System

Abstract

An object of a system according to an embodiment is to propose a sightseeing route and recommend parking lot information based on a user's preference or past history.SOLUTION: A system according to an embodiment includes a suggestion unit, a display unit, a parking lot recommendation unit, and a privilege granting unit. The suggestion unit suggests a sightseeing route based on the user's preference or past history. The display part displays the sightseeing point proposed by the proposal part on a map. The parking lot recommendation unit recommends parking lot information around the sightseeing point. The privilege granting unit recognizes a license plate and grants a privilege.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately suggest sightseeing routes or recommend parking information based on user preferences and past history, and there is room for improvement.

[0005] The system according to the embodiment aims to propose sightseeing routes based on the user's preferences and past history, and to recommend parking information. [Means for solving the problem]

[0006] The system according to the embodiment includes a suggestion unit, a display unit, a parking lot recommendation unit, and a reward granting unit. The suggestion unit suggests a sightseeing route based on the user's preferences or past history. The display unit displays the sightseeing points suggested by the suggestion unit on a map. The parking lot recommendation unit recommends parking lot information around the sightseeing points. The reward granting unit recognizes license plates and grants rewards. [Effects of the Invention]

[0007] The system according to the embodiment can propose sightseeing routes based on the user's preferences and past history, and recommend parking information. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A tourist route recommendation system according to an embodiment of the present invention uses a generation AI to propose tourist routes based on the user's preferences and past history, and provides parking information and special offers. The tourist route recommendation system allows users to provide information to the generation AI by circling desired destinations on a map or entering keywords. The generation AI then proposes tourist routes based on the user's preferences and past history, and displays the route information on the map through API integration with map apps and navigation systems. Tourist spot information is generated by AI using social media, eliminating intentional commercialization. Furthermore, the system recommends parking information near tourist spots to the user, and suggests options such as parking + public transportation, taxis, shared cars, rental bicycles, and rental kick scooters as needed. At recommended parking lots, the system scans license plates for AI recognition, or registers two-dimensional codes (e.g., QR Codes (registered trademarks)) and the number to grant special offers such as parking fee discounts, meal discounts, and discounts on tourist spot admission fees. Automatic parking fee payment is also smoothly implemented. Furthermore, the generation AI recommends nearby tourist spots depending on parking and other waiting times. For example, in a tourist route recommendation system, a user can circle a place they want to visit on a map or enter keywords. This information is then input into a generation AI. The generation AI then suggests a tourist route based on the user's preferences and past history. For example, it analyzes tourist spots the user has previously visited and posts on social media to generate the optimal tourist route for the user. The generated tourist route is then displayed on a map via API integration with a map app or navigation system. For example, when a user opens a map app on their smartphone, the tourist spots suggested by the generation AI are displayed as route information. This allows the user to easily check the tourist route and receive navigation. The generation AI also recommends parking information near tourist spots. For example, it provides information on parking availability and fees around tourist spots and suggests the most suitable parking lot for the user. If necessary, it also suggests options such as parking + public transportation, taxis, shared cars, rental bicycles, and rental kick scooters.Recommended parking lots offer perks such as parking fee discounts, meal discounts, and discounts on admission to sightseeing spots by scanning license plates and using AI recognition, or by registering 2D codes and numbers. For example, when a user parks their car in a parking lot, the AI ​​recognizes the license plate and automatically applies perks. Automatic parking fee payment is also smoothly implemented. Furthermore, depending on the parking lot and other waiting times, the generation AI recommends nearby sightseeing spots. For example, if a parking lot is full and there is a wait, the generation AI suggests nearby sightseeing spots, allowing the user to make effective use of the waiting time. This allows the tourist route recommendation system to suggest sightseeing routes based on the user's preferences and past history, recommend parking information, grant perks, and make effective use of waiting time, thereby improving the tourist experience. This allows the tourist route recommendation system to suggest sightseeing routes based on the user's preferences and past history, recommend parking information, grant perks, and make effective use of waiting time, thereby improving the tourist experience.

[0029] A tourist route recommendation system according to an embodiment includes a suggestion unit, a display unit, a parking recommendation unit, and a reward granting unit. The suggestion unit uses a generation AI to suggest a tourist route based on a user's preferences and past history. The suggestion unit provides information to the generation AI, for example, by the user circling a desired destination on a map or by entering keywords. The suggestion unit can also analyze the user's past visit history and social media posts to generate an optimal tourist route for the user. The display unit displays the tourist spots suggested by the suggestion unit on a map through API integration with a map app or navigation system. For example, when the user opens a map app on their smartphone, the display unit displays the tourist spots suggested by the generation AI as route information. The display unit also interacts with a navigation system to enable the user to easily check the tourist route and receive navigation. The parking recommendation unit updates parking information around tourist spots in real time and recommends optimal parking lots to the user. The parking recommendation unit, for example, provides information on parking availability and fees around tourist destinations to suggest optimal parking lots to the user. The parking recommendation unit also suggests options such as parking and public transportation, taxis, shared cars, rental bicycles, and rental kick scooters as needed. The reward granting unit grants rewards such as parking fee discounts, meal discounts, and discounts on admission fees to sightseeing spots by reading the license plate and performing AI recognition, or by registering the two-dimensional code and number. For example, when a user parks their car in a parking lot, the reward granting unit recognizes the license plate using AI and automatically applies the reward. The reward granting unit can also smoothly perform automatic payment of parking fees. As a result, the tourist route recommendation system according to the embodiment suggests tourist routes based on the user's preferences and past history, and provides parking information and rewards, thereby improving the tourist experience.

[0030] The suggestion unit can generate a sightseeing route based on the user's preferences and past history. The suggestion unit provides information to the generation AI, for example, by having the user circle places they want to visit on a map or input keywords. The suggestion unit can also analyze the user's past visit history and posts on social media to generate an optimal sightseeing route for the user. This enables more personalized suggestions by generating a sightseeing route based on the user's preferences and past history. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the suggestion unit can input the user's past visit history into the generation AI and have the generation AI generate an optimal sightseeing route.

[0031] The display unit can display the tourist spots suggested by the suggestion unit on a map by linking with a map app or a navigation system via API. For example, when a user opens a map app on their smartphone, the display unit displays the tourist spots suggested by the generation AI as route information. The display unit also links with the navigation system to enable the user to easily check the tourist route and receive navigation. This allows the user to easily check the tourist route and receive navigation by linking with the map app or the navigation system via API. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the tourist spots suggested by the suggestion unit into AI and have the AI ​​execute processing to display them on a map.

[0032] The parking recommendation unit updates parking information around tourist spots in real time and can recommend parking lots suitable for the user. For example, the parking recommendation unit provides information on parking availability and fees around tourist spots and suggests the most suitable parking lot to the user. The parking recommendation unit also suggests options such as parking lots + public transportation, taxis, shared cars, rental bicycles, and rental kick scooters as needed. This allows the most suitable parking lot to be provided to the user by updating parking information in real time. Some or all of the above-mentioned processing in the parking recommendation unit may be performed using AI, or may be performed without using AI. For example, the parking recommendation unit can input information on parking availability around tourist spots into AI and have the AI ​​recommend the most suitable parking lot.

[0033] The reward granting unit can grant rewards such as parking fee discounts, meal discounts, and discounts on admission fees to sightseeing spots by reading the license plate and performing AI recognition, or by registering the 2D code and number. For example, when a user parks a car in a parking lot, the reward granting unit recognizes the license plate using AI and automatically applies rewards. The reward granting unit can also grant rewards such as parking fee discounts, meal discounts, and discounts on admission fees to sightseeing spots by registering the 2D code and number. This improves the convenience of services for users by granting rewards using the license plate or 2D code. Some or all of the above-mentioned processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input image data of the license plate into AI and have the AI ​​apply the reward.

[0034] The reward granting unit can smoothly implement automatic payment of parking fees. For example, when a user parks a car in a parking lot, the reward granting unit recognizes the license plate using AI and automatically pays the parking fee. The reward granting unit can also smoothly implement automatic payment of parking fees by registering a two-dimensional code and number. This allows for smooth automatic payment of parking fees, improving user convenience. Some or all of the above-mentioned processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input image data of the license plate into AI and have the AI ​​execute automatic payment of parking fees.

[0035] The suggestion unit can recommend nearby tourist spots based on parking and other waiting times. For example, when a parking lot is full and a wait occurs, the suggestion unit allows the generation AI to suggest nearby tourist spots, allowing the user to make effective use of the waiting time. The suggestion unit can also recommend nearby tourist spots based on other waiting times (e.g., waiting time to enter a tourist spot). This improves the user's sightseeing experience by recommending nearby tourist spots to make effective use of the waiting time. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the suggestion unit can input waiting time information into the generation AI and cause the generation AI to recommend nearby tourist spots.

[0036] The system includes an analysis unit that analyzes SNS data and provides the user's preferences and past history to the suggestion unit. The analysis unit, for example, analyzes SNS data to identify the user's preferences and past history. The analysis unit, for example, analyzes the user's SNS posts and check-in history to identify tourist spots that may interest the user. The analysis unit can also identify related tourist spots by referring to the activities of the user's friends on SNS. In this way, by analyzing the SNS data, the user's preferences and past history can be more accurately understood and the suggestions can be optimized. Some or all of the above-mentioned processing by the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit may input SNS data into AI and have the AI ​​identify the user's preferences and past history.

[0037] The system includes an update unit, which updates parking lot availability in real time and provides it to the parking lot recommendation unit. The update unit, for example, monitors parking lot availability in real time and updates data. The update unit collects parking lot availability, for example, by using sensor detection or data acquisition from a management system. The update unit can also update parking lot availability in real time using real-time data streaming. This allows the latest parking lot information to be provided to users by updating parking lot availability in real time. Some or all of the above-mentioned processing in the update unit may be performed using AI, or may be performed without using AI. For example, the update unit can input parking lot availability data into AI and have the AI ​​perform real-time updates.

[0038] The device includes a registration unit that can register a two-dimensional code and a number and provide them to the reward granting unit. The registration unit, for example, scans the two-dimensional code and registers the number in a database. For example, when a user scans the two-dimensional code, the registration unit automatically registers the number in the database. The registration unit can also manually input and register the number. By registering the two-dimensional code and the number, the reward granting procedure is simplified and user convenience is improved. Some or all of the above-mentioned processing in the registration unit may be performed using AI, or may be performed without AI. For example, the registration unit can input the two-dimensional code data into AI and have the AI ​​register the number.

[0039] The system includes an additional suggestion unit, which can provide the suggestion unit with additional sightseeing spots based on waiting times. The additional suggestion unit measures waiting times at parking lots and sightseeing spots, for example, and suggests additional sightseeing spots based on that information. For example, when a parking lot is full and a wait occurs, the additional suggestion unit suggests nearby sightseeing spots. Also, when the waiting time to enter a sightseeing spot is long, the additional suggestion unit can suggest nearby sightseeing spots. This enhances the user's sightseeing experience by suggesting additional sightseeing spots based on waiting times. Some or all of the above-mentioned processing in the additional suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the additional suggestion unit can input waiting time information into the generation AI and have the generation AI suggest additional sightseeing spots.

[0040] The suggestion unit can analyze the user's past visit history in detail and generate an optimal sightseeing route based on the frequency of visits and duration of stay. For example, the suggestion unit prioritizes places that the user has frequently visited in the past and incorporates them into the sightseeing route. The suggestion unit can also suggest tourist spots of interest based on places where the user has stayed for a long time. Furthermore, the suggestion unit can analyze patterns of places the user has visited in the past and suggest similar tourist spots. This allows for a more accurate sightseeing route to be generated by analyzing the user's past visit history in detail. Some or all of the above-described processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the suggestion unit can input the user's past visit history data into the generation AI and have the generation AI generate an optimal sightseeing route.

