System

Abstract

An object of a system according to an embodiment is to provide an efficient and highly satisfactory travel plan to a user who travels for an event.SOLUTION: A system according to an embodiment includes a preference analysis unit, a route optimization unit, a congestion inference unit, and a proposal unit. The preference analysis unit analyzes the preference and personality of the user. The route optimization unit optimizes a route between each visiting spot and the event venue based on the information analyzed by the preference analysis unit. The congestion inference unit infers the degree of congestion at each location based on the route optimized by the route optimization unit. The proposal unit proposes an optimal way of spending time before and after event participation on the basis of the degree of congestion inferred by the congestion inference unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide users traveling to events with efficient and satisfying travel plans.

[0005] The system according to the embodiment aims to provide an efficient and satisfying travel plan to a user traveling to an event. [Means for solving the problem]

[0006] The system according to the embodiment includes a preference analysis unit, a route optimization unit, a congestion inference unit, and a suggestion unit. The preference analysis unit analyzes the user's preferences and personality. The route optimization unit optimizes routes between each visit spot and the event venue based on information analyzed by the preference analysis unit. The congestion inference unit infers the degree of congestion in each location based on the route optimized by the route optimization unit. The suggestion unit suggests optimal ways to spend time before and after attending the event based on the degree of congestion inferred by the congestion inference unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an efficient and satisfying travel plan to a user traveling to an event. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 travel plan proposal system according to an embodiment of the present invention utilizes AI technology to propose optimal travel plans for users traveling to events such as watching sports or attending live concerts. This system analyzes the user's preferences and personality, optimizes routes between each destination and the event venue, and estimates the level of congestion in each location to propose optimal ways to spend time before and after the event. This allows the travel plan proposal system to effectively utilize the time before and after the event, enabling users to enjoy a fulfilling trip.

[0029] The travel plan proposal system according to the embodiment includes a preference analysis unit, a route optimization unit, a congestion inference unit, and a proposal unit. The preference analysis unit analyzes the user's preferences and personality. For example, the preference analysis unit collects and analyzes data related to the user's past travel history, preferences, and personality. The preference analysis unit can also analyze the user's preferences and personality based on information such as the user's past visited places, events attended, and favorite activities. The preference analysis unit can also analyze the user's preferences and personality using a generation AI (e.g., a text generation AI or a multimodal generation AI). The route optimization unit optimizes routes between each visit spot and the event venue based on the information analyzed by the preference analysis unit. For example, the route optimization unit optimizes routes between spots the user wants to visit and the event venue. The route optimization unit can also optimize routes and propose efficient travel routes when the user specifies tourist spots or restaurants they want to visit on the way to the event venue. The route optimization unit can also optimize routes using a generation AI. The congestion inference unit infers the level of congestion in each location based on the route optimized by the route optimization unit. For example, the congestion inference unit predicts the level of congestion at a specific time period or location based on past data or real-time information. The congestion inference unit can also infer the level of congestion using a generation AI. Furthermore, the congestion inference unit can infer the level of congestion so that users can avoid crowds and enjoy a comfortable stay. The suggestion unit suggests optimal ways to spend time before and after participating in an event based on the level of congestion inferred by the congestion inference unit. For example, the suggestion unit suggests optimal ways to spend time before and after participating in an event based on the user's preferences and personality, route optimization, and the inferred results of the level of congestion. The suggestion unit can also suggest optimal ways to spend time using a generation AI. Furthermore, the suggestion unit can suggest cafes or tourist spots where users can relax before an event, or restaurants where users can enjoy dinner after an event. This allows the travel plan suggestion system according to the embodiment to enable users to effectively utilize the time before and after participating in an event and enjoy a fulfilling trip.For example, users can visit tourist spots before watching a sporting event or enjoy delicious local cuisine after attending a live event. This also allows users to avoid crowds and enjoy a more comfortable trip, making for a less stressful experience.

[0030] The preference analysis unit analyzes a user's social media posts and blog articles to extract travel-related emotions and interests, thereby analyzing the user's preferences and personality. For example, the preference analysis unit analyzes a user's social media posts and extracts travel-related keywords and hashtags. For example, it collects information about tourist spots and activities frequently posted by the user and analyzes the user's preferences and personality. The preference analysis unit also analyzes the user's blog articles and extracts travel-related emotional expressions and evaluations. For example, it estimates the user's preferences and personality based on their impressions and evaluations of places they have visited in the past. The preference analysis unit also identifies themes and topics that interest the user from the content of social media and blog posts and suggests travel plans based on them. For example, if the user is interested in natural landscapes or historical buildings, it suggests related spots. In this way, by analyzing a user's social media posts and blog articles, it is possible to extract travel-related emotions and interests and analyze the user's preferences and personality.

