system

Abstract

The system according to the embodiment aims to automatically create and guide an optimal theme park course for each user. [Solution] A system according to an embodiment includes an input unit, a collection unit, a generation unit, and a guidance unit. The input unit inputs internal information about the user. The collection unit collects information about the day's congestion level, weather, and attraction closure dates based on the internal information input by the input unit. The generation unit generates a course based on the external information collected by the collection unit and the internal information input by the input unit. The guidance unit guides the user along the course generated by the generation unit.

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 techniques have had the problem that it is difficult to automatically create an optimal theme park course for each user.

[0005] The system according to the embodiment aims to automatically create and guide an optimal theme park course for each user. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a collection unit, a generation unit, and a guidance unit. The input unit inputs internal information about the user. The collection unit collects information about the day's congestion level, weather, and attraction closure dates based on the internal information input by the input unit. The generation unit generates a course based on the external information collected by the collection unit and the internal information input by the input unit. The guidance unit guides the user along the course generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically create and guide the optimal theme park course for each user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The theme park course generation system according to an embodiment of the present invention is a system that automatically creates recommended courses tailored to each user when they visit a theme park. This system uses a pre-trained generating AI to guide users to the optimal course based on internal information such as who they are going with, when they will be there and when they will be there, whether they like thrill rides and whether they want to have a proper meal, and external information such as the crowd level, weather and attraction closures on the day. For example, if a user is going with family, they input the number of family members, their ages and preferences, etc. They also input detailed information such as when they will be at the theme park and when they will be there, whether they like thrill rides and whether they want to have a proper meal. This information is input into the generating AI. Next, the generating AI collects external information. For example, it collects information such as the crowd level, weather and attraction closures on the day in real time. This allows the system to create the optimal course based on the latest conditions. The generating AI creates the optimal course based on the internal and external information. For example, if a family is going and they like thrill rides, the system will prioritize guiding them to thrill rides during less crowded times and create a course that also takes meal times into consideration. Furthermore, the system can respond flexibly, for example, by prioritizing indoor attractions in case of bad weather. This tool allows users to easily create the optimal course tailored to their needs, maximizing their enjoyment of their time at the theme park. For example, a family can create a course that focuses on attractions enjoyable for children, while also considering meal and rest times. A user who loves thrill rides can reduce waiting times and enjoy them more efficiently by prioritizing thrill rides during less crowded times. In this way, using generation AI, the system can automatically create the optimal course tailored to the user's preferences and circumstances, improving their theme park experience. As a result, the theme park course generation system can automatically generate and guide users to the optimal course based on both internal and external information.

[0029] The theme park course generation system according to this embodiment comprises an input unit, a collection unit, a generation unit, and a guidance unit. The input unit receives internal information such as who the user is going with, what time they will be there and when, whether they like thrill rides, and whether they want to have a proper meal. For example, if the user is going with family, they will input the number of family members, their ages, preferences, etc. They will also input detailed information such as what time they will be at the theme park, whether they like thrill rides, and whether they want to have a proper meal. This information is input into the generation AI. The collection unit collects external information in real time, such as the crowd level, weather, and information on attraction closures. For example, it collects information in real time about the crowd level, weather, and information on attraction closures. This allows for the creation of an optimal course based on the latest conditions. The generation unit generates an optimal course based on the internal and external information. For example, if the user is going with family and likes thrill rides, the system will prioritize guiding them to thrill rides during less crowded times and create a course that also takes meal times into consideration. Furthermore, it can flexibly respond by prioritizing indoor attractions in case of bad weather. Some or all of the above-described processing in the generation unit is performed using a generation AI. The generation AI generates the optimal course based on internal and external information. For example, if a family enjoys thrill rides, the AI ​​will prioritize guiding them to thrill rides during less crowded times and create a course that also takes meal times into consideration. It can also respond flexibly, for example, by prioritizing indoor attractions in bad weather. The guidance unit presents the generated course to the user. For example, it presents the generated course to the user using a smartphone app or a paper map. This allows the user to easily check the optimal course that suits them and make the most of their time at the theme park. Thus, the theme park course generation system according to this embodiment can automatically generate and guide users to the optimal course based on the user's internal and external information.

[0030] The input unit allows the user to input information about who the user is going with, what time they will be there, whether they like thrill rides, and whether they want to have a proper meal. For example, the input unit inputs information about who the user is going with, such as family, friends, or a significant other. The input unit can also input what time they will be there, such as a stay duration, such as 9:00 AM to 5:00 PM. The input unit can also input whether they like thrill rides, such as whether they like them, dislike them, or neither. The input unit can also input whether they want to have a proper meal, such as whether they will eat at a restaurant or just have a light meal. By inputting detailed internal information about the user, a more accurate course can be generated. Some or all of the above-described processing in the input unit is performed using a generation AI. For example, the input unit inputs information about who the user is going with, what time they will be there, whether they like thrill rides, and whether they want to have a proper meal into the generation AI, and the generation AI generates the optimal course based on this information.

[0031] The collection unit can collect information on the day's congestion level, weather, and attraction closure dates in real time. The collection unit, for example, collects the day's congestion level in real time. For example, the collection unit collects the theme park's congestion status in real time and provides the latest information. The collection unit can also collect weather information in real time. For example, it collects weather information for the day based on weather forecasts and meteorological data. The collection unit can also collect attraction closure dates in real time. For example, it collects attraction closure date information from the theme park's official website or app. By collecting external information in real time, an optimal course based on the latest conditions can be generated. Some or all of the above-mentioned processing in the collection unit is performed using a generation AI. For example, the collection unit inputs the day's congestion level, weather, and attraction closure date information into the generation AI, which then generates an optimal course based on this information.

[0032] The generation unit can generate a course based on internal information and external information. The generation unit generates an optimal course based on internal information and external information, for example. For example, if a family likes thrill rides, a course can be created that prioritizes thrill rides during less crowded times and takes meal times into consideration. Furthermore, in bad weather, a flexible response can be made, such as prioritizing indoor attractions. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI generates an optimal course based on internal information and external information. For example, if a family likes thrill rides, a course can be created that prioritizes thrill rides during less crowded times and takes meal times into consideration. Furthermore, in bad weather, a flexible response can be made, such as prioritizing indoor attractions. In this way, by generating an optimal course based on internal information and external information, a course that suits the user can be provided.

[0033] The guidance unit can present the generated course to the user. The guidance unit, for example, presents the generated course to the user. For example, the generated course is presented to the user using a smartphone app or a paper map. This allows the user to easily check the optimal course that suits them and enjoy their time at the theme park to the fullest. Some or all of the above-mentioned processing in the guidance unit is performed using a generation AI. For example, the guidance unit inputs the generated course into the generation AI, which then generates the optimal course based on this information.