[0041] When making a proposal, the suggestion unit can generate a route from an optimal starting point by taking into account the user's current location information. For example, the suggestion unit can suggest the nearest tourist spot using the user's current location as the starting point. The suggestion unit can also generate an efficient sightseeing route by taking into account travel time from the user's current location. Furthermore, the suggestion unit can also suggest an optimal sightseeing route by taking into account the mode of transportation from the user's current location. In this way, a more efficient sightseeing route can be generated by taking into account the user's current location information. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the suggestion unit can input the user's current location data into the generation AI and cause the generation AI to generate a route from an optimal starting point.

[0042] When making a suggestion, the suggestion unit can provide detailed information (such as history, culture, and event information) about tourist spots based on the user's interests. For example, if the user is interested in history, the suggestion unit can provide detailed information about historical tourist spots. If the user is interested in culture, the suggestion unit can also provide information about cultural events and exhibitions. Furthermore, if the user is interested in a particular event, the suggestion unit can also provide detailed information about that event. This improves the tourist experience by providing detailed information about tourist spots based on the user's interests. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the suggestion unit can input the user's interest data into the generation AI and cause the generation AI to provide detailed information about tourist spots.

[0043] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related tourist spots. For example, the suggestion unit can suggest related tourist spots based on the locations where the user has checked in on social media. The suggestion unit can also analyze the content of the user's social media posts and suggest tourist spots that the user may be interested in. Furthermore, the suggestion unit can also suggest related tourist spots by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant tourist spots can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the suggestion unit can input the user's social media data into the generation AI and have the generation AI suggest related tourist spots.

[0044] When making a suggestion, the suggestion unit can customize the suggestion content by reflecting the user's past feedback. For example, the suggestion unit may prioritize and suggest tourist spots that the user has previously rated highly. The suggestion unit may also exclude tourist spots that the user has previously rated poorly. Furthermore, the suggestion unit can analyze the user's past feedback and suggest tourist spots that match the user's preferences. This makes it possible to suggest sightseeing routes that better suit the user's preferences by reflecting the user's past feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the suggestion unit may input the user's past feedback data into the generation AI and have the generation AI customize the suggestion content.

[0045] When making a suggestion, the suggestion unit can generate an optimal sightseeing route according to the user's travel purpose. For example, if the user is traveling for leisure, the suggestion unit can suggest relaxing tourist spots. Furthermore, if the user is traveling for business, the suggestion unit can also suggest an efficient sightseeing route. Furthermore, if the user is traveling with family, the suggestion unit can also suggest tourist spots that the whole family can enjoy. In this way, a more appropriate sightseeing route can be provided by generating a sightseeing route according to the user's travel purpose. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the suggestion unit can input the user's travel purpose data into the generation AI and cause the generation AI to generate an optimal sightseeing route.

[0046] The display unit can select the optimal display format according to the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display format optimized for the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display format optimized for a large screen. Furthermore, if the user is using a PC, the display unit can also provide a display format optimized for a wide screen. This improves visibility by selecting the optimal display format according to the user's device information. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the user's device information into AI and have the AI ​​select the optimal display format.

[0047] The display unit can update distance information from the user's current location in real time during display and display the optimal route. For example, the display unit can update distance information from the current location in real time while the user is moving and display the optimal route. The display unit can also update distance information from the current location in real time as the user approaches the destination. Furthermore, if the user gets lost, the display unit can update distance information from the current location in real time and display the optimal route again. This makes it possible to always provide the optimal route by updating distance information from the user's current location in real time. Some or all of the above-mentioned processing in the display unit may be performed using AI or without AI. For example, the display unit can input the user's current location data into AI and have the AI ​​display the optimal route.

[0048] When displaying, the display unit can select the optimal display method by referring to the user's past operation history. For example, the display unit can preferentially provide a display method that the user has used in the past. The display unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and provide a display method with high visibility. In this way, by referring to the user's past operation history, a more user-friendly display method can be provided. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the user's past operation history data into AI and have the AI ​​select the optimal display method.

[0049] The display unit can make the display content multilingual according to the user's language setting when displaying. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the display unit can provide the display content in that language. This makes it possible to accommodate a wider range of users by making the display content multilingual according to the user's language setting. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the user's language setting data into AI and have the AI ​​provide the display content in multiple languages.

[0050] The display unit can adjust the color scheme of the map according to the user's visual preferences when displaying the map. For example, the display unit adjusts the color scheme of the map based on the user's preferred colors. The display unit can also suggest an optimal color scheme based on the user's past selection history. Furthermore, if the user selects a specific color scheme, the display unit can display the map in that color scheme. This improves visibility by adjusting the color scheme of the map according to the user's visual preferences. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the user's visual preference data into AI and have the AI ​​adjust the color scheme of the map.

[0051] The display unit can adjust the level of display detail during display, taking into account the remaining battery level of the user's device. For example, when the remaining battery level of the user's device is low, the display unit reduces the level of display detail to reduce battery consumption. The display unit can also display detailed information when the remaining battery level of the user's device is sufficient. Furthermore, the display unit can monitor the remaining battery level of the user's device in real time and dynamically adjust the level of display detail. This makes it possible to provide necessary information while reducing battery consumption by taking into account the remaining battery level of the user's device. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input battery information of the user's device into AI and have the AI ​​adjust the level of display detail.

[0052] The parking recommendation unit can update parking availability in real time and suggest the most suitable parking lot. For example, the parking recommendation unit can monitor parking availability in real time and suggest available parking lots. Furthermore, if parking availability changes, the parking recommendation unit can update the availability in real time and suggest the most suitable parking lot. Furthermore, the parking recommendation unit can suggest the most efficient parking lot based on parking availability. In this way, by updating parking availability in real time, the most suitable parking lot can always be provided. Some or all of the above-mentioned processing in the parking recommendation unit may be performed using AI, or may be performed without using AI. For example, the parking recommendation unit can input parking availability data into AI and have the AI ​​suggest the most suitable parking lot.

[0053] The parking lot recommendation unit can provide detailed parking lot fee information and recommend parking lots according to the user's budget. For example, the parking lot recommendation unit can provide detailed parking lot fee information and suggest parking lots according to the user's budget. Furthermore, if the user sets a budget, the parking lot recommendation unit can also suggest the most suitable parking lot within that budget. Furthermore, the parking lot recommendation unit can update parking lot fee information in real time and suggest parking lots with the best cost performance. This allows the user to select parking lots with the best cost performance by providing parking lots according to the user's budget. Some or all of the above-mentioned processing in the parking lot recommendation unit may be performed using AI, or may be performed without using AI. For example, the parking lot recommendation unit can input parking lot fee information into AI and have the AI ​​suggest parking lots according to the user's budget.

[0054] The parking recommendation unit can provide detailed location information of parking lots and display the shortest route from the user's current location. For example, the parking recommendation unit can provide detailed location information of parking lots and display the shortest route from the user's current location. The parking recommendation unit can also update the location information of parking lots in real time as the user approaches their destination. Furthermore, if the user gets lost, the parking recommendation unit can update the location information of parking lots in real time and display the shortest route again. This allows the user to use parking lots efficiently by providing the shortest route from the user's current location. Some or all of the above-mentioned processing in the parking recommendation unit may be performed using AI, or may be performed without using AI. For example, the parking recommendation unit can input the location information of parking lots into AI and have the AI ​​display the shortest route.

[0055] When recommending a parking lot, the parking lot recommendation unit can suggest the optimal parking lot by taking into consideration the user's vehicle type information. For example, the parking lot recommendation unit can suggest the optimal parking lot by taking into consideration the size of the parking space based on the user's vehicle type information. The parking lot recommendation unit can also suggest the optimal parking lot by taking into consideration the parking lot's height restrictions based on the user's vehicle type information. Furthermore, the parking lot recommendation unit can also suggest parking lots with charging facilities for electric vehicles based on the user's vehicle type information. This makes it possible to provide a more appropriate parking lot by taking into consideration the user's vehicle type information. Some or all of the above-mentioned processing in the parking lot recommendation unit may be performed using AI, or may be performed without using AI. For example, the parking lot recommendation unit can input the user's vehicle type information into AI and have the AI ​​suggest the optimal parking lot.

[0056] When recommending a parking lot, the parking lot recommendation unit can customize the recommendation content by reflecting the user's past parking history. For example, the parking lot recommendation unit may prioritize and recommend parking lots that the user has used in the past. The parking lot recommendation unit can also recommend parking lots that suit the user's preferences based on the user's past parking history. Furthermore, the parking lot recommendation unit can analyze the user's past parking history and recommend the most efficient parking lot. This makes it possible to recommend parking lots that better suit the user's preferences by reflecting the user's past parking history. Some or all of the above-mentioned processing in the parking lot recommendation unit may be performed using AI, or may be performed without using AI. For example, the parking lot recommendation unit can input the user's past parking history data into AI and have the AI ​​customize the recommendation content.

[0057] When recommending a parking lot, the parking lot recommendation unit can suggest the optimal parking lot according to the user's travel purpose. For example, if the user is traveling for leisure, the parking lot recommendation unit can suggest a parking lot close to a tourist spot. Furthermore, if the user is traveling for business, the parking lot recommendation unit can also suggest a parking lot close to a meeting location. Furthermore, if the user is traveling with family, the parking lot recommendation unit can also suggest a parking lot that is easy for the whole family to use. This allows the user to select a more appropriate parking lot by providing the optimal parking lot according to the user's travel purpose. Some or all of the above-mentioned processing in the parking lot recommendation unit may be performed using AI, or may be performed without using AI. For example, the parking lot recommendation unit can input the user's travel purpose data into AI and have the AI ​​suggest the optimal parking lot.

[0058] When granting a reward, the reward granting unit can customize the content of the reward by referring to the user's past usage history. For example, the reward granting unit can prioritize and provide rewards that the user has given high ratings to in the past. The reward granting unit can also provide rewards that match the user's preferences based on rewards that the user has used in the past. Furthermore, the reward granting unit can analyze the user's past usage history and provide the reward that the user will enjoy most. In this way, by referring to the user's past usage history, rewards that better match the user's preferences can be provided. Some or all of the above-described processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input the user's past usage history data into AI and have the AI ​​customize the content of the reward.

[0059] When granting a reward, the reward granting unit can provide the optimal reward by taking into account the user's current location information. For example, the reward granting unit can provide a reward that can be used at a store close to the user's current location. The reward granting unit can also provide a reward that can be used efficiently by taking into account the travel time from the user's current location. Furthermore, the reward granting unit can also provide the optimal reward by taking into account the means of transportation from the user's current location. In this way, by taking into account the user's current location information, more appropriate rewards can be provided. Some or all of the above-described processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input the user's current location data into AI and cause the AI ​​to provide the optimal reward.

[0060] The reward granting unit can improve the content of the reward by reflecting user feedback when granting the reward. For example, the reward granting unit can prioritize and provide rewards that the user has previously rated highly. The reward granting unit can also exclude and provide rewards that the user has previously rated poorly. Furthermore, the reward granting unit can analyze user feedback and provide the reward that is most appreciated. In this way, by reflecting user feedback, rewards that are more appreciated by the user can be provided. Some or all of the above-mentioned processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input user feedback data into AI and have the AI ​​improve the content of the reward.