[0031] The preference analysis unit analyzes the user's voice memos and conversation history and can infer preferences and personality from travel-related comments. For example, the preference analysis unit analyzes the user's voice memos and extracts travel-related keywords and phrases. For example, it identifies places the user wants to visit and activities they are interested in from notes recorded while traveling. The preference analysis unit also analyzes the user's conversation history and extracts travel-related comments. For example, it infers preferences and personality based on tourist spots and events the user discussed in conversations with friends and family. The preference analysis unit also uses voice recognition technology to convert the user's voice memos and conversation history into text data and analyzes the content. For example, it suggests travel plans based on the emotions and evaluations the user felt during the trip. In this way, by analyzing the user's voice memos and conversation history, it is possible to infer preferences and personality from travel-related comments.

[0032] When analyzing the user's preferences and personality, the preference analysis unit also takes into account the preferences of family and friends, allowing it to propose the optimal plan for a group trip. For example, the preference analysis unit collects the preferences and personalities of the user's family and friends and proposes a travel plan based on the preferences of the entire group. For example, it proposes tourist spots and activities that the whole family can enjoy. In addition, the preference analysis unit considers the preferences and personalities of each member when traveling in a group and proposes a plan that will satisfy everyone. For example, it creates a balanced plan so that members with different interests can enjoy themselves. In addition, the preference analysis unit analyzes the travel history and preferences of the user's family and friends and customizes the travel plan based on the preferences of the entire group. For example, it proposes a plan that the whole group can enjoy based on places visited in the past and events attended. In this way, the optimal plan for a group trip can be proposed by taking into account the preferences of family and friends.

[0033] The preference analysis unit can consider the influence of seasons and weather when analyzing the user's preferences and personality, and suggest the optimal travel time. The preference analysis unit, for example, analyzes the user's preferences and personality, and suggests the optimal travel time by considering the influence of seasons and weather. For example, it suggests a season in which the user can enjoy their favorite activities. The preference analysis unit also analyzes the user's past travel history and identifies the optimal travel time by considering the influence of seasons and weather. For example, it suggests the optimal travel time based on weather data of places the user has visited in the past. The preference analysis unit also customizes the travel plan based on the user's preferences and personality, taking into account the influence of seasons and weather. For example, it suggests tourist spots and activities that match the user's preferred seasons and weather. In this way, it is possible to suggest the optimal travel time by considering the influence of seasons and weather.

[0034] The route optimization unit can propose an optimal route depending on the user's means of transportation. The route optimization unit, for example, builds a system that proposes an optimal route depending on the user's means of transportation. For example, when traveling on foot, it proposes an easy-to-walk route. The route optimization unit also proposes an optimal route that includes information on bicycle-only roads and bicycle parking areas for users traveling by bicycle. For example, it proposes tourist spots that are easily accessible by bicycle. The route optimization unit also proposes a route that takes into account optimal transfer routes and timetables for users using public transportation. For example, it proposes a route that includes information on bus and train transfers. This makes it possible to propose an optimal route depending on the user's means of transportation.

[0035] The route optimization unit can propose a reasonable travel plan by taking into account the user's physical strength and health condition. The route optimization unit, for example, builds a system that proposes a reasonable travel plan by taking into account the user's physical strength and health condition. For example, it proposes a route that avoids long travel times. The route optimization unit also proposes a travel plan that includes rest spots and places to relax based on the user's health condition. For example, it proposes cafes and parks where users can take a break along the way. The route optimization unit also proposes a plan that adjusts travel distance and travel time according to the user's physical strength. For example, it proposes shorter distance travel for users who are not confident in their physical strength. In this way, a reasonable travel plan can be proposed by taking into account the user's physical strength and health condition.