[0034] The generation unit can generate a route that takes families who like thrill rides on thrill rides during less crowded hours. For example, if a family likes thrill rides, the generation unit generates a route that prioritizes thrill rides on thrill rides during less crowded hours. For example, if a family likes thrill rides, the generation unit creates a route that prioritizes thrill rides on thrill rides during less crowded hours and takes meal times into consideration. This reduces waiting time and allows families who like thrill rides to enjoy themselves more efficiently by prioritizing thrill rides on thrill rides during less crowded hours. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, if a family likes thrill rides, the generation unit inputs a route that prioritizes thrill rides on thrill rides on thrill rides into the generation AI, and the generation AI generates the optimal route based on this information.

[0035] The generation unit can generate a route that guides users to indoor attractions when the weather is bad. For example, the generation unit generates a route that prioritizes indoor attractions when the weather is bad. For example, the generation unit can respond flexibly to bad weather, such as by prioritizing indoor attractions when the weather is bad. This allows users to enjoy themselves regardless of the weather by prioritizing indoor attractions when the weather is bad. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs a route that prioritizes indoor attractions when the weather is bad into the generation AI, and the generation AI generates an optimal route based on this information.

[0036] The input unit can analyze the user's past theme park visit history and select the optimal input method. For example, the input unit automatically suggests a favorite attraction based on the user's past history of attractions visited. For example, the input unit automatically suggests a favorite attraction based on the user's past history of attractions visited. The input unit can also suggest a favorite restaurant based on the user's past history of eating places. For example, the input unit can suggest a favorite restaurant based on the user's past history of eating places. The input unit can also suggest an optimal visit time based on the user's past history of time zones of visit. For example, the input unit can suggest an optimal visit time based on the user's past history of time zones of visit. In this way, a more appropriate input method can be selected by analyzing the user's past theme park visit history. Some or all of the above-described processing in the input unit is performed using a generation AI. For example, the input unit inputs the user's past theme park visit history into the generation AI, and the generation AI generates an optimal course based on this information.

[0037] The input unit can adjust the input content based on the user's current health condition and fatigue level when inputting the information. For example, if the user is tired, the input unit sets a longer rest period. For example, if the user is tired, the input unit sets a longer rest period. Furthermore, if the user is in a healthy state, the input unit can set a larger number of active attractions. For example, if the user is in a healthy state, the input unit sets a larger number of active attractions. Furthermore, if the user is in poor health, the input unit can suggest a reasonable schedule. For example, if the user is in poor health, the input unit suggests a reasonable schedule. In this way, by adjusting the input content in consideration of the user's health condition and fatigue level, a more appropriate course can be generated. Some or all of the above-mentioned processing in the input unit is performed using a generation AI. For example, the input unit inputs the user's current health condition and fatigue level into the generation AI, and the generation AI generates an optimal course based on this information.

[0038] The input unit can prioritize inputting highly relevant information based on the user's geographical location information during input. For example, if the user is in a specific area of ​​a theme park, the input unit prioritizes inputting attraction information related to that area. For example, if the user is in a specific area of ​​a theme park, the input unit prioritizes inputting attraction information related to that area. Furthermore, if the user is near the entrance of a theme park, the input unit can prioritize inputting information about an attraction that the user should visit first. For example, if the user is near the entrance of a theme park, the input unit prioritizes inputting information about an attraction that the user should visit first. Furthermore, if the user is staying in a specific area for a long time, the input unit can prioritize inputting detailed information about that area. For example, if the user is staying in a specific area for a long time, the input unit prioritizes inputting detailed information about that area. This prioritizes inputting highly relevant information taking the user's geographical location information into consideration, thereby enabling the generation of a more appropriate course. Some or all of the above-described processing in the input unit is performed using a generation AI. For example, the input unit inputs the user's geographical location information to the generation AI, which then generates an optimal course based on this information.

[0039] The input unit can analyze the user's social media activity during input and automatically input relevant information. For example, the input unit can automatically input the user's preferred attractions based on the attraction information the user has shared on social media. The input unit can also automatically input places the user wants to visit based on the places the user has checked in to on social media. The input unit can also automatically input the latest attraction information based on the theme park account information the user follows on social media. In this way, relevant information can be automatically input by analyzing the user's social media activity. Some or all of the above processing in the input unit is performed using a generation AI. For example, the input unit inputs the user's social media activity into the generation AI, and the generation AI generates the optimal course based on this information.

[0040] The data collection unit can improve the accuracy of data collection based on past congestion data and weather data. For example, the data collection unit can improve the accuracy of data collection by predicting congestion for specific time periods based on past congestion data. The data collection unit can also improve the accuracy of data collection by predicting fluctuations in congestion due to weather based on past weather data. The data collection unit can also improve the accuracy of data collection by predicting the possibility of attraction closures based on past attraction closure data. In this way, the accuracy of data collection can be improved by referring to past data. Some or all of the above processing in the data collection unit is performed using a generation AI. For example, the data collection unit inputs past congestion data and weather data into the generation AI, and the generation AI generates the optimal course based on this information.

[0041] The data collection unit can collect information on specific areas of a theme park during the collection process. For example, the data collection unit can focus on collecting congestion information for areas the user plans to visit. The data collection unit can also focus on collecting area information for attractions the user is interested in. The data collection unit can also focus on collecting restaurant information for areas where the user plans to eat. By focusing on collecting information for specific areas, a more accurate course can be generated. Some or all of the above processing in the data collection unit is performed using a generation AI. For example, the data collection unit inputs information on specific areas of the theme park into the generation AI, which then generates the optimal course based on this information.

[0042] When collecting data, the collection unit can collect information based on the geographical distribution of theme parks. For example, the collection unit collects congestion information for each area of ​​a theme park. For example, the collection unit collects congestion information for each area of ​​a theme park. The collection unit can also collect weather information for each area of ​​a theme park. For example, the collection unit collects weather information for each area of ​​a theme park. The collection unit can also collect attraction closure information for each area of ​​a theme park. For example, the collection unit collects attraction closure information for each area of ​​a theme park. This allows for the collection of information taking into account the geographical distribution of theme parks, making it possible to generate a more appropriate course. Some or all of the above-described processing in the collection unit is performed using a generation AI. For example, the collection unit inputs the geographical distribution of theme parks into the generation AI, which then generates an optimal course based on this information.

[0043] The collection unit can improve the accuracy of collection based on news and event information during collection. For example, the collection unit collects information on events held within a theme park and reflects it in congestion predictions. For example, the collection unit collects information on events held within a theme park and reflects it in congestion predictions. The collection unit can also collect traffic information around the theme park and reflect it in travel time predictions. For example, the collection unit collects traffic information around the theme park and reflects it in travel time predictions. The collection unit can also collect information on new attractions within the theme park and reflect it in course creation. For example, the collection unit collects information on new attractions within the theme park and reflects it in course creation. This allows the accuracy of collection to be improved by referring to related news and event information. Some or all of the above-mentioned processing in the collection unit is performed using a generation AI. For example, the collection unit inputs news and event information into the generation AI, which generates an optimal course based on this information.