[0061] The reward granting unit can analyze the user's social media activity and provide a related reward when granting a reward. For example, the reward granting unit can provide a reward related to a location where the user checked in on social media. The reward granting unit can also analyze the content of the user's social media posts and provide rewards that the user may be interested in. Furthermore, the reward granting unit can provide related rewards by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant rewards can be provided. Some or all of the above-described processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input the user's social media data into AI and have the AI ​​provide the related reward.

[0062] When granting a reward, the reward granting unit can customize the reward content by reflecting the user's past feedback. For example, the reward granting unit can prioritize and provide rewards that the user has previously rated highly. The reward granting unit can also exclude and provide rewards that the user has previously rated poorly. Furthermore, the reward granting unit can analyze the user's past feedback and provide the reward that the user will enjoy most. In this way, by reflecting the user's past feedback, rewards that better suit the user's preferences can be provided. Some or all of the above-described processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input the user's past feedback data into AI and have the AI ​​customize the reward content.

[0063] The reward granting unit can provide the optimal reward according to the user's travel purpose when granting the reward. For example, if the user is traveling for leisure, the reward granting unit can provide a reward that can be used at tourist spots. Furthermore, if the user is traveling for business, the reward granting unit can also provide a reward that can be used at conference locations. Furthermore, if the user is traveling as a family, the reward granting unit can also provide a reward that can be used by the entire family. This allows for more appropriate rewards to be provided by providing the optimal reward according to the user's travel purpose. Some or all of the above-described processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input the user's travel purpose data into AI and have the AI ​​provide the optimal reward.

[0064] During the analysis, the analysis unit can perform a detailed analysis of the user's past SNS posts to identify the user's preferences. For example, the analysis unit can analyze the user's past SNS posts to identify tourist spots that the user is likely to be interested in. The analysis unit can also identify tourist spots that match the user's preferences based on the user's SNS check-in history. Furthermore, the analysis unit can also identify tourist spots that the user is likely to be interested in by referring to the activities of the user's friends on the SNS. This allows for a detailed analysis of the user's past SNS posts to provide more accurate analysis results. Some or all of the above-described processing by the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's past SNS post data into AI and have the AI ​​identify the user's preferences.

[0065] During analysis, the analysis unit can provide optimal analysis results by taking into account the user's current location information. For example, the analysis unit can provide analysis results by prioritizing tourist spots close to the user's current location. The analysis unit can also provide efficient tourist spots as analysis results by taking into account travel time from the user's current location. Furthermore, the analysis unit can provide optimal tourist spots as analysis results by taking into account the means of transportation from the user's current location. In this way, by taking into account the user's current location information, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's current location data into AI and have the AI ​​provide optimal analysis results.

[0066] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past operation history. For example, the analysis unit can provide tourist spots that match the user's preferences as analysis results based on the user's past operation history. The analysis unit can also analyze the user's past operation history and provide the most efficient tourist spots as analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past operation history. In this way, by referring to the user's past operation history, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's past operation history data into AI and have the AI ​​improve the accuracy of the analysis.

[0067] During the analysis, the analysis unit can analyze the user's social media activity and provide relevant analysis results. For example, the analysis unit can provide analysis results regarding places where the user has checked in on social media. The analysis unit can also analyze the content of the user's social media posts and provide analysis results regarding related tourist spots and stores. Furthermore, the analysis unit can provide analysis results regarding related places and events by referring to the activities of the user's friends on social media. This makes it possible to provide more relevant analysis results by analyzing the user's social media activity. Some or all of the above-described processing by the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's social media data into AI and have the AI ​​provide relevant analysis results.

[0068] During analysis, the analysis unit can customize the analysis content by reflecting the user's past feedback. For example, the analysis unit can provide analysis results by prioritizing tourist spots that the user has previously rated highly. The analysis unit can also provide analysis results by excluding tourist spots that the user has previously rated poorly. Furthermore, the analysis unit can analyze the user's past feedback and provide the most popular tourist spots as analysis results. In this way, by reflecting the user's past feedback, it is possible to provide analysis results that are more suited to the user's preferences. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's past feedback data into AI and have the AI ​​customize the analysis content.

[0069] During analysis, the analysis unit can provide optimal analysis results according to the user's travel purpose. For example, if the user is traveling for leisure, the analysis unit can provide relaxing tourist spots as the analysis result. Furthermore, if the user is traveling for business, the analysis unit can also provide efficient tourist spots as the analysis result. Furthermore, if the user is traveling with family, the analysis unit can also provide tourist spots that the whole family can enjoy as the analysis result. This allows for more appropriate information to be provided by providing optimal analysis results according to the user's travel purpose. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's travel purpose data into AI and have the AI ​​provide optimal analysis results.

[0070] The update unit can adjust the update frequency by referring to the user's past operation history when updating. For example, the update unit prioritizes updating information that the user uses frequently. The update unit can also suggest an optimal update frequency based on the user's past operation history. Furthermore, the update unit can analyze the user's past operation history and prioritize updating necessary information. In this way, by referring to the user's past operation history, a more appropriate update frequency can be provided. Some or all of the above-mentioned processing in the update unit may be performed using AI, or may be performed without using AI. For example, the update unit can input the user's past operation history data into AI and have the AI ​​adjust the update frequency.

[0071] The update unit can provide optimal update content by taking into account the user's current location information when updating. For example, the update unit prioritizes updating information related to the user's current location. The update unit can also update efficient information by taking into account travel time from the user's current location. Furthermore, the update unit can also update optimal information by taking into account the means of transportation from the user's current location. This makes it possible to provide more appropriate information by taking into account the user's current location information. Some or all of the above-described processing in the update unit may be performed using AI, or may be performed without using AI. For example, the update unit can input the user's current location data into AI and have the AI ​​provide optimal update content.

[0072] The update unit can analyze the user's social media activity at the time of updating and provide relevant updates. For example, the update unit prioritizes updating information related to places where the user has checked in on social media. The update unit can also analyze the content of the user's social media posts and prioritize updating information that is likely to be of interest to the user. Furthermore, the update unit can also prioritize updating relevant information based on the activities of the user's friends on social media. In this way, more relevant information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the update unit may be performed using AI, or may be performed without using AI. For example, the update unit can input the user's social media data into AI and have the AI ​​provide relevant updates.

[0073] When updating, the update unit can customize the update content by reflecting the user's past feedback. For example, the update unit prioritizes updating information that the user has previously rated highly. The update unit can also update information by excluding information that the user has previously rated poorly. Furthermore, the update unit can analyze the user's past feedback and prioritize updating the information that the user finds most pleasing. In this way, by reflecting the user's past feedback, it is possible to provide information that is more suited to the user's preferences. Some or all of the above-mentioned processing in the update unit may be performed using AI, or may be performed without using AI. For example, the update unit can input the user's past feedback data into AI and have the AI ​​customize the update content.

[0074] The registration unit can improve the accuracy of registration by referring to the user's past operation history during registration. The registration unit can, for example, suggest optimal registration content based on the user's past operation history. The registration unit can also analyze the user's past operation history to improve the accuracy of registration. Furthermore, the registration unit can also simplify the registration procedure by referring to the user's past operation history. In this way, by referring to the user's past operation history, a more accurate registration procedure can be provided. Some or all of the above-mentioned processing in the registration unit may be performed using AI, or may be performed without using AI. For example, the registration unit can input the user's past operation history data into AI and have the AI ​​improve the accuracy of registration.

[0075] The registration unit can provide optimal registration content by taking into account the user's current location information at the time of registration. For example, the registration unit prioritizes registration of information related to the user's current location. The registration unit can also provide efficient registration content by taking into account travel time from the user's current location. Furthermore, the registration unit can also provide optimal registration content by taking into account the means of transportation from the user's current location. In this way, more appropriate registration content can be provided by taking into account the user's current location information. Some or all of the above-described processing in the registration unit may be performed using AI, or may be performed without using AI. For example, the registration unit can input the user's current location data into AI and have the AI ​​provide optimal registration content.

[0076] At the time of registration, the registration unit can analyze the user's social media activity and provide relevant registration content. For example, the registration unit can prioritize registering information related to places where the user has checked in on social media. The registration unit can also analyze the content of the user's social media posts and prioritize registering information that is likely to be of interest to the user. Furthermore, the registration unit can also prioritize registering relevant information based on the activities of the user's friends on social media. This makes it possible to provide more relevant information by analyzing the user's social media activity. Some or all of the above-described processing in the registration unit may be performed using AI, or may be performed without using AI. For example, the registration unit can input the user's social media data into AI and have the AI ​​provide the relevant registration content.

[0077] At the time of registration, the registration unit can customize the registered content by reflecting the user's past feedback. For example, the registration unit prioritizes registering information that the user has previously given a high rating. The registration unit can also exclude and register information that the user has previously given a low rating. Furthermore, the registration unit can analyze the user's past feedback and prioritize registering the information that the user finds most pleasing. In this way, by reflecting the user's past feedback, it is possible to provide information that is more suited to the user's preferences. Some or all of the above-described processing in the registration unit may be performed using AI, or may be performed without using AI. For example, the registration unit can input the user's past feedback data into AI and have the AI ​​customize the registered content.

[0078] When making an additional suggestion, the additional suggestion unit can analyze the user's past visit history in detail and suggest optimal tourist spots. For example, the additional suggestion unit can suggest related tourist spots based on tourist spots that the user has visited in the past. The additional suggestion unit can also analyze the user's past visit history and suggest tourist spots that the user may be interested in. Furthermore, the additional suggestion unit can suggest the most efficient tourist spots based on the user's past visit history. This allows for more accurate suggestion of tourist spots by analyzing the user's past visit history in detail. Some or all of the above-mentioned processing in the additional suggestion unit may be performed using or without the generation AI. For example, the additional suggestion unit can input the user's past visit history data into the generation AI and have the generation AI suggest optimal tourist spots.

[0079] When making a suggestion, the suggestion unit can suggest optimal tourist spots by taking into account the user's current location information. For example, the suggestion unit can prioritize suggesting tourist spots close to the user's current location. The suggestion unit can also suggest efficient tourist spots by taking into account travel time from the user's current location. Furthermore, the suggestion unit can also suggest optimal tourist spots by taking into account the mode of transportation from the user's current location. This makes it possible to suggest more appropriate tourist spots by taking into account the user's current location information. Some or all of the above-described processing in the suggestion unit can be performed using or without the generation AI. For example, the suggestion unit can input the user's current location data into the generation AI and cause the generation AI to suggest optimal tourist spots.

[0080] When suggesting an additional item, the additional suggestion unit can provide detailed information about tourist spots based on the user's interests. For example, the additional suggestion unit can provide detailed information about tourist spots that the user is interested in. The additional suggestion unit can also provide detailed information about stores that the user frequently visits. Furthermore, the additional suggestion unit can provide detailed information about tourist spots that the user may be interested in based on the user's past search history. This improves the tourist experience by providing detailed information about tourist spots based on the user's interests. Some or all of the above-mentioned processing in the additional suggestion unit may be performed using or without the generation AI. For example, the additional suggestion unit can input the user's interest data into the generation AI and cause the generation AI to provide detailed information about tourist spots.

[0081] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related tourist spots. For example, the suggestion unit can suggest related tourist spots based on the locations where the user has checked in on social media. The suggestion unit can also analyze the content of the user's social media posts and suggest tourist spots that the user may be interested in. Furthermore, the suggestion unit can also suggest related tourist spots based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant tourist spots can be suggested. Some or all of the above-described processing in the suggestion unit can be performed using or without the generation AI. For example, the suggestion unit can input the user's social media data into the generation AI and have the generation AI suggest related tourist spots.