[0036] The route optimization unit can take into account not only tourist spots but also local events and festivals when optimizing a route. For example, the route optimization unit considers information about local events and festivals when optimizing a route and proposes a route that the user can enjoy. For example, it proposes festivals that can be stopped off on the way to an event venue. The route optimization unit also analyzes the event calendar of the area the user will be visiting and reflects this in route optimization. For example, it proposes a route taking into account events held on a specific day. The route optimization unit also builds a system that collects information about local events and festivals in real time and reflects this in route optimization. For example, it proposes a route based on events held on the day the user will visit. In this way, it is possible to propose a route that the user can enjoy by considering not only tourist spots but also local events and festivals when optimizing a route.

[0037] The route optimization unit can include historical buildings and natural scenery that interest the user when optimizing the route. For example, when optimizing the route, the route optimization unit proposes a route that includes historical buildings and natural scenery that interest the user. For example, if the user is interested in history, it proposes a route that goes around historical buildings. The route optimization unit also analyzes the user's preferences and personality and optimizes a route that includes spots of interest. For example, it proposes a scenic route for a user who wants to enjoy natural scenery. The route optimization unit also builds a system that collects information on historical buildings and natural scenery and reflects it in route optimization. For example, it proposes a route based on tourist spots in the area that the user visits. In this way, by including historical buildings and natural scenery that the user is interested in when optimizing the route, it is possible to propose a route that the user will enjoy.

[0038] The congestion inference unit can analyze past event data and traffic data to predict congestion patterns for specific time periods and locations. The congestion inference unit, for example, analyzes past event data to build a system that predicts congestion patterns for specific time periods and locations. For example, congestion predictions are made based on the number of event participants in past events and traffic data. The congestion inference unit also analyzes traffic data to predict congestion patterns for specific time periods and locations. For example, it identifies time periods when congestion is expected based on past traffic volume data. The congestion inference unit also integrates event data and traffic data to develop a system that predicts congestion patterns for specific time periods and locations. For example, it makes congestion predictions based on traffic conditions at the time an event is held. In this way, congestion patterns for specific time periods and locations can be predicted by analyzing past event data and traffic data.

[0039] The congestion inference unit can dynamically suggest routes to avoid congestion based on the user's real-time location information. The congestion inference unit, for example, builds a system that dynamically suggests routes to avoid congestion based on the user's real-time location information. For example, it suggests routes that avoid places where congestion is expected in real time. The congestion inference unit also integrates the user's location information with congestion prediction data to suggest the optimal route to avoid congestion. For example, it monitors congestion conditions in real time and dynamically adjusts the optimal route. The congestion inference unit also develops an application that suggests routes to avoid congestion in real time based on the user's location information. For example, it suggests routes to avoid congestion while the user is traveling. This makes it possible to dynamically suggest routes to avoid congestion based on the user's real-time location information.

[0040] The congestion inference unit can suggest cafes and rest spots where the user can spend time comfortably based on the inference result of the congestion level. The congestion inference unit, for example, builds a system that suggests cafes and rest spots where the user can spend time comfortably based on the inference result of the congestion level. For example, it suggests cafes and rest spots that are not too crowded. The congestion inference unit also takes into account the user's preferences and personality and suggests spots where the user can spend time comfortably based on the inference result of the congestion level. For example, it suggests cafes where you can relax and quiet parks. The congestion inference unit also develops an application that analyzes the inference result of the congestion level in real time and suggests cafes and rest spots where the user can spend time comfortably. For example, it suggests spots that can be visited during times when it is not too crowded. In this way, it is possible to suggest cafes and rest spots where the user can spend time comfortably based on the inference result of the congestion level.

[0041] The congestion inference unit can suggest alternative tourist spots that the user can enjoy and avoid crowds based on the inference result of the congestion level. The congestion inference unit, for example, builds a system that suggests alternative tourist spots that the user can enjoy and avoid crowds based on the inference result of the congestion level. For example, it suggests tourist spots that are less crowded. The congestion inference unit also suggests alternative tourist spots based on the inference result of the congestion level, taking into account the user's preferences and personality. For example, it suggests places that are less crowded from among spots that the user is interested in. The congestion inference unit also develops an application that analyzes the inference result of the congestion level in real time and suggests alternative tourist spots that the user can enjoy and avoid crowds. For example, it suggests spots that can be visited during times when they are less crowded. In this way, it is possible to suggest alternative tourist spots that the user can enjoy and avoid crowds based on the inference result of the congestion level.