[0044] The generation unit can optimize the generation algorithm based on past course generation data during generation. For example, the generation unit proposes an optimal course based on past course generation data. For example, the generation unit proposes an optimal course based on past course generation data. The generation unit can also extract patterns for avoiding congestion from past course generation data and optimize the generation algorithm. For example, the generation unit extracts patterns for avoiding congestion from past course generation data and optimizes the generation algorithm. The generation unit can also analyze past course generation data and generate a course tailored to the user's preferences. For example, the generation unit analyzes past course generation data and generates a course tailored to the user's preferences. By referencing past data, the generation algorithm can be optimized and a more accurate course can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs past course generation data into the generation AI, and the generation AI generates an optimal course based on this information.

[0045] The generation unit can customize the course based on the user's current physical condition and mood when generating the course. For example, if the user is tired, the generation unit generates a course with more rest times set. For example, if the user is tired, the generation unit generates a course with more rest times set. Furthermore, if the user is in good health, the generation unit can generate a course with more active attractions set. For example, if the user is in good health, the generation unit generates a course with more active attractions set. Furthermore, if the user is in poor health, the generation unit can generate a course that suggests a reasonable schedule. For example, if the user is in poor health, the generation unit generates a course that suggests a reasonable schedule. In this way, by customizing the course taking the user's physical condition and mood into consideration, a more appropriate course can be provided. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's current physical condition and mood into the generation AI, and the generation AI generates an optimal course based on this information.

[0046] The generation unit can generate a course based on the geographical distribution of theme parks during generation. The generation unit generates an optimal course based on, for example, congestion information for each area of ​​the theme park. For example, the optimal course is generated based on congestion information for each area of ​​the theme park. The generation unit can also generate an optimal course based on weather information for each area of ​​the theme park. For example, the optimal course is generated based on weather information for each area of ​​the theme park. The generation unit can also generate an optimal course based on attraction closure information for each area of ​​the theme park. For example, the optimal course is generated based on attraction closure information for each area of ​​the theme park. In this way, by generating a course taking into account the geographical distribution of theme parks, a more efficient course can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the geographical distribution of theme parks into the generation AI, and the generation AI generates an optimal course based on this information.

[0047] The generation unit can improve the accuracy of the course based on reviews and ratings of attractions during generation. For example, the generation unit generates a course that includes attractions that match the user's preferences based on attraction reviews. For example, a course that includes attractions that match the user's preferences is generated based on attraction reviews. The generation unit can also generate a course that prioritizes popular attractions based on attraction ratings. For example, a course that prioritizes popular attractions is generated based on attraction ratings. The generation unit can also analyze attraction reviews and ratings to generate a course that increases user satisfaction. For example, the generation unit analyzes attraction reviews and ratings to generate a course that increases user satisfaction. In this way, the accuracy of the course can be improved by referring to attraction reviews and ratings. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs attraction reviews and ratings into the generation AI, and the generation AI generates an optimal course based on this information.

[0048] The guidance system can select the most appropriate guidance method based on the user's past theme park visit history. For example, the guidance system can prioritize guiding users to their favorite attractions based on their past visit history. The guidance system can also recommend restaurants based on the user's past dining history. Furthermore, the guidance system can recommend the optimal time to visit based on the user's past visit history. This allows the system to select a more appropriate guidance method by referring to the user's past visit history. Some or all of the above processing in the guidance system is performed using a generation AI. For example, the guidance system inputs the user's past theme park visit history into the generation AI, which then generates the optimal course based on this information.

[0049] The guidance unit can update the guidance content in real time based on the user's current location information during guidance. For example, the guidance unit can update the user's current location in real time while the user is moving and guide them along the optimal route. For example, the guidance unit can update the user's current location in real time as the user approaches their destination and guide them along the next attraction. For example, the guidance unit can update the user's current location in real time as the user approaches their destination and guide them along the next attraction. Furthermore, if the user gets lost, the guidance unit can update their current location in real time and guide them again. For example, if the user gets lost, the guidance unit can update their current location in real time and guide them again. This allows for more appropriate guidance by updating the guidance content in real time while considering the user's current location information. Some or all of the above processing in the guidance unit is performed using a generation AI. For example, the guidance unit inputs the user's current location information into the generation AI, and the generation AI generates the optimal course based on this information.

[0050] When providing guidance, the guidance unit can adjust the guidance content based on the geographical distribution of the theme park. For example, the guidance unit guides the user to the optimal route based on congestion information for each area of ​​the theme park. For example, the guidance unit guides the user to the optimal route based on congestion information for each area of ​​the theme park. The guidance unit can also guide the user to the optimal route based on weather information for each area of ​​the theme park. For example, the guidance unit guides the user to the optimal route based on weather information for each area of ​​the theme park. The guidance unit can also guide the user to the optimal route based on attraction closure information for each area of ​​the theme park. For example, the guidance unit guides the user to the optimal route based on attraction closure information for each area of ​​the theme park. This allows the guidance content to be adjusted taking into account the geographical distribution of the theme park, thereby providing more efficient guidance. Some or all of the above-described processing in the guidance unit is performed using a generation AI. For example, the guidance unit inputs the geographical distribution of the theme parks into the generation AI, which then generates the optimal course based on this information.

[0051] The guidance unit can analyze the user's social media activity when providing guidance and reflect the information in the guidance. For example, the guidance unit guides the user to favorite attractions based on attraction information shared by the user on social media. For example, the guidance unit guides the user to favorite attractions based on attraction information shared by the user on social media. The guidance unit can also guide the user to places the user wants to visit based on places the user has checked in to on social media. For example, the guidance unit guides the user to places the user wants to visit based on places the user has checked in to on social media. The guidance unit can also guide the user to the latest attraction information based on account information of theme parks the user follows on social media. For example, the guidance unit guides the user to the latest attraction information based on account information of theme parks the user follows on social media. In this way, by analyzing the user's social media activity, related information can be reflected in the guidance. Some or all of the above-described processing in the guidance unit is performed using a generation AI. For example, the guidance unit inputs the user's social media activity into the generation AI, which then generates an optimal course based on this information.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The theme park course generation system can further include a history analysis unit that analyzes the user's past theme park visit history. The history analysis unit, for example, automatically suggests a user's favorite attraction based on the user's history of past visits to attractions. It can also suggest a user's favorite restaurant based on the user's history of past dining locations. It can also suggest the optimal time to visit based on the user's history of past visit times. This allows a more appropriate course to be generated by analyzing the user's past theme park visit history.

[0054] The input section can adjust the input content based on the user's current health status and fatigue level. For example, if the user is tired, more rest times can be set. Conversely, if the user is healthy, more active attractions can be set. Furthermore, if the user is feeling unwell, it is possible to suggest a less strenuous schedule. In this way, by adjusting the input content while considering the user's health status and fatigue level, a more appropriate course can be generated.