[0082] When making an additional suggestion, the additional suggestion unit can customize the suggestion content by reflecting the user's past feedback. For example, the additional suggestion unit prioritizes suggesting tourist spots that the user has previously rated highly. The additional suggestion unit can also exclude tourist spots that the user has previously rated poorly. Furthermore, the additional suggestion unit can analyze the user's past feedback and suggest the tourist spots that the user will enjoy the most. In this way, by reflecting the user's past feedback, tourist spots that better suit the user's preferences can be suggested. Some or all of the above-mentioned processing in the additional suggestion unit may be performed using or without the generation AI. For example, the additional suggestion unit can input the user's past feedback data into the generation AI and have the generation AI customize the suggestion content.

[0083] When making an additional suggestion, the additional suggestion unit can suggest optimal sightseeing spots according to the user's travel purpose. For example, if the user is traveling for leisure, the additional suggestion unit can suggest relaxing tourist spots. Furthermore, if the user is traveling for business, the additional suggestion unit can also suggest efficient tourist spots. Furthermore, if the user is traveling with family, the additional suggestion unit can also suggest tourist spots that the whole family can enjoy. In this way, by suggesting optimal sightseeing spots according to the user's travel purpose, more appropriate sightseeing spots can be provided. Some or all of the above-mentioned processing in the additional suggestion unit may be performed using or without the generation AI. For example, the additional suggestion unit can input the user's travel purpose data into the generation AI and cause the generation AI to suggest optimal sightseeing spots.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The display unit can display the tourist spots suggested by the suggestion unit on a map by linking with a map app or navigation system via API, and can also adjust the color scheme of the map according to the user's visual preferences. For example, the display unit can adjust the color scheme of the map based on the user's preferred colors. The display unit can also suggest an optimal color scheme based on the user's past selection history. Furthermore, if the user selects a specific color scheme, the display unit can display the map in that color scheme. This improves visibility by adjusting the color scheme of the map according to the user's visual preferences.

[0086] The parking recommendation unit updates parking information around tourist spots in real time and can recommend parking lots that are suitable for the user, but it can also suggest the most suitable parking lot by taking into account the user's vehicle type information. For example, it can suggest the most suitable parking lot by taking into account the size of the parking space based on the user's vehicle type information. It can also suggest the most suitable parking lot by taking into account the height restrictions of the parking lot based on the user's vehicle type information. It can also suggest parking lots with charging facilities for electric vehicles based on the user's vehicle type information. In this way, it is possible to provide a more suitable parking lot by taking into account the user's vehicle type information.

[0087] The system is equipped with an analysis unit that can analyze SNS data and provide the user's preferences and past history to the suggestion unit, and can also analyze the user's social media activity to suggest related tourist spots. For example, related tourist spots can be suggested based on the locations where the user has checked in on social media. It can also analyze the content of the user's social media posts to suggest tourist spots that may interest the user. It can also suggest related tourist spots based on the activities of the user's friends on social media. By analyzing the user's social media activity, it is possible to suggest more relevant tourist spots.

[0088] The system is provided with a registration unit, which can register the two-dimensional code and number and provide them to the reward granting unit, and can also improve the accuracy of registration by referring to the user's past operation history. For example, the registration unit can suggest optimal registration content based on the user's past operation history. The registration accuracy can also be improved by analyzing the user's past operation history. Furthermore, the registration procedure can be simplified by referring to the user's past operation history. In this way, by referring to the user's past operation history, a more accurate registration procedure can be provided.

[0089] The suggestion unit can generate a sightseeing route based on the user's preferences and past history, but can also generate an optimal sightseeing route according to the user's travel purpose. For example, if the user is traveling for leisure, it can suggest relaxing tourist spots. If the user is traveling for business, it can also suggest an efficient sightseeing route. Furthermore, if the user is traveling with family, it can also suggest tourist spots that the whole family can enjoy. In this way, by generating a sightseeing route according to the user's travel purpose, it is possible to provide a more appropriate sightseeing route.

[0090] The processing flow of the first embodiment will be briefly explained below.

[0091] Step 1: The suggestion unit uses the generation AI to suggest sightseeing routes based on the user's preferences and past history. Users provide information to the generation AI by circling the places they want to visit on a map or by entering keywords. The suggestion unit also analyzes the user's past visit history and posts on social media to generate the optimal sightseeing route for the user. Step 2: The display unit displays the tourist spots suggested by the suggestion unit on a map through API integration with a map app or navigation system. When the user opens the map app on their smartphone, the tourist spots suggested by the generation AI are displayed as route information. The display unit also works with the navigation system to allow the user to easily check the tourist route and receive navigation. Step 3: The parking recommendation unit updates parking information around tourist spots in real time and recommends the most suitable parking lot to the user. It provides information on parking availability and fees around tourist spots and suggests the most suitable parking lot to the user. The parking recommendation unit also suggests options such as parking + public transportation, taxis, shared cars, rental bicycles, and rental kick scooters as needed. Step 4: The rewards granting unit reads the license plate and performs AI recognition, or registers the 2D code and number, to grant rewards such as discounts on parking fees, meal discounts, and discounts on admission fees to tourist spots. When the user parks their car in the parking lot, the license plate is recognized by AI and the rewards are automatically applied. The rewards granting unit can also smoothly carry out automatic payment of parking fees.

[0092] (Example 2) The tourist route recommendation system according to an embodiment of the present invention uses a generation AI to propose tourist routes based on the user's preferences and past history, and provides parking information and special offers. The tourist route recommendation system allows users to provide information to the generation AI by circling desired destinations on a map or entering keywords. The generation AI then proposes tourist routes based on the user's preferences and past history, and displays the route information on the map via API integration with map apps and navigation systems. Tourist spot information is generated by AI using social media, eliminating intentional commercialization. Furthermore, the system recommends parking information near tourist spots to the user, and suggests options such as parking + public transportation, taxis, shared cars, rental bicycles, and rental kick scooters as needed. Recommended parking lots also offer special offers such as parking fee discounts, meal discounts, and discounts on tourist spot admission fees by scanning license plates and registering 2D codes and numbers. Automatic parking fee payment is also smoothly implemented. Furthermore, the generation AI recommends nearby tourist spots based on parking and other waiting times. For example, in a tourist route recommendation system, a user can circle a place they want to visit on a map or enter keywords. This information is then input into a generation AI. The generation AI then suggests a tourist route based on the user's preferences and past history. For example, it analyzes tourist spots the user has previously visited and posts on social media to generate the optimal tourist route for the user. The generated tourist route is then displayed on a map via API integration with a map app or navigation system. For example, when a user opens a map app on their smartphone, the tourist spots suggested by the generation AI are displayed as route information. This allows the user to easily check the tourist route and receive navigation. The generation AI also recommends parking information near tourist spots. For example, it provides information on parking availability and fees around tourist spots and suggests the most suitable parking lot for the user. If necessary, it also suggests options such as parking + public transportation, taxis, shared cars, rental bicycles, and rental kick scooters.Recommended parking lots offer perks such as parking fee discounts, meal discounts, and discounts on admission to sightseeing spots by scanning license plates and using AI recognition, or by registering 2D codes and numbers. For example, when a user parks their car in a parking lot, the AI ​​recognizes the license plate and automatically applies perks. Automatic parking fee payment is also smoothly implemented. Furthermore, depending on the parking lot and other waiting times, the generation AI recommends nearby sightseeing spots. For example, if a parking lot is full and there is a wait, the generation AI suggests nearby sightseeing spots, allowing the user to make effective use of the waiting time. This allows the tourist route recommendation system to suggest sightseeing routes based on the user's preferences and past history, recommend parking information, grant perks, and make effective use of waiting time, thereby improving the tourist experience. This allows the tourist route recommendation system to suggest sightseeing routes based on the user's preferences and past history, recommend parking information, grant perks, and make effective use of waiting time, thereby improving the tourist experience.

[0093] A tourist route recommendation system according to an embodiment includes a suggestion unit, a display unit, a parking recommendation unit, and a reward granting unit. The suggestion unit uses a generation AI to suggest a tourist route based on a user's preferences and past history. The suggestion unit provides information to the generation AI, for example, by the user circling a desired destination on a map or by entering keywords. The suggestion unit can also analyze the user's past visit history and social media posts to generate an optimal tourist route for the user. The display unit displays the tourist spots suggested by the suggestion unit on a map through API integration with a map app or navigation system. For example, when the user opens a map app on their smartphone, the display unit displays the tourist spots suggested by the generation AI as route information. The display unit also interacts with a navigation system to enable the user to easily check the tourist route and receive navigation. The parking recommendation unit updates parking information around tourist spots in real time and recommends optimal parking lots to the user. The parking recommendation unit, for example, provides information on parking availability and fees around tourist destinations to suggest optimal parking lots to the user. The parking recommendation unit also suggests options such as parking and public transportation, taxis, shared cars, rental bicycles, and rental kick scooters as needed. The reward granting unit grants rewards such as parking fee discounts, meal discounts, and discounts on admission fees to sightseeing spots by reading the license plate and performing AI recognition, or by registering the two-dimensional code and number. For example, when a user parks their car in a parking lot, the reward granting unit recognizes the license plate using AI and automatically applies the reward. The reward granting unit can also smoothly perform automatic payment of parking fees. As a result, the tourist route recommendation system according to the embodiment suggests tourist routes based on the user's preferences and past history, and provides parking information and rewards, thereby improving the tourist experience.

[0094] The suggestion unit can generate a sightseeing route based on the user's preferences and past history. The suggestion unit provides information to the generation AI, for example, by having the user circle places they want to visit on a map or input keywords. The suggestion unit can also analyze the user's past visit history and posts on social media to generate an optimal sightseeing route for the user. This enables more personalized suggestions by generating a sightseeing route based on the user's preferences and past history. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the suggestion unit can input the user's past visit history into the generation AI and have the generation AI generate an optimal sightseeing route.

[0095] The display unit can display the tourist spots suggested by the suggestion unit on a map by linking with a map app or a navigation system via API. For example, when a user opens a map app on their smartphone, the display unit displays the tourist spots suggested by the generation AI as route information. The display unit also links with the navigation system to enable the user to easily check the tourist route and receive navigation. This allows the user to easily check the tourist route and receive navigation by linking with the map app or the navigation system via API. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the tourist spots suggested by the suggestion unit into AI and have the AI ​​execute processing to display them on a map.

[0096] The parking recommendation unit updates parking information around tourist spots in real time and can recommend parking lots suitable for the user. For example, the parking recommendation unit provides information on parking availability and fees around tourist spots and suggests the most suitable parking lot to the user. The parking recommendation unit also suggests options such as parking lots + public transportation, taxis, shared cars, rental bicycles, and rental kick scooters as needed. This allows the most suitable parking lot to be provided to the user by updating parking information in real time. Some or all of the above-mentioned processing in the parking recommendation unit may be performed using AI, or may be performed without using AI. For example, the parking recommendation unit can input information on parking availability around tourist spots into AI and have the AI ​​recommend the most suitable parking lot.

[0097] The reward granting unit can grant rewards such as parking fee discounts, meal discounts, and discounts on admission fees to sightseeing spots by reading the license plate and performing AI recognition, or by registering the 2D code and number. For example, when a user parks a car in a parking lot, the reward granting unit recognizes the license plate using AI and automatically applies rewards. The reward granting unit can also grant rewards such as parking fee discounts, meal discounts, and discounts on admission fees to sightseeing spots by registering the 2D code and number. This improves the convenience of services for users by granting rewards using the license plate or 2D code. Some or all of the above-mentioned processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input image data of the license plate into AI and have the AI ​​apply the reward.