[0042] The suggestion unit can analyze the user's past event participation history and suggest the optimal way to spend their time. The suggestion unit, for example, analyzes the user's past event participation history and builds a system that suggests the optimal way to spend their time. For example, suggestions are made based on spots and activities visited before and after events attended in the past. The suggestion unit also identifies ways of spending time that have been well-received in the past based on the user's event participation history and suggests the optimal way to spend their time based on that. For example, it suggests cafes and tourist spots where people have relaxed in the past. The suggestion unit also develops a system that analyzes the event participation history and suggests the optimal plan based on ways of spending time that the user has enjoyed in the past. For example, it makes suggestions based on places visited in the past and activities participated in. In this way, the optimal way to spend their time can be suggested by analyzing the user's past event participation history.

[0043] The suggestion unit can suggest ways to relax by taking into account the user's physical condition and mood. The suggestion unit, for example, builds a system that suggests ways to relax by taking into account the user's physical condition and mood. For example, it suggests relaxing cafes and spas. The suggestion unit also identifies ways to relax based on the user's physical condition data and suggests the optimal plan based on that. For example, if the user is not feeling well, it suggests taking a break in a quiet place. The suggestion unit also develops a system that analyzes the user's mood in real time and suggests ways to relax. For example, if the user is feeling down, it suggests a refreshing activity. In this way, it is possible to suggest ways to relax by taking into account the user's physical condition and mood.

[0044] The suggestion unit can suggest activities that allow users to experience local culture and traditions before or after participating in an event. The suggestion unit, for example, builds a system that suggests activities that allow users to experience local culture and traditions before or after participating in an event. For example, it suggests local festivals and experiences of traditional crafts. The suggestion unit also takes into account the user's preferences and personality to suggest activities that allow users to experience local culture and traditions. For example, it suggests activities related to culture and traditions that interest the user. The suggestion unit also collects information about local culture and traditions and develops a system that suggests activities that can be experienced before or after participating in an event. For example, it suggests spots where users can experience local history and culture. This makes it possible to suggest activities that allow users to experience local culture and traditions before or after participating in an event.

[0045] The suggestion unit can suggest workshops and seminars that the user may be interested in before or after participating in an event. The suggestion unit, for example, builds a system that suggests workshops and seminars that the user may be interested in before or after participating in an event. For example, it suggests workshops related to themes that the user is interested in. The suggestion unit also takes into account the user's preferences and personality to suggest workshops and seminars that the user may be interested in. For example, it suggests seminars related to fields that the user is interested in. The suggestion unit also collects information about workshops and seminars, and develops a system that suggests activities that the user can participate in before or after participating in an event. For example, it suggests events related to themes that the user is interested in. This makes it possible to suggest workshops and seminars that the user may be interested in before or after participating in an event.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The suggestion unit can analyze the user's past event participation history and suggest the optimal way to spend their time. For example, suggestions can be made based on spots and activities visited before and after events previously attended. The suggestion unit can also identify ways of spending time that have been well-received in the past based on the user's event participation history and suggest the optimal way to spend their time based on that. For example, it can suggest cafes and tourist spots where users have previously enjoyed relaxing. The suggestion unit can also analyze the event participation history and develop a system that suggests the optimal plan based on ways of spending time that the user has previously enjoyed. For example, it can make suggestions based on places visited and activities participated in in the past. In this way, the optimal way to spend their time can be suggested by analyzing the user's past event participation history.

[0048] The suggestion unit can suggest activities that allow users to experience local culture and traditions before or after participating in an event. For example, it can suggest local festivals or experiences with traditional crafts. The suggestion unit also considers the user's preferences and personality to suggest activities that allow users to experience local culture and traditions. For example, it can suggest activities related to culture and traditions that interest the user. The suggestion unit also collects information about local culture and traditions, and develops a system that suggests activities that can be experienced before or after participating in an event. For example, it can suggest spots where users can experience local history and culture. This makes it possible to suggest activities that allow users to experience local culture and traditions before or after participating in an event.

[0049] The suggestion unit can suggest workshops and seminars that the user may be interested in before or after attending an event. For example, it can suggest workshops related to themes that interest the user. The suggestion unit also considers the user's preferences and personality to suggest workshops and seminars that the user may be interested in. For example, it can suggest seminars related to fields that the user is interested in. The suggestion unit also collects information about workshops and seminars, and develops a system that suggests activities that the user can participate in before or after attending an event. For example, it can suggest events related to themes that the user is interested in. This makes it possible to suggest workshops and seminars that the user may be interested in before or after attending an event.