[0055] The generation unit can optimize its generation algorithm based on past course generation data during the generation process. For example, it can propose the optimal course based on past course generation data. It can also extract patterns to avoid congestion from past course generation data and optimize the generation algorithm accordingly. Furthermore, it can analyze past course generation data and generate courses tailored to user preferences. By referencing past data, the generation algorithm can be optimized, providing more accurate courses.

[0056] The generation unit can customize the course based on the user's current physical condition and mood during the generation process. For example, if the user is tired, it can generate a course with more rest time. If the user is healthy, it can generate a course with more active attractions. Furthermore, if the user is feeling unwell, it can generate a course that suggests a manageable schedule. In this way, by customizing the course to take the user's physical condition and mood into consideration, it is possible to provide a more appropriate course.

[0057] When providing guidance, the guidance unit can select the optimal guidance method based on the user's past theme park visit history. For example, the guidance unit can prioritize favorite attractions based on the user's history of attractions visited in the past. The guidance unit can also guide the user to favorite restaurants based on the user's history of dining locations used in the past. Furthermore, the guidance unit can also guide the user to the optimal time to visit based on the user's history of time periods when the user visited in the past. This allows the guidance unit to select a more appropriate guidance method by referring to the user's past visit history.

[0058] The collection unit can improve the accuracy of collection based on news and event information when collecting data. For example, information on events held in a theme park can be collected and reflected in congestion predictions. Traffic information around the theme park can also be collected and reflected in travel time predictions. Furthermore, information on new attractions in the theme park can be collected and reflected in course creation. This allows the accuracy of collection to be improved by referring to related news and event information.

[0059] The guidance unit can update the guidance content in real time based on the user's current location information when providing guidance. For example, the current location can be updated in real time while the user is moving, and the optimal route can be provided. In addition, as the user approaches the destination, the current location can be updated in real time and guidance can be provided to the next attraction. Furthermore, if the user gets lost, the current location can be updated in real time and guidance can be provided again. In this way, more appropriate guidance can be provided by updating the guidance content in real time taking into account the user's current location information.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The input section is where the user enters internal information such as who they are going with, what time they will be there and when, whether they like thrill rides, and whether they want to eat a proper meal. For example, if the user is going with family, they will enter the number of family members, their ages, and preferences. They will also enter detailed information such as what time they will be at the theme park, whether they like thrill rides, and whether they want to eat a proper meal. Step 2: The collection unit collects external information in real time, such as the day's crowd levels, weather, and attraction closure information. This allows the system to create an optimal course based on the latest conditions. Step 3: The generation unit generates the optimal course based on internal and external information. For example, if a family enjoys thrill rides, the unit will prioritize guiding them to thrill rides during less crowded times and create a course that also takes meal times into consideration. It can also flexibly respond to bad weather by prioritizing indoor attractions. Some or all of the above processing in the generation unit is performed using a generation AI. Step 4: The guidance unit presents the generated course to the user. For example, the generated course is presented to the user using a smartphone app or a paper map. This allows the user to easily check the optimal course that suits them and make the most of their time at the theme park.

[0062] (Example 2) The theme park course generation system according to an embodiment of the present invention is a system that automatically creates recommended courses tailored to each user when they visit a theme park. This system uses a pre-trained generating AI to guide users to the optimal course based on internal information such as who they are going with, when they will be there and when they will be there, whether they like thrill rides and whether they want to have a proper meal, and external information such as the crowd level, weather and attraction closures on the day. For example, if a user is going with family, they input the number of family members, their ages and preferences, etc. They also input detailed information such as when they will be at the theme park and when they will be there, whether they like thrill rides and whether they want to have a proper meal. This information is input into the generating AI. Next, the generating AI collects external information. For example, it collects information such as the crowd level, weather and attraction closures on the day in real time. This allows the system to create the optimal course based on the latest conditions. The generating AI creates the optimal course based on the internal and external information. For example, if a family is going and they like thrill rides, the system will prioritize guiding them to thrill rides during less crowded times and create a course that also takes meal times into consideration. Furthermore, the system can respond flexibly, for example, by prioritizing indoor attractions in case of bad weather. This tool allows users to easily create the optimal course tailored to their needs, maximizing their enjoyment of their time at the theme park. For example, a family can create a course that focuses on attractions enjoyable for children, while also considering meal and rest times. A user who loves thrill rides can reduce waiting times and enjoy them more efficiently by prioritizing thrill rides during less crowded times. In this way, using generation AI, the system can automatically create the optimal course tailored to the user's preferences and circumstances, improving their theme park experience. As a result, the theme park course generation system can automatically generate and guide users to the optimal course based on both internal and external information.

[0063] The theme park course generation system according to this embodiment comprises an input unit, a collection unit, a generation unit, and a guidance unit. The input unit receives internal information such as who the user is going with, what time they will be there and when, whether they like thrill rides, and whether they want to have a proper meal. For example, if the user is going with family, they will input the number of family members, their ages, preferences, etc. They will also input detailed information such as what time they will be at the theme park, whether they like thrill rides, and whether they want to have a proper meal. This information is input into the generation AI. The collection unit collects external information in real time, such as the crowd level, weather, and information on attraction closures. For example, it collects information in real time about the crowd level, weather, and information on attraction closures. This allows for the creation of an optimal course based on the latest conditions. The generation unit generates an optimal course based on the internal and external information. For example, if the user is going with family and likes thrill rides, the system will prioritize guiding them to thrill rides during less crowded times and create a course that also takes meal times into consideration. Furthermore, it can flexibly respond by prioritizing indoor attractions in case of bad weather. Some or all of the above-described processing in the generation unit is performed using a generation AI. The generation AI generates the optimal course based on internal and external information. For example, if a family enjoys thrill rides, the AI ​​will prioritize guiding them to thrill rides during less crowded times and create a course that also takes meal times into consideration. It can also respond flexibly, for example, by prioritizing indoor attractions in bad weather. The guidance unit presents the generated course to the user. For example, it presents the generated course to the user using a smartphone app or a paper map. This allows the user to easily check the optimal course that suits them and make the most of their time at the theme park. Thus, the theme park course generation system according to this embodiment can automatically generate and guide users to the optimal course based on the user's internal and external information.

[0064] The input unit allows the user to input information about who the user is going with, what time they will be there, whether they like thrill rides, and whether they want to have a proper meal. For example, the input unit inputs information about who the user is going with, such as family, friends, or a significant other. The input unit can also input what time they will be there, such as a stay duration, such as 9:00 AM to 5:00 PM. The input unit can also input whether they like thrill rides, such as whether they like them, dislike them, or neither. The input unit can also input whether they want to have a proper meal, such as whether they will eat at a restaurant or just have a light meal. By inputting detailed internal information about the user, a more accurate course can be generated. Some or all of the above-described processing in the input unit is performed using a generation AI. For example, the input unit inputs information about who the user is going with, what time they will be there, whether they like thrill rides, and whether they want to have a proper meal into the generation AI, and the generation AI generates the optimal course based on this information.