[0098] The reward granting unit can smoothly implement automatic payment of parking fees. For example, when a user parks a car in a parking lot, the reward granting unit recognizes the license plate using AI and automatically pays the parking fee. The reward granting unit can also smoothly implement automatic payment of parking fees by registering a two-dimensional code and number. This allows for smooth automatic payment of parking fees, improving user convenience. Some or all of the above-mentioned processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input image data of the license plate into AI and have the AI ​​execute automatic payment of parking fees.

[0099] The suggestion unit can recommend nearby tourist spots based on parking and other waiting times. For example, when a parking lot is full and a wait occurs, the suggestion unit allows the generation AI to suggest nearby tourist spots, allowing the user to make effective use of the waiting time. The suggestion unit can also recommend nearby tourist spots based on other waiting times (e.g., waiting time to enter a tourist spot). This improves the user's sightseeing experience by recommending nearby tourist spots to make effective use of the waiting time. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the suggestion unit can input waiting time information into the generation AI and cause the generation AI to recommend nearby tourist spots.

[0100] The system includes an analysis unit that analyzes SNS data and provides the user's preferences and past history to the suggestion unit. The analysis unit, for example, analyzes SNS data to identify the user's preferences and past history. The analysis unit, for example, analyzes the user's SNS posts and check-in history to identify tourist spots that may interest the user. The analysis unit can also identify related tourist spots by referring to the activities of the user's friends on SNS. In this way, by analyzing the SNS data, the user's preferences and past history can be more accurately understood and the suggestions can be optimized. Some or all of the above-mentioned processing by the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit may input SNS data into AI and have the AI ​​identify the user's preferences and past history.

[0101] The system includes an update unit, which updates parking lot availability in real time and provides it to the parking lot recommendation unit. The update unit, for example, monitors parking lot availability in real time and updates data. The update unit collects parking lot availability, for example, by using sensor detection or data acquisition from a management system. The update unit can also update parking lot availability in real time using real-time data streaming. This allows the latest parking lot information to be provided to users by updating parking lot availability in real time. Some or all of the above-mentioned processing in the update unit may be performed using AI, or may be performed without using AI. For example, the update unit can input parking lot availability data into AI and have the AI ​​perform real-time updates.

[0102] The device includes a registration unit that can register a two-dimensional code and a number and provide them to the reward granting unit. The registration unit, for example, scans the two-dimensional code and registers the number in a database. For example, when a user scans the two-dimensional code, the registration unit automatically registers the number in the database. The registration unit can also manually input and register the number. By registering the two-dimensional code and the number, the reward granting procedure is simplified and user convenience is improved. Some or all of the above-mentioned processing in the registration unit may be performed using AI, or may be performed without AI. For example, the registration unit can input the two-dimensional code data into AI and have the AI ​​register the number.

[0103] The system includes an additional suggestion unit, which can provide the suggestion unit with additional sightseeing spots based on waiting times. The additional suggestion unit measures waiting times at parking lots and sightseeing spots, for example, and suggests additional sightseeing spots based on that information. For example, when a parking lot is full and a wait occurs, the additional suggestion unit suggests nearby sightseeing spots. Also, when the waiting time to enter a sightseeing spot is long, the additional suggestion unit can suggest nearby sightseeing spots. This enhances the user's sightseeing experience by suggesting additional sightseeing spots based on waiting times. Some or all of the above-mentioned processing in the additional suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the additional suggestion unit can input waiting time information into the generation AI and have the generation AI suggest additional sightseeing spots.

[0104] The suggestion unit can estimate the user's emotions and adjust the proposed sightseeing route based on the estimated user emotions. The suggestion unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, if the user is feeling stressed, the suggestion unit can prioritize and suggest relaxing tourist spots. If the user is excited, the suggestion unit can suggest sightseeing routes that include active activities. Furthermore, if the user is tired, the suggestion unit can suggest sightseeing routes that include many rest stops. This allows for more personalized suggestions by adjusting the proposed sightseeing route based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using the generation AI, or without the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI adjust the proposed sightseeing route.

[0105] The suggestion unit can analyze the user's past visit history in detail and generate an optimal sightseeing route based on the frequency of visits and duration of stay. For example, the suggestion unit prioritizes places that the user has frequently visited in the past and incorporates them into the sightseeing route. The suggestion unit can also suggest tourist spots of interest based on places where the user has stayed for a long time. Furthermore, the suggestion unit can analyze patterns of places the user has visited in the past and suggest similar tourist spots. This allows for a more accurate sightseeing route to be generated by analyzing the user's past visit history in detail. Some or all of the above-described processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the suggestion unit can input the user's past visit history data into the generation AI and have the generation AI generate an optimal sightseeing route.

[0106] When making a proposal, the suggestion unit can generate a route from an optimal starting point by taking into account the user's current location information. For example, the suggestion unit can suggest the nearest tourist spot using the user's current location as the starting point. The suggestion unit can also generate an efficient sightseeing route by taking into account travel time from the user's current location. Furthermore, the suggestion unit can also suggest an optimal sightseeing route by taking into account the mode of transportation from the user's current location. In this way, a more efficient sightseeing route can be generated by taking into account the user's current location information. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the suggestion unit can input the user's current location data into the generation AI and cause the generation AI to generate a route from an optimal starting point.

[0107] When making a suggestion, the suggestion unit can provide detailed information (such as history, culture, and event information) about tourist spots based on the user's interests. For example, if the user is interested in history, the suggestion unit can provide detailed information about historical tourist spots. If the user is interested in culture, the suggestion unit can also provide information about cultural events and exhibitions. Furthermore, if the user is interested in a particular event, the suggestion unit can also provide detailed information about that event. This improves the tourist experience by providing detailed information about tourist spots based on the user's interests. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the suggestion unit can input the user's interest data into the generation AI and cause the generation AI to provide detailed information about tourist spots.

[0108] The suggestion unit can estimate the user's emotions and prioritize sightseeing routes based on the estimated user emotions. The suggestion unit estimates emotions using, for example, facial expression recognition or voice analysis of the user. For example, if the user is relaxed, the suggestion unit can prioritize relaxing tourist spots. If the user is excited, the suggestion unit can prioritize sightseeing routes that include active activities. If the user is tired, the suggestion unit can prioritize sightseeing routes that include many rest stops. This enables more personalized suggestions by prioritizing sightseeing routes based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using the generation AI, or without the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of sightseeing routes.

[0109] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related tourist spots. For example, the suggestion unit can suggest related tourist spots based on the locations where the user has checked in on social media. The suggestion unit can also analyze the content of the user's social media posts and suggest tourist spots that the user may be interested in. Furthermore, the suggestion unit can also suggest related tourist spots by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant tourist spots can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the suggestion unit can input the user's social media data into the generation AI and have the generation AI suggest related tourist spots.

[0110] When making a suggestion, the suggestion unit can customize the suggestion content by reflecting the user's past feedback. For example, the suggestion unit may prioritize and suggest tourist spots that the user has previously rated highly. The suggestion unit may also exclude tourist spots that the user has previously rated poorly. Furthermore, the suggestion unit can analyze the user's past feedback and suggest tourist spots that match the user's preferences. This makes it possible to suggest sightseeing routes that better suit the user's preferences by reflecting the user's past feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the suggestion unit may input the user's past feedback data into the generation AI and have the generation AI customize the suggestion content.

[0111] When making a suggestion, the suggestion unit can generate an optimal sightseeing route according to the user's travel purpose. For example, if the user is traveling for leisure, the suggestion unit can suggest relaxing tourist spots. Furthermore, if the user is traveling for business, the suggestion unit can also suggest an efficient sightseeing route. Furthermore, if the user is traveling with family, the suggestion unit can also suggest tourist spots that the whole family can enjoy. In this way, a more appropriate sightseeing route can be provided by generating a sightseeing route according to the user's travel purpose. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the suggestion unit can input the user's travel purpose data into the generation AI and cause the generation AI to generate an optimal sightseeing route.

[0112] The display unit can estimate the user's emotions and adjust the style of the map display based on the estimated user's emotions. For example, if the user is nervous, the display unit can provide a map display with subdued colors. If the user is relaxed, the display unit can also provide a map display with bright colors. Furthermore, if the user is excited, the display unit can also provide a visually stimulating map display. This allows for adjusting the style of the map display based on the user's emotions to provide a more comfortable user experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the display unit may be performed using AI, or may be performed without AI. For example, the display unit can input the user's emotion data into AI and have the AI ​​adjust the style of the map display.

[0113] The display unit can select the optimal display format according to the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display format optimized for the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display format optimized for a large screen. Furthermore, if the user is using a PC, the display unit can also provide a display format optimized for a wide screen. This improves visibility by selecting the optimal display format according to the user's device information. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the user's device information into AI and have the AI ​​select the optimal display format.

[0114] The display unit can update distance information from the user's current location in real time during display and display the optimal route. For example, the display unit can update distance information from the current location in real time while the user is moving and display the optimal route. The display unit can also update distance information from the current location in real time as the user approaches the destination. Furthermore, if the user gets lost, the display unit can update distance information from the current location in real time and display the optimal route again. This makes it possible to always provide the optimal route by updating distance information from the user's current location in real time. Some or all of the above-mentioned processing in the display unit may be performed using AI or without AI. For example, the display unit can input the user's current location data into AI and have the AI ​​display the optimal route.

[0115] When displaying, the display unit can select the optimal display method by referring to the user's past operation history. For example, the display unit can preferentially provide a display method that the user has used in the past. The display unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and provide a display method with high visibility. In this way, by referring to the user's past operation history, a more user-friendly display method can be provided. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the user's past operation history data into AI and have the AI ​​select the optimal display method.

[0116] The display unit can estimate the user's emotions and determine the priority of map display based on the estimated user's emotions. For example, when the user is nervous, the display unit can prioritize displaying important information. Furthermore, when the user is relaxed, the display unit can prioritize displaying detailed information. Furthermore, when the user is in a hurry, the display unit can prioritize displaying information that emphasizes the main points. By determining the priority of map display based on the user's emotions, more important information can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using AI, or without AI. For example, the display unit can input the user's emotion data into AI and have the AI ​​determine the priority of map display.

[0117] The display unit can make the display content multilingual according to the user's language setting when displaying. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the display unit can provide the display content in that language. This makes it possible to accommodate a wider range of users by making the display content multilingual according to the user's language setting. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the user's language setting data into AI and have the AI ​​provide the display content in multiple languages.

[0118] The display unit can adjust the color scheme of the map according to the user's visual preferences when displaying the map. For example, the display unit adjusts the color scheme of the map based on the user's preferred colors. The display unit can also suggest an optimal color scheme based on the user's past selection history. Furthermore, if the user selects a specific color scheme, the display unit can display the map in that color scheme. This improves visibility by adjusting the color scheme of the map according to the user's visual preferences. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the user's visual preference data into AI and have the AI ​​adjust the color scheme of the map.

[0119] The display unit can adjust the level of display detail during display, taking into account the remaining battery level of the user's device. For example, when the remaining battery level of the user's device is low, the display unit reduces the level of display detail to reduce battery consumption. The display unit can also display detailed information when the remaining battery level of the user's device is sufficient. Furthermore, the display unit can monitor the remaining battery level of the user's device in real time and dynamically adjust the level of display detail. This makes it possible to provide necessary information while reducing battery consumption by taking into account the remaining battery level of the user's device. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input battery information of the user's device into AI and have the AI ​​adjust the level of display detail.