[0050] The route optimization unit can propose the optimal route depending on the user's means of transportation. For example, if the user is traveling on foot, it will propose an easy-to-walk route. The route optimization unit will also propose the optimal route including information on bicycle-only roads and bicycle parking areas to a user traveling by bicycle. For example, it will propose tourist spots that are easily accessible by bicycle. The route optimization unit will also propose a route that takes into account the optimal transfer routes and timetables to a user using public transportation. For example, it will propose a route that includes information on bus and train transfers. This makes it possible to propose the optimal route depending on the user's means of transportation.

[0051] The route optimization unit can propose a reasonable travel plan by taking into account the user's physical strength and health condition. For example, it can propose a route that avoids long travel times. The route optimization unit also proposes a travel plan that includes rest spots and places to relax based on the user's health condition. For example, it can propose cafes and parks where users can take a break along the way. The route optimization unit also proposes a plan that adjusts travel distance and travel time according to the user's physical strength. For example, it can propose shorter distance travel for users who are not confident in their physical strength. In this way, it is possible to propose a reasonable travel plan by taking into account the user's physical strength and health condition.

[0052] The route optimization unit can take into account not only tourist spots but also local events and festivals when optimizing routes. For example, when optimizing routes, it takes into account information about local events and festivals and suggests routes that the user can enjoy. For example, it suggests festivals that can be stopped off on the way to an event venue. The route optimization unit also analyzes the event calendar of the area the user will be visiting and reflects this in route optimization. For example, it suggests routes that take into account events held on specific days. The route optimization unit also builds a system that collects information on local events and festivals in real time and reflects this in route optimization. For example, it suggests routes based on events held on the day the user will visit. This allows it to take into account not only tourist spots but also local events and festivals when optimizing routes, making it possible to suggest routes that the user can enjoy.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The preference analysis unit analyzes the user's preferences and personality. For example, it collects and analyzes data on the user's past travel history, preferences, and personality, and performs analysis based on information such as the places the user has visited, events they have participated in, and their favorite activities. Furthermore, it can also use generative AI (e.g., text generation AI or multimodal generation AI) to perform analysis. Step 2: The route optimization unit optimizes the routes between each visit spot and the event venue based on the information analyzed by the preference analysis unit. For example, if the route between the spot the user wants to visit and the event venue is optimized, and the user specifies tourist spots or restaurants they want to visit on the way to the event venue, the system will optimize that route and suggest an efficient route. In addition, route optimization can also be performed using generative AI. Step 3: The congestion inference unit infers the level of congestion in each location based on the route optimized by the route optimization unit. For example, it can predict the level of congestion at a specific time or location based on past data and real-time information, and then use generative AI to infer the level of congestion. It can also infer the level of congestion so that users can avoid crowds and enjoy a more comfortable experience. Step 4: The proposal unit suggests optimal ways to spend time before and after participating in the event based on the congestion level inferred by the congestion inference unit. For example, it suggests optimal ways to spend time before and after participating in the event based on the user's preferences and personality, route optimization, and the inferred congestion level. Furthermore, it can also use generative AI to suggest optimal ways to spend time. It can also suggest cafes and tourist spots where users can relax before the event, or restaurants where they can enjoy dinner after the event.

[0055] (Example 2) The travel plan proposal system according to an embodiment of the present invention utilizes AI technology to propose optimal travel plans for users traveling to events such as watching sports or attending live concerts. This system analyzes the user's preferences and personality, optimizes routes between each destination and the event venue, and estimates the level of congestion in each location to propose optimal ways to spend time before and after the event. This allows the travel plan proposal system to effectively utilize the time before and after the event, enabling users to enjoy a fulfilling trip.