[0065] The collection unit can collect information on the day's congestion level, weather, and attraction closure dates in real time. The collection unit, for example, collects the day's congestion level in real time. For example, the collection unit collects the theme park's congestion status in real time and provides the latest information. The collection unit can also collect weather information in real time. For example, it collects weather information for the day based on weather forecasts and meteorological data. The collection unit can also collect attraction closure dates in real time. For example, it collects attraction closure date information from the theme park's official website or app. By collecting external information in real time, an optimal course based on the latest conditions can be generated. Some or all of the above-mentioned processing in the collection unit is performed using a generation AI. For example, the collection unit inputs the day's congestion level, weather, and attraction closure date information into the generation AI, which then generates an optimal course based on this information.

[0066] The generation unit can generate a course based on internal information and external information. The generation unit generates an optimal course based on internal information and external information, for example. For example, if a family likes thrill rides, a course can be created that prioritizes thrill rides during less crowded times and takes meal times into consideration. Furthermore, in bad weather, a flexible response can be made, such as prioritizing indoor attractions. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI generates an optimal course based on internal information and external information. For example, if a family likes thrill rides, a course can be created that prioritizes thrill rides during less crowded times and takes meal times into consideration. Furthermore, in bad weather, a flexible response can be made, such as prioritizing indoor attractions. In this way, by generating an optimal course based on internal information and external information, a course that suits the user can be provided.

[0067] The guidance unit can present the generated course to the user. The guidance unit, for example, presents the generated course to the user. For example, the generated course is presented to the user using a smartphone app or a paper map. This allows the user to easily check the optimal course that suits them and enjoy their time at the theme park to the fullest. Some or all of the above-mentioned processing in the guidance unit is performed using a generation AI. For example, the guidance unit inputs the generated course into the generation AI, which then generates the optimal course based on this information.

[0068] The generation unit can generate a route that takes families who like thrill rides on thrill rides during less crowded hours. For example, if a family likes thrill rides, the generation unit generates a route that prioritizes thrill rides on thrill rides during less crowded hours. For example, if a family likes thrill rides, the generation unit creates a route that prioritizes thrill rides on thrill rides during less crowded hours and takes meal times into consideration. This reduces waiting time and allows families who like thrill rides to enjoy themselves more efficiently by prioritizing thrill rides on thrill rides during less crowded hours. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, if a family likes thrill rides, the generation unit inputs a route that prioritizes thrill rides on thrill rides on thrill rides into the generation AI, and the generation AI generates the optimal route based on this information.

[0069] The generation unit can generate a route that guides users to indoor attractions when the weather is bad. For example, the generation unit generates a route that prioritizes indoor attractions when the weather is bad. For example, the generation unit can respond flexibly to bad weather, such as by prioritizing indoor attractions when the weather is bad. This allows users to enjoy themselves regardless of the weather by prioritizing indoor attractions when the weather is bad. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs a route that prioritizes indoor attractions when the weather is bad into the generation AI, and the generation AI generates an optimal route based on this information.

[0070] The input unit can estimate the user's emotions and determine the priority of the information to be input based on the estimated emotions. For example, if the user is excited, the input unit will prioritize inputting information about thrill rides. Similarly, if the user is relaxed, the input unit will prioritize inputting information about meals and breaks. Furthermore, if the user is stressed, the input unit will prioritize inputting information to avoid crowds. By determining the priority of the information to be input based on the user's emotions, more appropriate information can be input. Some or all of the above processing in the input unit is performed using a generative AI. For example, the input unit estimates the user's emotions, inputs the priority of the information to be input based on the estimated emotions to the generative AI, and the generative AI generates the optimal course based on this information.

[0071] The input unit can analyze the user's past theme park visit history and select the optimal input method. For example, the input unit automatically suggests a favorite attraction based on the user's past history of attractions visited. For example, the input unit automatically suggests a favorite attraction based on the user's past history of attractions visited. The input unit can also suggest a favorite restaurant based on the user's past history of eating places. For example, the input unit can suggest a favorite restaurant based on the user's past history of eating places. The input unit can also suggest an optimal visit time based on the user's past history of time zones of visit. For example, the input unit can suggest an optimal visit time based on the user's past history of time zones of visit. In this way, a more appropriate input method can be selected by analyzing the user's past theme park visit history. Some or all of the above-described processing in the input unit is performed using a generation AI. For example, the input unit inputs the user's past theme park visit history into the generation AI, and the generation AI generates an optimal course based on this information.

[0072] The input unit can adjust the input content based on the user's current health condition and fatigue level when inputting the information. For example, if the user is tired, the input unit sets a longer rest period. For example, if the user is tired, the input unit sets a longer rest period. Furthermore, if the user is in a healthy state, the input unit can set a larger number of active attractions. For example, if the user is in a healthy state, the input unit sets a larger number of active attractions. Furthermore, if the user is in poor health, the input unit can suggest a reasonable schedule. For example, if the user is in poor health, the input unit suggests a reasonable schedule. In this way, by adjusting the input content in consideration of the user's health condition and fatigue level, a more appropriate course can be generated. Some or all of the above-mentioned processing in the input unit is performed using a generation AI. For example, the input unit inputs the user's current health condition and fatigue level into the generation AI, and the generation AI generates an optimal course based on this information.

[0073] The input unit can estimate the user's emotions and adjust the design of the input interface based on those emotions. For example, if the user is tense, the input unit can provide an interface with calming colors to reduce visual stress. Similarly, if the user is enjoying themselves, the input unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the input unit can provide a simple and highly visible interface to facilitate the input process. This allows for a more comfortable input environment by adjusting the input interface design based on the user's emotions. Some or all of the above processing in the input unit is performed using a generative AI. For example, the input unit estimates the user's emotions, inputs the input interface design based on those emotions into the generative AI, and the generative AI generates the optimal course based on this information.

[0074] The input unit can prioritize inputting highly relevant information based on the user's geographical location information during input. For example, if the user is in a specific area of ​​a theme park, the input unit prioritizes inputting attraction information related to that area. For example, if the user is in a specific area of ​​a theme park, the input unit prioritizes inputting attraction information related to that area. Furthermore, if the user is near the entrance of a theme park, the input unit can prioritize inputting information about an attraction that the user should visit first. For example, if the user is near the entrance of a theme park, the input unit prioritizes inputting information about an attraction that the user should visit first. Furthermore, if the user is staying in a specific area for a long time, the input unit can prioritize inputting detailed information about that area. For example, if the user is staying in a specific area for a long time, the input unit prioritizes inputting detailed information about that area. This prioritizes inputting highly relevant information taking the user's geographical location information into consideration, thereby enabling the generation of a more appropriate course. Some or all of the above-described processing in the input unit is performed using a generation AI. For example, the input unit inputs the user's geographical location information to the generation AI, which then generates an optimal course based on this information.