[0120] The parking recommendation unit can estimate the user's emotions and adjust the parking recommendation content based on the estimated user emotions. For example, if the user is feeling stressed, the parking recommendation unit can prioritize suggesting vacant parking lots. Furthermore, if the user is relaxed, the parking recommendation unit can prioritize suggesting parking lots with low fees. Furthermore, if the user is in a hurry, the parking recommendation unit can prioritize suggesting the nearest parking lot. This allows for adjusting the parking recommendation content based on the user's emotions to suggest a more appropriate parking lot. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the parking recommendation unit may be performed using AI, or may be performed without AI. For example, the parking recommendation unit can input the user's emotion data into AI and have the AI ​​adjust the parking recommendation content.

[0121] The parking recommendation unit can update parking availability in real time and suggest the most suitable parking lot. For example, the parking recommendation unit can monitor parking availability in real time and suggest available parking lots. Furthermore, if parking availability changes, the parking recommendation unit can update the availability in real time and suggest the most suitable parking lot. Furthermore, the parking recommendation unit can suggest the most efficient parking lot based on parking availability. In this way, by updating parking availability in real time, the most suitable parking lot can always be provided. Some or all of the above-mentioned processing in the parking recommendation unit may be performed using AI, or may be performed without using AI. For example, the parking recommendation unit can input parking availability data into AI and have the AI ​​suggest the most suitable parking lot.

[0122] The parking lot recommendation unit can provide detailed parking lot fee information and recommend parking lots according to the user's budget. For example, the parking lot recommendation unit can provide detailed parking lot fee information and suggest parking lots according to the user's budget. Furthermore, if the user sets a budget, the parking lot recommendation unit can also suggest the most suitable parking lot within that budget. Furthermore, the parking lot recommendation unit can update parking lot fee information in real time and suggest parking lots with the best cost performance. This allows the user to select parking lots with the best cost performance by providing parking lots according to the user's budget. Some or all of the above-mentioned processing in the parking lot recommendation unit may be performed using AI, or may be performed without using AI. For example, the parking lot recommendation unit can input parking lot fee information into AI and have the AI ​​suggest parking lots according to the user's budget.

[0123] The parking recommendation unit can provide detailed location information of parking lots and display the shortest route from the user's current location. For example, the parking recommendation unit can provide detailed location information of parking lots and display the shortest route from the user's current location. The parking recommendation unit can also update the location information of parking lots in real time as the user approaches their destination. Furthermore, if the user gets lost, the parking recommendation unit can update the location information of parking lots in real time and display the shortest route again. This allows the user to use parking lots efficiently by providing the shortest route from the user's current location. Some or all of the above-mentioned processing in the parking recommendation unit may be performed using AI, or may be performed without using AI. For example, the parking recommendation unit can input the location information of parking lots into AI and have the AI ​​display the shortest route.

[0124] The parking lot recommendation unit can estimate the user's emotions and prioritize parking lots based on the estimated user emotions. For example, if the user is feeling stressed, the parking lot recommendation unit can prioritize vacant parking lots. Furthermore, if the user is relaxed, the parking lot recommendation unit can prioritize parking lots with low fees. Furthermore, if the user is in a hurry, the parking lot recommendation unit can prioritize the nearest parking lot. This allows for more appropriate parking lots to be recommended by prioritizing parking lots based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the parking lot recommendation unit may be performed using AI, or may be performed without AI. For example, the parking lot recommendation unit can input the user's emotion data into AI and have the AI ​​determine the priority of parking lots.

[0125] When recommending a parking lot, the parking lot recommendation unit can suggest the optimal parking lot by taking into consideration the user's vehicle type information. For example, the parking lot recommendation unit can suggest the optimal parking lot by taking into consideration the size of the parking space based on the user's vehicle type information. The parking lot recommendation unit can also suggest the optimal parking lot by taking into consideration the parking lot's height restrictions based on the user's vehicle type information. Furthermore, the parking lot recommendation unit can also suggest parking lots with charging facilities for electric vehicles based on the user's vehicle type information. This makes it possible to provide a more appropriate parking lot by taking into consideration the user's vehicle type information. Some or all of the above-mentioned processing in the parking lot recommendation unit may be performed using AI, or may be performed without using AI. For example, the parking lot recommendation unit can input the user's vehicle type information into AI and have the AI ​​suggest the optimal parking lot.

[0126] When recommending a parking lot, the parking lot recommendation unit can customize the recommendation content by reflecting the user's past parking history. For example, the parking lot recommendation unit may prioritize and recommend parking lots that the user has used in the past. The parking lot recommendation unit can also recommend parking lots that suit the user's preferences based on the user's past parking history. Furthermore, the parking lot recommendation unit can analyze the user's past parking history and recommend the most efficient parking lot. This makes it possible to recommend parking lots that better suit the user's preferences by reflecting the user's past parking history. Some or all of the above-mentioned processing in the parking lot recommendation unit may be performed using AI, or may be performed without using AI. For example, the parking lot recommendation unit can input the user's past parking history data into AI and have the AI ​​customize the recommendation content.

[0127] When recommending a parking lot, the parking lot recommendation unit can suggest the optimal parking lot according to the user's travel purpose. For example, if the user is traveling for leisure, the parking lot recommendation unit can suggest a parking lot close to a tourist spot. Furthermore, if the user is traveling for business, the parking lot recommendation unit can also suggest a parking lot close to a meeting location. Furthermore, if the user is traveling with family, the parking lot recommendation unit can also suggest a parking lot that is easy for the whole family to use. This allows the user to select a more appropriate parking lot by providing the optimal parking lot according to the user's travel purpose. Some or all of the above-mentioned processing in the parking lot recommendation unit may be performed using AI, or may be performed without using AI. For example, the parking lot recommendation unit can input the user's travel purpose data into AI and have the AI ​​suggest the optimal parking lot.

[0128] The reward granting unit can estimate the user's emotions and adjust the reward content based on the estimated user emotions. For example, if the user is feeling stressed, the reward granting unit can provide a reward for relaxation. Furthermore, if the user is excited, the reward granting unit can also provide a reward for active activities. Furthermore, if the user is tired, the reward granting unit can also provide a discount reward at a rest point. By adjusting the reward content based on the user's emotions, more personalized rewards can be provided. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reward granting unit can be performed using AI, or without AI. For example, the reward granting unit can input the user's emotion data into AI and have the AI ​​adjust the reward content.

[0129] When granting a reward, the reward granting unit can customize the content of the reward by referring to the user's past usage history. For example, the reward granting unit can prioritize and provide rewards that the user has given high ratings to in the past. The reward granting unit can also provide rewards that match the user's preferences based on rewards that the user has used in the past. Furthermore, the reward granting unit can analyze the user's past usage history and provide the reward that the user will enjoy most. In this way, by referring to the user's past usage history, rewards that better match the user's preferences can be provided. Some or all of the above-described processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input the user's past usage history data into AI and have the AI ​​customize the content of the reward.

[0130] When granting a reward, the reward granting unit can provide the optimal reward by taking into account the user's current location information. For example, the reward granting unit can provide a reward that can be used at a store close to the user's current location. The reward granting unit can also provide a reward that can be used efficiently by taking into account the travel time from the user's current location. Furthermore, the reward granting unit can also provide the optimal reward by taking into account the means of transportation from the user's current location. In this way, by taking into account the user's current location information, more appropriate rewards can be provided. Some or all of the above-described processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input the user's current location data into AI and cause the AI ​​to provide the optimal reward.

[0131] The reward granting unit can improve the content of the reward by reflecting user feedback when granting the reward. For example, the reward granting unit can prioritize and provide rewards that the user has previously rated highly. The reward granting unit can also exclude and provide rewards that the user has previously rated poorly. Furthermore, the reward granting unit can analyze user feedback and provide the reward that is most appreciated. In this way, by reflecting user feedback, rewards that are more appreciated by the user can be provided. Some or all of the above-mentioned processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input user feedback data into AI and have the AI ​​improve the content of the reward.

[0132] The reward granting unit can estimate the user's emotions and determine the priority of rewards based on the estimated user emotions. For example, if the user is feeling stressed, the reward granting unit can prioritize providing a reward that helps the user relax. Furthermore, if the user is excited, the reward granting unit can prioritize providing a reward for an active activity. Furthermore, if the user is tired, the reward granting unit can prioritize providing a discount reward at a rest point. This allows more appropriate rewards to be provided by determining the priority of rewards based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reward granting unit can be performed using AI, or without AI. For example, the reward granting unit can input the user's emotion data into AI and have the AI ​​determine the priority of rewards.

[0133] The reward granting unit can analyze the user's social media activity and provide a related reward when granting a reward. For example, the reward granting unit can provide a reward related to a location where the user checked in on social media. The reward granting unit can also analyze the content of the user's social media posts and provide rewards that the user may be interested in. Furthermore, the reward granting unit can provide related rewards by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant rewards can be provided. Some or all of the above-described processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input the user's social media data into AI and have the AI ​​provide the related reward.

[0134] When granting a reward, the reward granting unit can customize the reward content by reflecting the user's past feedback. For example, the reward granting unit can prioritize and provide rewards that the user has previously rated highly. The reward granting unit can also exclude and provide rewards that the user has previously rated poorly. Furthermore, the reward granting unit can analyze the user's past feedback and provide the reward that the user will enjoy most. In this way, by reflecting the user's past feedback, rewards that better suit the user's preferences can be provided. Some or all of the above-described processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input the user's past feedback data into AI and have the AI ​​customize the reward content.

[0135] The reward granting unit can provide the optimal reward according to the user's travel purpose when granting the reward. For example, if the user is traveling for leisure, the reward granting unit can provide a reward that can be used at tourist spots. Furthermore, if the user is traveling for business, the reward granting unit can also provide a reward that can be used at conference locations. Furthermore, if the user is traveling as a family, the reward granting unit can also provide a reward that can be used by the entire family. This allows for more appropriate rewards to be provided by providing the optimal reward according to the user's travel purpose. Some or all of the above-described processing in the reward granting unit may be performed using AI, or may be performed without using AI. For example, the reward granting unit can input the user's travel purpose data into AI and have the AI ​​provide the optimal reward.

[0136] The analysis unit can estimate the user's emotions and adjust the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide analysis results that prioritize relaxing tourist spots. Furthermore, if the user is excited, the analysis unit can provide analysis results that include active activities. Furthermore, if the user is tired, the analysis unit can provide analysis results that include many resting points. By adjusting the analysis results based on the user's emotions, more personalized analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into AI and have the AI ​​adjust the analysis results.

[0137] During the analysis, the analysis unit can perform a detailed analysis of the user's past SNS posts to identify the user's preferences. For example, the analysis unit can analyze the user's past SNS posts to identify tourist spots that the user is likely to be interested in. The analysis unit can also identify tourist spots that match the user's preferences based on the user's SNS check-in history. Furthermore, the analysis unit can also identify tourist spots that the user is likely to be interested in by referring to the activities of the user's friends on the SNS. This allows for a detailed analysis of the user's past SNS posts to provide more accurate analysis results. Some or all of the above-described processing by the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's past SNS post data into AI and have the AI ​​identify the user's preferences.

[0138] During analysis, the analysis unit can provide optimal analysis results by taking into account the user's current location information. For example, the analysis unit can provide analysis results by prioritizing tourist spots close to the user's current location. The analysis unit can also provide efficient tourist spots as analysis results by taking into account travel time from the user's current location. Furthermore, the analysis unit can provide optimal tourist spots as analysis results by taking into account the means of transportation from the user's current location. In this way, by taking into account the user's current location information, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's current location data into AI and have the AI ​​provide optimal analysis results.

