user's past selection history and optimize the analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During analysis, the analysis results are adjusted to take into account the user's current location information. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned analysis unit, During analysis, the system analyzes users' social media activity and incorporates relevant information into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the analysis results are provided in multiple languages, taking into account the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the content of the plan generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During the creation process, the system references the user's past travel history to generate the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the plan is adjusted to take into account the user's current weather information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and determines the priority of the plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the system selects the most suitable tourist destinations by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the plan content is customized based on the user's interests. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) 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 20) The aforementioned reservation section is, When making a reservation, we adjust the timing of the reservation considering the user's current schedule. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reservation section is, The system estimates the user's emotions and adjusts the reservation confirmation method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reservation section is, When making a reservation, we provide reservation confirmations in multiple languages, taking into account the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reservation section is, When you make a reservation, we will refer to your payment history and suggest the most suitable payment method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 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. An analysis unit that analyzes user selection information, A generation unit generates a travel plan based on the information analyzed by the analysis unit, The system includes a reservation unit that makes reservations for hotels and restaurants based on the travel plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze information on tourist destinations and food selected by the user. The system according to feature 1.

3. The generating unit is Based on the analyzed information, it generates plans ranging from those to fill a few hours of free time to multi-day travel itineraries. The system according to feature 1.

4. The aforementioned reservation section is, Based on the generated travel plan, make reservations for hotels and restaurants. The system according to feature 1.

5. The aforementioned reservation section is, We provide travel guides tailored to the user's preferences. The system according to feature 1.

6. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit, Analyze the user's past selection history and optimize the analysis algorithm. The system according to feature 1.

8. The aforementioned analysis unit, During analysis, the analysis results are adjusted to take into account the user's current location information. The system according to feature 1.

9. 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 according to feature 1.

10. The aforementioned analysis unit, During analysis, the system analyzes users' social media activity and incorporates relevant information into the analysis. The system according to feature 1.

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