[0075] The input unit can analyze the user's social media activity during input and automatically input relevant information. For example, the input unit can automatically input the user's preferred attractions based on the attraction information the user has shared on social media. The input unit can also automatically input places the user wants to visit based on the places the user has checked in to on social media. The input unit can also automatically input the latest attraction information based on the theme park account information the user follows on social media. In this way, relevant information can be automatically input by analyzing the user's social media activity. Some or all of the above processing in the input unit is performed using a generation AI. For example, the input unit inputs the user's social media activity into the generation AI, and the generation AI generates the optimal course based on this information.

[0076] The data collection unit can estimate the user's emotions and determine the priority of external information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting information about crowded thrill rides. Similarly, if the user is relaxed, the data collection unit will prioritize collecting information about crowded restaurants. Furthermore, if the user is stressed, the data collection unit will prioritize collecting information to help avoid crowds. This allows for the collection of more appropriate information by prioritizing external information based on the user's emotions. Some or all of the above processing in the data collection unit is performed using a generative AI. For example, the data collection unit estimates the user's emotions, inputs the priority of external information to collect based on the estimated emotions into the generative AI, and the generative AI generates an optimal course based on this information.

[0077] The data collection unit can improve the accuracy of data collection based on past congestion data and weather data. For example, the data collection unit can improve the accuracy of data collection by predicting congestion for specific time periods based on past congestion data. The data collection unit can also improve the accuracy of data collection by predicting fluctuations in congestion due to weather based on past weather data. The data collection unit can also improve the accuracy of data collection by predicting the possibility of attraction closures based on past attraction closure data. In this way, the accuracy of data collection can be improved by referring to past data. Some or all of the above processing in the data collection unit is performed using a generation AI. For example, the data collection unit inputs past congestion data and weather data into the generation AI, and the generation AI generates the optimal course based on this information.

[0078] The data collection unit can collect information on specific areas of a theme park during the collection process. For example, the data collection unit can focus on collecting congestion information for areas the user plans to visit. The data collection unit can also focus on collecting area information for attractions the user is interested in. The data collection unit can also focus on collecting restaurant information for areas where the user plans to eat. By focusing on collecting information for specific areas, a more accurate course can be generated. Some or all of the above processing in the data collection unit is performed using a generation AI. For example, the data collection unit inputs information on specific areas of the theme park into the generation AI, which then generates the optimal course based on this information.

[0079] The data collection unit can estimate the user's emotions and adjust how the collected information is displayed based on the estimated emotions. For example, if the user is nervous, the data collection unit can provide a simple and highly visible display method. If the user is relaxed, the data collection unit can provide a display method that includes detailed information. If the user is in a hurry, the data collection unit can provide a display method that gets straight to the point. In this way, by adjusting how information is displayed based on the user's emotions, more appropriate information can be provided. Some or all of the above processing in the data collection unit is performed using a generative AI. For example, the data collection unit estimates the user's emotions, inputs the method for displaying the collected information based on the estimated emotions into the generative AI, and the generative AI generates the optimal course based on this information.

[0080] When collecting data, the collection unit can collect information based on the geographical distribution of theme parks. For example, the collection unit collects congestion information for each area of ​​a theme park. For example, the collection unit collects congestion information for each area of ​​a theme park. The collection unit can also collect weather information for each area of ​​a theme park. For example, the collection unit collects weather information for each area of ​​a theme park. The collection unit can also collect attraction closure information for each area of ​​a theme park. For example, the collection unit collects attraction closure information for each area of ​​a theme park. This allows for the collection of information taking into account the geographical distribution of theme parks, making it possible to generate a more appropriate course. Some or all of the above-described processing in the collection unit is performed using a generation AI. For example, the collection unit inputs the geographical distribution of theme parks into the generation AI, which then generates an optimal course based on this information.

[0081] The collection unit can improve the accuracy of collection based on news and event information during collection. For example, the collection unit collects information on events held within a theme park and reflects it in congestion predictions. For example, the collection unit collects information on events held within a theme park and reflects it in congestion predictions. The collection unit can also collect traffic information around the theme park and reflect it in travel time predictions. For example, the collection unit collects traffic information around the theme park and reflects it in travel time predictions. The collection unit can also collect information on new attractions within the theme park and reflect it in course creation. For example, the collection unit collects information on new attractions within the theme park and reflects it in course creation. This allows the accuracy of collection to be improved by referring to related news and event information. Some or all of the above-mentioned processing in the collection unit is performed using a generation AI. For example, the collection unit inputs news and event information into the generation AI, which generates an optimal course based on this information.

[0082] The generation unit can estimate the user's emotions and determine the priority of courses to be generated based on the estimated user's emotions. For example, if the user is excited, the generation unit generates a course that prioritizes thrill rides. For example, if the user is excited, the generation unit generates a course that prioritizes thrill rides. Furthermore, if the user is relaxed, the generation unit can generate a course that prioritizes meals and rest. For example, if the user is relaxed, the generation unit generates a course that prioritizes meals and rest. Furthermore, if the user is stressed, the generation unit can generate a course that avoids crowded places. For example, if the user is stressed, the generation unit generates a course that avoids crowded places. In this way, by determining the priority of courses based on the user's emotions, a more appropriate course can be provided. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit estimates the user's emotions and inputs the priority of courses to be generated based on the estimated user's emotions into the generation AI, and the generation AI generates an optimal course based on this information.

[0083] The generation unit can optimize the generation algorithm based on past course generation data during generation. For example, the generation unit proposes an optimal course based on past course generation data. For example, the generation unit proposes an optimal course based on past course generation data. The generation unit can also extract patterns for avoiding congestion from past course generation data and optimize the generation algorithm. For example, the generation unit extracts patterns for avoiding congestion from past course generation data and optimizes the generation algorithm. The generation unit can also analyze past course generation data and generate a course tailored to the user's preferences. For example, the generation unit analyzes past course generation data and generates a course tailored to the user's preferences. By referencing past data, the generation algorithm can be optimized and a more accurate course can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs past course generation data into the generation AI, and the generation AI generates an optimal course based on this information.

[0084] The generation unit can customize the course based on the user's current physical condition and mood when generating the course. For example, if the user is tired, the generation unit generates a course with more rest times set. For example, if the user is tired, the generation unit generates a course with more rest times set. Furthermore, if the user is in good health, the generation unit can generate a course with more active attractions set. For example, if the user is in good health, the generation unit generates a course with more active attractions set. Furthermore, if the user is in poor health, the generation unit can generate a course that suggests a reasonable schedule. For example, if the user is in poor health, the generation unit generates a course that suggests a reasonable schedule. In this way, by customizing the course taking the user's physical condition and mood into consideration, a more appropriate course can be provided. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's current physical condition and mood into the generation AI, and the generation AI generates an optimal course based on this information.