[0139] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past operation history. For example, the analysis unit can provide tourist spots that match the user's preferences as analysis results based on the user's past operation history. The analysis unit can also analyze the user's past operation history and provide the most efficient tourist spots as analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past operation history. In this way, by referring to the user's past operation history, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's past operation history data into AI and have the AI ​​improve the accuracy of the analysis.

[0140] The analysis unit can estimate the user's emotions and prioritize analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize relaxing tourist spots in the analysis results. Furthermore, if the user is excited, the analysis unit can prioritize analysis results that include active activities. Furthermore, if the user is tired, the analysis unit can prioritize analysis results that include many resting points. By prioritizing analysis results based on the user's emotions, more important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into AI and have the AI ​​determine the priority of the analysis results.

[0141] During the analysis, the analysis unit can analyze the user's social media activity and provide relevant analysis results. For example, the analysis unit can provide analysis results regarding places where the user has checked in on social media. The analysis unit can also analyze the content of the user's social media posts and provide analysis results regarding related tourist spots and stores. Furthermore, the analysis unit can provide analysis results regarding related places and events by referring to the activities of the user's friends on social media. This makes it possible to provide more relevant analysis results by analyzing the user's social media activity. Some or all of the above-described processing by the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's social media data into AI and have the AI ​​provide relevant analysis results.

[0142] During analysis, the analysis unit can customize the analysis content by reflecting the user's past feedback. For example, the analysis unit can provide analysis results by prioritizing tourist spots that the user has previously rated highly. The analysis unit can also provide analysis results by excluding tourist spots that the user has previously rated poorly. Furthermore, the analysis unit can analyze the user's past feedback and provide the most popular tourist spots as analysis results. In this way, by reflecting the user's past feedback, it is possible to provide analysis results that are more suited to the user's preferences. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's past feedback data into AI and have the AI ​​customize the analysis content.

[0143] During analysis, the analysis unit can provide optimal analysis results according to the user's travel purpose. For example, if the user is traveling for leisure, the analysis unit can provide relaxing tourist spots as the analysis result. Furthermore, if the user is traveling for business, the analysis unit can also provide efficient tourist spots as the analysis result. Furthermore, if the user is traveling with family, the analysis unit can also provide tourist spots that the whole family can enjoy as the analysis result. This allows for more appropriate information to be provided by providing optimal analysis results according to the user's travel purpose. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's travel purpose data into AI and have the AI ​​provide optimal analysis results.

[0144] The update unit can estimate the user's emotions and adjust the update content based on the estimated user emotions. For example, if the user is feeling stressed, the update unit can prioritize updating information that helps the user relax. Furthermore, if the user is excited, the update unit can prioritize updating information about active activities. Furthermore, if the user is tired, the update unit can prioritize updating information about rest points. By adjusting the update content based on the user's emotions, more personalized information can be provided. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit can be performed using AI, or without AI. For example, the update unit can input the user's emotion data into AI and have the AI ​​adjust the update content.

[0145] The update unit can adjust the update frequency by referring to the user's past operation history when updating. For example, the update unit prioritizes updating information that the user uses frequently. The update unit can also suggest an optimal update frequency based on the user's past operation history. Furthermore, the update unit can analyze the user's past operation history and prioritize updating necessary information. In this way, by referring to the user's past operation history, a more appropriate update frequency can be provided. Some or all of the above-mentioned processing in the update unit may be performed using AI, or may be performed without using AI. For example, the update unit can input the user's past operation history data into AI and have the AI ​​adjust the update frequency.

[0146] The update unit can provide optimal update content by taking into account the user's current location information when updating. For example, the update unit prioritizes updating information related to the user's current location. The update unit can also update efficient information by taking into account travel time from the user's current location. Furthermore, the update unit can also update optimal information by taking into account the means of transportation from the user's current location. This makes it possible to provide more appropriate information by taking into account the user's current location information. Some or all of the above-described processing in the update unit may be performed using AI, or may be performed without using AI. For example, the update unit can input the user's current location data into AI and have the AI ​​provide optimal update content.

[0147] The update unit can estimate the user's emotions and determine the update priority based on the estimated user's emotions. For example, if the user is feeling stressed, the update unit can prioritize updating information that helps the user relax. Furthermore, if the user is excited, the update unit can prioritize updating information related to active activities. Furthermore, if the user is tired, the update unit can prioritize updating information related to rest points. This allows for determining the update priority based on the user's emotions, thereby providing more important information preferentially. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit can be performed using AI, or without AI. For example, the update unit can input the user's emotion data into AI and have the AI ​​determine the update priority.

[0148] The update unit can analyze the user's social media activity at the time of updating and provide relevant updates. For example, the update unit prioritizes updating information related to places where the user has checked in on social media. The update unit can also analyze the content of the user's social media posts and prioritize updating information that is likely to be of interest to the user. Furthermore, the update unit can also prioritize updating relevant information based on the activities of the user's friends on social media. In this way, more relevant information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the update unit may be performed using AI, or may be performed without using AI. For example, the update unit can input the user's social media data into AI and have the AI ​​provide relevant updates.

[0149] When updating, the update unit can customize the update content by reflecting the user's past feedback. For example, the update unit prioritizes updating information that the user has previously rated highly. The update unit can also update information by excluding information that the user has previously rated poorly. Furthermore, the update unit can analyze the user's past feedback and prioritize updating the information that the user finds most pleasing. In this way, by reflecting the user's past feedback, it is possible to provide information that is more suited to the user's preferences. Some or all of the above-mentioned processing in the update unit may be performed using AI, or may be performed without using AI. For example, the update unit can input the user's past feedback data into AI and have the AI ​​customize the update content.

[0150] The registration unit can estimate the user's emotions and adjust the registration content based on the estimated user emotions. For example, the registration unit can provide a simple registration procedure when the user is stressed. The registration unit can also provide detailed registration options when the user is relaxed. Furthermore, the registration unit can provide a quick registration procedure when the user is in a hurry. This allows for a more personalized registration process by adjusting the registration content based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be 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-described processing in the registration unit can be performed using AI, or without AI. For example, the registration unit can input the user's emotion data into AI and have the AI ​​adjust the registration content.

[0151] The registration unit can improve the accuracy of registration by referring to the user's past operation history during registration. The registration unit can, for example, suggest optimal registration content based on the user's past operation history. The registration unit can also analyze the user's past operation history to improve the accuracy of registration. Furthermore, the registration unit can also simplify the registration procedure by referring to the user's past operation history. In this way, by referring to the user's past operation history, a more accurate registration procedure can be provided. Some or all of the above-mentioned processing in the registration unit may be performed using AI, or may be performed without using AI. For example, the registration unit can input the user's past operation history data into AI and have the AI ​​improve the accuracy of registration.

[0152] The registration unit can provide optimal registration content by taking into account the user's current location information at the time of registration. For example, the registration unit prioritizes registration of information related to the user's current location. The registration unit can also provide efficient registration content by taking into account travel time from the user's current location. Furthermore, the registration unit can also provide optimal registration content by taking into account the means of transportation from the user's current location. In this way, more appropriate registration content can be provided by taking into account the user's current location information. Some or all of the above-described processing in the registration unit may be performed using AI, or may be performed without using AI. For example, the registration unit can input the user's current location data into AI and have the AI ​​provide optimal registration content.

[0153] The registration unit can estimate the user's emotions and determine the registration priority based on the estimated user emotions. For example, if the user is feeling stressed, the registration unit can prioritize providing a simple registration procedure. Furthermore, if the user is relaxed, the registration unit can prioritize providing detailed registration options. Furthermore, if the user is in a hurry, the registration unit can prioritize providing a procedure that allows for quick registration. This allows for prioritizing registration based on the user's emotions, thereby providing more important information preferentially. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit can be performed using AI, or without AI. For example, the registration unit can input the user's emotion data into AI and have the AI ​​determine the registration priority.

[0154] At the time of registration, the registration unit can analyze the user's social media activity and provide relevant registration content. For example, the registration unit can prioritize registering information related to places where the user has checked in on social media. The registration unit can also analyze the content of the user's social media posts and prioritize registering information that is likely to be of interest to the user. Furthermore, the registration unit can also prioritize registering relevant information based on the activities of the user's friends on social media. This makes it possible to provide more relevant information by analyzing the user's social media activity. Some or all of the above-described processing in the registration unit may be performed using AI, or may be performed without using AI. For example, the registration unit can input the user's social media data into AI and have the AI ​​provide the relevant registration content.

[0155] At the time of registration, the registration unit can customize the registered content by reflecting the user's past feedback. For example, the registration unit prioritizes registering information that the user has previously given a high rating. The registration unit can also exclude and register information that the user has previously given a low rating. Furthermore, the registration unit can analyze the user's past feedback and prioritize registering the information that the user finds most pleasing. In this way, by reflecting the user's past feedback, it is possible to provide information that is more suited to the user's preferences. Some or all of the above-described processing in the registration unit may be performed using AI, or may be performed without using AI. For example, the registration unit can input the user's past feedback data into AI and have the AI ​​customize the registered content.

[0156] The additional suggestion unit can estimate the user's emotions and adjust the content of the additional suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the additional suggestion unit can suggest additional tourist spots where the user can relax. Furthermore, if the user is excited, the additional suggestion unit can suggest additional tourist spots that include active activities. Furthermore, if the user is tired, the additional suggestion unit can suggest additional tourist spots that include many resting points. This allows for more personalized suggestions by adjusting the content of the additional suggestions based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the additional suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the additional suggestion unit can input the user's emotion data into the generation AI and have the generation AI adjust the content of the additional suggestions.

[0157] When making an additional suggestion, the additional suggestion unit can analyze the user's past visit history in detail and suggest optimal tourist spots. For example, the additional suggestion unit can suggest related tourist spots based on tourist spots that the user has visited in the past. The additional suggestion unit can also analyze the user's past visit history and suggest tourist spots that the user may be interested in. Furthermore, the additional suggestion unit can suggest the most efficient tourist spots based on the user's past visit history. This allows for more accurate suggestion of tourist spots by analyzing the user's past visit history in detail. Some or all of the above-mentioned processing in the additional suggestion unit may be performed using or without the generation AI. For example, the additional suggestion unit can input the user's past visit history data into the generation AI and have the generation AI suggest optimal tourist spots.

[0158] When making a suggestion, the suggestion unit can suggest optimal tourist spots by taking into account the user's current location information. For example, the suggestion unit can prioritize suggesting tourist spots close to the user's current location. The suggestion unit can also suggest efficient tourist spots by taking into account travel time from the user's current location. Furthermore, the suggestion unit can also suggest optimal tourist spots by taking into account the mode of transportation from the user's current location. This makes it possible to suggest more appropriate tourist spots by taking into account the user's current location information. Some or all of the above-described processing in the suggestion unit can be performed using or without the generation AI. For example, the suggestion unit can input the user's current location data into the generation AI and cause the generation AI to suggest optimal tourist spots.

[0159] When suggesting an additional item, the additional suggestion unit can provide detailed information about tourist spots based on the user's interests. For example, the additional suggestion unit can provide detailed information about tourist spots that the user is interested in. The additional suggestion unit can also provide detailed information about stores that the user frequently visits. Furthermore, the additional suggestion unit can provide detailed information about tourist spots that the user may be interested in based on the user's past search history. This improves the tourist experience by providing detailed information about tourist spots based on the user's interests. Some or all of the above-mentioned processing in the additional suggestion unit may be performed using or without the generation AI. For example, the additional suggestion unit can input the user's interest data into the generation AI and cause the generation AI to provide detailed information about tourist spots.