[0085] The generation unit can estimate the user's emotions and adjust the display method of the generated course based on the estimated user's emotions. For example, if the user is nervous, the generation unit provides a simple, highly visible display method. For example, if the user is nervous, the generation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the generation unit can provide a display method including detailed information. For example, if the user is relaxed, the generation unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the generation unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the generation unit provides a display method that focuses on the main points. In this way, by adjusting the display method of the course based on the user's emotions, more appropriate information can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit estimates the user's emotions and inputs a display method of the generated course based on the estimated user's emotions into the generation AI, and the generation AI generates an optimal course based on this information.

[0086] The generation unit can generate a course based on the geographical distribution of theme parks during generation. The generation unit generates an optimal course based on, for example, congestion information for each area of ​​the theme park. For example, the optimal course is generated based on congestion information for each area of ​​the theme park. The generation unit can also generate an optimal course based on weather information for each area of ​​the theme park. For example, the optimal course is generated based on weather information for each area of ​​the theme park. The generation unit can also generate an optimal course based on attraction closure information for each area of ​​the theme park. For example, the optimal course is generated based on attraction closure information for each area of ​​the theme park. In this way, by generating a course taking into account the geographical distribution of theme parks, a more efficient course can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the geographical distribution of theme parks into the generation AI, and the generation AI generates an optimal course based on this information.

[0087] The generation unit can improve the accuracy of the course based on reviews and ratings of attractions during generation. For example, the generation unit generates a course that includes attractions that match the user's preferences based on attraction reviews. For example, a course that includes attractions that match the user's preferences is generated based on attraction reviews. The generation unit can also generate a course that prioritizes popular attractions based on attraction ratings. For example, a course that prioritizes popular attractions is generated based on attraction ratings. The generation unit can also analyze attraction reviews and ratings to generate a course that increases user satisfaction. For example, the generation unit analyzes attraction reviews and ratings to generate a course that increases user satisfaction. In this way, the accuracy of the course can be improved by referring to attraction reviews and ratings. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs attraction reviews and ratings into the generation AI, and the generation AI generates an optimal course based on this information.

[0088] The guidance unit can estimate the user's emotions and adjust the way it delivers guidance based on those emotions. For example, if the user is nervous, the guidance unit will deliver guidance in a calm voice. If the user is relaxed, the guidance unit will deliver guidance in a cheerful voice. If the user is in a hurry, the guidance unit will deliver guidance quickly and concisely. By adjusting the way it delivers guidance based on the user's emotions, it can provide more appropriate guidance. Some or all of the above processing in the guidance unit is performed using a generative AI. For example, the guidance unit estimates the user's emotions, inputs the method of delivering guidance based on those emotions into the generative AI, and the generative AI generates the optimal course based on this information.

[0089] The guidance system can select the most appropriate guidance method based on the user's past theme park visit history. For example, the guidance system can prioritize guiding users to their favorite attractions based on their past visit history. The guidance system can also recommend restaurants based on the user's past dining history. Furthermore, the guidance system can recommend the optimal time to visit based on the user's past visit history. This allows the system to select a more appropriate guidance method by referring to the user's past visit history. Some or all of the above processing in the guidance system is performed using a generation AI. For example, the guidance system inputs the user's past theme park visit history into the generation AI, which then generates the optimal course based on this information.

[0090] The guidance unit can update the guidance content in real time based on the user's current location information during guidance. For example, the guidance unit can update the user's current location in real time while the user is moving and guide them along the optimal route. For example, the guidance unit can update the user's current location in real time as the user approaches their destination and guide them along the next attraction. For example, the guidance unit can update the user's current location in real time as the user approaches their destination and guide them along the next attraction. Furthermore, if the user gets lost, the guidance unit can update their current location in real time and guide them again. For example, if the user gets lost, the guidance unit can update their current location in real time and guide them again. This allows for more appropriate guidance by updating the guidance content in real time while considering the user's current location information. Some or all of the above processing in the guidance unit is performed using a generation AI. For example, the guidance unit inputs the user's current location information into the generation AI, and the generation AI generates the optimal course based on this information.

[0091] The guidance unit can estimate the user's emotions and adjust the order of guidance based on the estimated user's emotions. For example, if the user is excited, the guidance unit can guide the user to thrill rides first. For example, if the user is excited, the guidance unit can guide the user to thrill rides first. Furthermore, if the user is relaxed, the guidance unit can guide the user to meal or rest spots first. For example, if the user is relaxed, the guidance unit can guide the user to meal or rest spots first. Furthermore, if the user is stressed, the guidance unit can guide the user to attractions that avoid crowds first. For example, if the user is stressed, the guidance unit can guide the user to attractions that avoid crowds first. In this way, by adjusting the order of guidance based on the user's emotions, more appropriate guidance can be provided. Some or all of the above-mentioned processing in the guidance unit is performed using a generation AI. For example, the guidance unit can estimate the user's emotions and input the order of guidance to the generation AI based on the estimated user's emotions, and the generation AI generates an optimal course based on this information.

[0092] When providing guidance, the guidance unit can adjust the guidance content based on the geographical distribution of the theme park. For example, the guidance unit guides the user to the optimal route based on congestion information for each area of ​​the theme park. For example, the guidance unit guides the user to the optimal route based on congestion information for each area of ​​the theme park. The guidance unit can also guide the user to the optimal route based on weather information for each area of ​​the theme park. For example, the guidance unit guides the user to the optimal route based on weather information for each area of ​​the theme park. The guidance unit can also guide the user to the optimal route based on attraction closure information for each area of ​​the theme park. For example, the guidance unit guides the user to the optimal route based on attraction closure information for each area of ​​the theme park. This allows the guidance content to be adjusted taking into account the geographical distribution of the theme park, thereby providing more efficient guidance. Some or all of the above-described processing in the guidance unit is performed using a generation AI. For example, the guidance unit inputs the geographical distribution of the theme parks into the generation AI, which then generates the optimal course based on this information.