[0160] The additional suggestion unit can estimate the user's emotions and prioritize the additional suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the additional suggestion unit can prioritize tourist spots where the user can relax. Furthermore, if the user is excited, the additional suggestion unit can prioritize tourist spots that include active activities. Furthermore, if the user is tired, the additional suggestion unit can prioritize tourist spots that include many resting points. By prioritizing the additional suggestions based on the user's emotions, more important information can be provided preferentially. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the additional suggestion unit can be performed using the generation AI, or can be performed without the generation AI. For example, the additional suggestion unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the additional suggestions.

[0161] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related tourist spots. For example, the suggestion unit can suggest related tourist spots based on the locations where the user has checked in on social media. The suggestion unit can also analyze the content of the user's social media posts and suggest tourist spots that the user may be interested in. Furthermore, the suggestion unit can also suggest related tourist spots based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant tourist spots can be suggested. Some or all of the above-described processing in the suggestion unit can be performed using or without the generation AI. For example, the suggestion unit can input the user's social media data into the generation AI and have the generation AI suggest related tourist spots.

[0162] When making an additional suggestion, the additional suggestion unit can customize the suggestion content by reflecting the user's past feedback. For example, the additional suggestion unit prioritizes suggesting tourist spots that the user has previously rated highly. The additional suggestion unit can also exclude tourist spots that the user has previously rated poorly. Furthermore, the additional suggestion unit can analyze the user's past feedback and suggest the tourist spots that the user will enjoy the most. In this way, by reflecting the user's past feedback, tourist spots that better suit the user's preferences can be suggested. Some or all of the above-mentioned processing in the additional suggestion unit may be performed using or without the generation AI. For example, the additional suggestion unit can input the user's past feedback data into the generation AI and have the generation AI customize the suggestion content.

[0163] When making an additional suggestion, the additional suggestion unit can suggest optimal sightseeing spots according to the user's travel purpose. For example, if the user is traveling for leisure, the additional suggestion unit can suggest relaxing tourist spots. Furthermore, if the user is traveling for business, the additional suggestion unit can also suggest efficient tourist spots. Furthermore, if the user is traveling with family, the additional suggestion unit can also suggest tourist spots that the whole family can enjoy. In this way, by suggesting optimal sightseeing spots according to the user's travel purpose, more appropriate sightseeing spots can be provided. Some or all of the above-mentioned processing in the additional suggestion unit may be performed using or without the generation AI. For example, the additional suggestion unit can input the user's travel purpose data into the generation AI and cause the generation AI to suggest optimal sightseeing spots. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned suggestion unit, display unit, parking lot recommendation unit, reward granting unit, analysis unit, update unit, registration unit, and additional suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The display unit is realized by the display 40A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The parking lot recommendation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The reward granting unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12. The update unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The registration unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The addition suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned suggestion unit, display unit, parking lot recommendation unit, reward granting unit, analysis unit, update unit, registration unit, and additional suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The display unit is realized by the display 40A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The parking lot recommendation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The reward granting unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12. The update unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The registration unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The addition suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned suggestion unit, display unit, parking lot recommendation unit, reward granting unit, analysis unit, update unit, registration unit, and additional suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The display unit is realized by the display 40A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The parking lot recommendation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The reward granting unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12. The update unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The registration unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The addition suggestion unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned suggestion unit, display unit, parking lot recommendation unit, reward granting unit, analysis unit, update unit, registration unit, and additional suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The display unit is realized by the display 40A of the robot 414 or the specific processing unit 290 of the data processing device 12. The parking lot recommendation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The reward granting unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12. The update unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The registration unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The addition suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0164] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0165] The suggestion unit can generate sightseeing routes based on the user's preferences and past history, but can also adjust the suggested content by taking into account the user's real-time health data. For example, it can analyze the user's heart rate and stress level obtained from the user's smartwatch and prioritize suggested sightseeing spots where the user can relax. If the user is tired, it can suggest sightseeing routes that include many rest stops. Furthermore, if the user is active, it can suggest sightseeing routes that include active activities. This allows for more personalized suggestions by adjusting the suggested sightseeing routes based on the user's health condition.

[0166] The display unit can display the tourist spots suggested by the suggestion unit on a map by linking with a map app or navigation system via API, and can also adjust the color scheme of the map according to the user's visual preferences. For example, the display unit can adjust the color scheme of the map based on the user's preferred colors. The display unit can also suggest an optimal color scheme based on the user's past selection history. Furthermore, if the user selects a specific color scheme, the display unit can display the map in that color scheme. This improves visibility by adjusting the color scheme of the map according to the user's visual preferences.

[0167] The parking recommendation unit updates parking information around tourist spots in real time and can recommend parking lots that are suitable for the user, but it can also suggest the most suitable parking lot by taking into account the user's vehicle type information. For example, it can suggest the most suitable parking lot by taking into account the size of the parking space based on the user's vehicle type information. It can also suggest the most suitable parking lot by taking into account the height restrictions of the parking lot based on the user's vehicle type information. It can also suggest parking lots with charging facilities for electric vehicles based on the user's vehicle type information. In this way, it is possible to provide a more suitable parking lot by taking into account the user's vehicle type information.

[0168] The reward granting unit can grant rewards such as discounts on parking fees, dining discounts, and discounts on admission fees to sightseeing spots by reading the license plate and performing AI recognition, or by registering the 2D code and number.It can also estimate the user's emotions and adjust the reward content based on the estimated user emotions.For example, if the user is feeling stressed, a reward for relaxation can be provided.Also, if the user is excited, a reward for active activities can be provided.Furthermore, if the user is tired, a discount reward at a rest spot can be provided.In this way, by adjusting the reward content based on the user's emotions, more personalized rewards can be provided.

[0169] The suggestion unit can recommend nearby tourist spots based on parking lots and other waiting times, but can also estimate the user's emotions and suggest ways to spend the waiting time based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can suggest relaxing cafes and parks. Also, if the user is excited, the suggestion unit can suggest tourist spots that include active activities. Furthermore, if the user is tired, the suggestion unit can suggest tourist spots that include many resting points. This allows for more personalized suggestions by suggesting ways to spend the waiting time based on the user's emotions.

[0170] The system is equipped with an analysis unit that can analyze SNS data and provide the user's preferences and past history to the suggestion unit, and can also analyze the user's social media activity to suggest related tourist spots. For example, related tourist spots can be suggested based on the locations where the user has checked in on social media. It can also analyze the content of the user's social media posts to suggest tourist spots that may interest the user. It can also suggest related tourist spots based on the activities of the user's friends on social media. By analyzing the user's social media activity, it is possible to suggest more relevant tourist spots.

[0171] The system is equipped with an update unit, which updates the availability of parking spaces in real time and provides the information to the parking space recommendation unit. It can also estimate the user's emotions and adjust the update content based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize updating information that helps them relax. Also, if the user is excited, it can prioritize updating information related to active activities. Furthermore, if the user is tired, it can prioritize updating information related to rest spots. By adjusting the update content based on the user's emotions, it is possible to provide more personalized information.

[0172] The system is provided with a registration unit, which can register the two-dimensional code and number and provide them to the reward granting unit, and can also improve the accuracy of registration by referring to the user's past operation history. For example, the registration unit can suggest optimal registration content based on the user's past operation history. The registration accuracy can also be improved by analyzing the user's past operation history. Furthermore, the registration procedure can be simplified by referring to the user's past operation history. In this way, by referring to the user's past operation history, a more accurate registration procedure can be provided.

[0173] The system includes an additional suggestion unit, which can provide additional sightseeing spots to the suggestion unit based on waiting time, and can also estimate the user's emotions and adjust the content of the additional suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the system can suggest additional sightseeing spots where the user can relax. Also, if the user is excited, the system can suggest additional sightseeing spots that include active activities. Furthermore, if the user is tired, the system can suggest additional sightseeing spots that include many resting points. This allows for more personalized suggestions by adjusting the content of the additional suggestions based on the user's emotions.

[0174] The suggestion unit can generate a sightseeing route based on the user's preferences and past history, but can also generate an optimal sightseeing route according to the user's travel purpose. For example, if the user is traveling for leisure, it can suggest relaxing tourist spots. If the user is traveling for business, it can also suggest an efficient sightseeing route. Furthermore, if the user is traveling with family, it can also suggest tourist spots that the whole family can enjoy. In this way, by generating a sightseeing route according to the user's travel purpose, it is possible to provide a more appropriate sightseeing route.

[0175] The processing flow of the second embodiment will be briefly explained below.

[0176] Step 1: The suggestion unit uses the generation AI to suggest sightseeing routes based on the user's preferences and past history. Users provide information to the generation AI by circling the places they want to visit on a map or by entering keywords. The suggestion unit also analyzes the user's past visit history and posts on social media to generate the optimal sightseeing route for the user. Step 2: The display unit displays the tourist spots suggested by the suggestion unit on a map through API integration with a map app or navigation system. When the user opens the map app on their smartphone, the tourist spots suggested by the generation AI are displayed as route information. The display unit also works with the navigation system to allow the user to easily check the tourist route and receive navigation. Step 3: The parking recommendation unit updates parking information around tourist spots in real time and recommends the most suitable parking lot to the user. It provides information on parking availability and fees around tourist spots and suggests the most suitable parking lot to the user. The parking recommendation unit also suggests options such as parking + public transportation, taxis, shared cars, rental bicycles, and rental kick scooters as needed. Step 4: The rewards granting unit reads the license plate and performs AI recognition, or registers the 2D code and number, to grant rewards such as discounts on parking fees, meal discounts, and discounts on admission fees to tourist spots. When the user parks their car in the parking lot, the license plate is recognized by AI and the rewards are automatically applied. The rewards granting unit can also smoothly carry out automatic payment of parking fees.

[0177] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0181] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0182] 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.

[0183] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0184] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0185] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0186] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0187] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0188] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0189] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0190] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0191] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0192] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0193] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0194] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0195] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0196] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0197] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0198] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0199] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0200] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0201] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0202] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0203] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0204] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0205] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0206] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0207] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0208] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0209] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0210] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0211] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0212] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0213] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0214] 7, a 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.

[0215] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0216] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0217] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0218] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0219] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0220] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0221] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0222] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0224] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0225] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0226] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0227] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0228] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0229] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0230] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0231] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0232] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0233] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0234] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0235] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0236] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0237] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0238] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0239] 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.

[0240] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0241] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0242] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0243] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0244] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0245] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0246] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0247] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0248] [Explanation of symbols]

[0249] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The system comprises a suggestion unit that suggests tourist routes based on the user's preferences or past history, a display unit that displays the tourist spots suggested by the suggestion unit on a map, a parking lot recommendation unit that recommends parking lot information around the tourist spots, and a reward granting unit that recognizes license plates and grants rewards.

2. The proposal unit Generate sightseeing routes based on user preferences and past history 2. The system of claim 1.

3. The display unit The tourist spots suggested by the suggestion unit are displayed on a map through API linkage with a map app or navigation system.

2. The system of claim 1.

4. The system according to claim 1, characterized in that parking information around tourist spots is updated in real time and parking lots suitable for the user are recommended.

5. The system described in claim 1 provides benefits such as discounts on parking fees, meals, and admission fees to tourist spots by reading the license plate and performing AI recognition, or by registering the two-dimensional code and number.

6. The benefit granting unit Smoothly implement automatic payment for parking fees 2. The system of claim 1.

7. The proposal unit Recommend nearby sightseeing spots based on parking and other waiting times 2. The system of claim 1.

8. An analysis unit is provided, The analysis unit Analyzes SNS data and provides user preferences and past history to the suggestion department 2. The system of claim 1.

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