[0093] The guidance unit can analyze the user's social media activity when providing guidance and reflect the information in the guidance. For example, the guidance unit guides the user to favorite attractions based on attraction information shared by the user on social media. For example, the guidance unit guides the user to favorite attractions based on attraction information shared by the user on social media. The guidance unit can also guide the user to places the user wants to visit based on places the user has checked in to on social media. For example, the guidance unit guides the user to places the user wants to visit based on places the user has checked in to on social media. The guidance unit can also guide the user to the latest attraction information based on account information of theme parks the user follows on social media. For example, the guidance unit guides the user to the latest attraction information based on account information of theme parks the user follows on social media. In this way, by analyzing the user's social media activity, related information can be reflected in the guidance. Some or all of the above-described processing in the guidance unit is performed using a generation AI. For example, the guidance unit inputs the user's social media activity into the generation AI, which then generates an optimal course based on this information. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, collection unit, generation unit, and guidance unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14, through which the user inputs internal information. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects external information in real time. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal course based on the internal information and external information. The guidance unit is realized, for example, by the output device 40 of the smart device 14, and presents the generated course to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, collection unit, generation unit, and guidance unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214, and the user inputs internal information. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects external information in real time. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal course based on the internal information and the external information. The guidance unit is realized, for example, by the speaker 240 of the smart glasses 214, and presents the generated course to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, collection unit, generation unit, and guidance unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314, and the user inputs internal information. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects external information in real time. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal course based on the internal information and the external information. The guidance unit is realized, for example, by the display 343 of the headset-type terminal 314, and presents the generated course to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, collection unit, generation unit, and guidance unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414, and the user inputs internal information. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects external information in real time. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal course based on the internal information and the external information. The guidance unit is realized, for example, by the speaker 240 of the robot 414, and presents the generated course to the user.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The theme park course generation system can further include a history analysis unit that analyzes the user's past theme park visit history. The history analysis unit, for example, automatically suggests a user's favorite attraction based on the user's history of past visits to attractions. It can also suggest a user's favorite restaurant based on the user's history of past dining locations. It can also suggest the optimal time to visit based on the user's history of past visit times. This allows a more appropriate course to be generated by analyzing the user's past theme park visit history.

[0096] The input section can adjust the input content based on the user's current health status and fatigue level. For example, if the user is tired, more rest times can be set. Conversely, if the user is healthy, more active attractions can be set. Furthermore, if the user is feeling unwell, it is possible to suggest a less strenuous schedule. In this way, by adjusting the input content while considering the user's health status and fatigue level, a more appropriate course can be generated.

[0097] The collection unit can estimate the user's emotions and determine the priority of external information to be collected based on the estimated user's emotions. For example, if the user is excited, congestion information for thrill rides can be collected with priority. Also, if the user is relaxed, congestion information for restaurants can be collected with priority. Furthermore, if the user is stressed, it is also possible to collect information to avoid congestion with priority. In this way, by determining the priority of external information based on the user's emotions, more appropriate information can be collected.

[0098] The generation unit can optimize its generation algorithm based on past course generation data during the generation process. For example, it can propose the optimal course based on past course generation data. It can also extract patterns to avoid congestion from past course generation data and optimize the generation algorithm accordingly. Furthermore, it can analyze past course generation data and generate courses tailored to user preferences. By referencing past data, the generation algorithm can be optimized, providing more accurate courses.

[0099] The guidance system can estimate the user's emotions and adjust the way it delivers guidance based on those emotions. For example, if the user is nervous, it can deliver guidance in a calm voice. If the user is relaxed, it can deliver guidance in a cheerful voice. Furthermore, if the user is in a hurry, it can deliver guidance quickly and concisely. In this way, by adjusting the way guidance is delivered based on the user's emotions, more appropriate guidance can be provided.

[0100] The generation unit can customize the course based on the user's current physical condition and mood during the generation process. For example, if the user is tired, it can generate a course with more rest time. If the user is healthy, it can generate a course with more active attractions. Furthermore, if the user is feeling unwell, it can generate a course that suggests a manageable schedule. In this way, by customizing the course to take the user's physical condition and mood into consideration, it is possible to provide a more appropriate course.

[0101] When providing guidance, the guidance unit can select the optimal guidance method based on the user's past theme park visit history. For example, the guidance unit can prioritize favorite attractions based on the user's history of attractions visited in the past. The guidance unit can also guide the user to favorite restaurants based on the user's history of dining locations used in the past. Furthermore, the guidance unit can also guide the user to the optimal time to visit based on the user's history of time periods when the user visited in the past. This allows the guidance unit to select a more appropriate guidance method by referring to the user's past visit history.

[0102] The collection unit can improve the accuracy of collection based on news and event information when collecting data. For example, information on events held in a theme park can be collected and reflected in congestion predictions. Traffic information around the theme park can also be collected and reflected in travel time predictions. Furthermore, information on new attractions in the theme park can be collected and reflected in course creation. This allows the accuracy of collection to be improved by referring to related news and event information.

[0103] The generation unit can estimate the user's emotions and determine the priority of the courses to be generated based on the estimated user's emotions. For example, if the user is excited, a course that prioritizes thrill rides can be generated. If the user is relaxed, a course that prioritizes meals and rest can be generated. Furthermore, if the user is stressed, a course that avoids crowded areas can be generated. In this way, by determining the priority of courses based on the user's emotions, it is possible to provide a more appropriate course.

[0104] The guidance unit can update the guidance content in real time based on the user's current location information when providing guidance. For example, the current location can be updated in real time while the user is moving, and the optimal route can be provided. In addition, as the user approaches the destination, the current location can be updated in real time and guidance can be provided to the next attraction. Furthermore, if the user gets lost, the current location can be updated in real time and guidance can be provided again. In this way, more appropriate guidance can be provided by updating the guidance content in real time taking into account the user's current location information.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The input section is where the user enters internal information such as who they are going with, what time they will be there and when, whether they like thrill rides, and whether they want to eat a proper meal. For example, if the user is going with family, they will enter the number of family members, their ages, and preferences. They will also enter detailed information such as what time they will be at the theme park, whether they like thrill rides, and whether they want to eat a proper meal. Step 2: The collection unit collects external information in real time, such as the day's crowd levels, weather, and attraction closure information. This allows the system to create an optimal course based on the latest conditions. Step 3: The generation unit generates the optimal course based on internal and external information. For example, if a family enjoys thrill rides, the unit will prioritize guiding them to thrill rides during less crowded times and create a course that also takes meal times into consideration. It can also flexibly respond to bad weather by prioritizing indoor attractions. Some or all of the above processing in the generation unit is performed using a generation AI. Step 4: The guidance unit presents the generated course to the user. For example, the generated course is presented to the user using a smartphone app or a paper map. This allows the user to easily check the optimal course that suits them and make the most of their time at the theme park.

[0107] 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.

[0108] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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. an input unit for inputting internal information of a user; a collection unit that collects information on the level of crowding, weather, and attraction closures on the day based on the internal information input by the input unit; a generation unit that generates a course based on the external information collected by the collection unit and the internal information input by the input unit; a guidance unit that guides the user along the course generated by the generation unit; A system characterized by:

2. The input unit The user inputs information such as who they are going with, what time they will be there, whether they like thrill rides, and whether they want to have a proper meal.

2. The system of claim 1.

3. The collecting unit Collect real-time information on the day's crowd levels, weather, and attraction closures 2. The system of claim 1.

4. The generation unit Generate courses based on internal and external information 2. The system of claim 1.

5. The guide unit is Present the generated course to the user 2. The system of claim 1.

6. The generation unit If you have a family that loves thrill rides, generate a route that takes you to thrill rides during less crowded times.

2. The system of claim 1.

7. The generation unit Generate a route that takes you to indoor attractions when the weather is bad 2. The system of claim 1.

8. The input unit Estimate the user's emotions and prioritize the information to be input based on the estimated user emotions.

2. The system of claim 1.

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