[0056] The travel plan proposal system according to the embodiment includes a preference analysis unit, a route optimization unit, a congestion inference unit, and a proposal unit. The preference analysis unit analyzes the user's preferences and personality. For example, the preference analysis unit collects and analyzes data related to the user's past travel history, preferences, and personality. The preference analysis unit can also analyze the user's preferences and personality based on information such as the user's past visited places, events attended, and favorite activities. The preference analysis unit can also analyze the user's preferences and personality using a generation AI (e.g., a text generation AI or a multimodal generation AI). The route optimization unit optimizes routes between each visit spot and the event venue based on the information analyzed by the preference analysis unit. For example, the route optimization unit optimizes routes between spots the user wants to visit and the event venue. The route optimization unit can also optimize routes and propose efficient travel routes when the user specifies tourist spots or restaurants they want to visit on the way to the event venue. The route optimization unit can also optimize routes using a generation AI. The congestion inference unit infers the level of congestion in each location based on the route optimized by the route optimization unit. For example, the congestion inference unit predicts the level of congestion at a specific time period or location based on past data or real-time information. The congestion inference unit can also infer the level of congestion using a generation AI. Furthermore, the congestion inference unit can infer the level of congestion so that users can avoid crowds and enjoy a comfortable stay. The suggestion unit suggests optimal ways to spend time before and after participating in an event based on the level of congestion inferred by the congestion inference unit. For example, the suggestion unit suggests optimal ways to spend time before and after participating in an event based on the user's preferences and personality, route optimization, and the inferred results of the level of congestion. The suggestion unit can also suggest optimal ways to spend time using a generation AI. Furthermore, the suggestion unit can suggest cafes or tourist spots where users can relax before an event, or restaurants where users can enjoy dinner after an event. This allows the travel plan suggestion system according to the embodiment to enable users to effectively utilize the time before and after participating in an event and enjoy a fulfilling trip.For example, users can visit tourist spots before watching a sporting event or enjoy delicious local cuisine after attending a live event. This also allows users to avoid crowds and enjoy a more comfortable trip, making for a less stressful experience.

[0057] The preference analysis unit analyzes a user's social media posts and blog articles to extract travel-related emotions and interests, thereby analyzing the user's preferences and personality. For example, the preference analysis unit analyzes a user's social media posts and extracts travel-related keywords and hashtags. For example, it collects information about tourist spots and activities frequently posted by the user and analyzes the user's preferences and personality. The preference analysis unit also analyzes the user's blog articles and extracts travel-related emotional expressions and evaluations. For example, it estimates the user's preferences and personality based on their impressions and evaluations of places they have visited in the past. The preference analysis unit also identifies themes and topics that interest the user from the content of social media and blog posts and suggests travel plans based on them. For example, if the user is interested in natural landscapes or historical buildings, it suggests related spots. In this way, by analyzing a user's social media posts and blog articles, it is possible to extract travel-related emotions and interests and analyze the user's preferences and personality.

[0058] The preference analysis unit analyzes the user's voice memos and conversation history and can infer preferences and personality from travel-related comments. For example, the preference analysis unit analyzes the user's voice memos and extracts travel-related keywords and phrases. For example, it identifies places the user wants to visit and activities they are interested in from notes recorded while traveling. The preference analysis unit also analyzes the user's conversation history and extracts travel-related comments. For example, it infers preferences and personality based on tourist spots and events the user discussed in conversations with friends and family. The preference analysis unit also uses voice recognition technology to convert the user's voice memos and conversation history into text data and analyzes the content. For example, it suggests travel plans based on the emotions and evaluations the user felt during the trip. In this way, by analyzing the user's voice memos and conversation history, it is possible to infer preferences and personality from travel-related comments.

[0059] The preference analysis unit uses the emotion estimation function to analyze the user's emotional reactions to places they have visited in the past and can propose a travel plan that elicits positive emotions. The preference analysis unit, for example, analyzes the user's emotional reactions to places they have visited in the past and identifies spots that elicit positive emotions. For example, it calculates an emotion score based on photos and comments from places they have visited in the past. The preference analysis unit also uses the emotion estimation function to analyze the user's emotional reactions to places they have visited in real time and propose a travel plan that elicits positive emotions. For example, it suggests similar spots based on the emotion scores of places they have visited in the past. The preference analysis unit also analyzes the user's past travel history and identifies spots and activities that elicit positive emotions. For example, it customizes a travel plan based on the user's emotional reactions to places they have visited in the past. In this way, the emotion estimation function can be used to analyze the user's emotional reactions to places they have visited in the past and propose a travel plan that elicits positive emotions.

[0060] When analyzing the user's preferences and personality, the preference analysis unit also takes into account the preferences of family and friends, allowing it to propose the optimal plan for a group trip. For example, the preference analysis unit collects the preferences and personalities of the user's family and friends and proposes a travel plan based on the preferences of the entire group. For example, it proposes tourist spots and activities that the whole family can enjoy. In addition, the preference analysis unit considers the preferences and personalities of each member when traveling in a group and proposes a plan that will satisfy everyone. For example, it creates a balanced plan so that members with different interests can enjoy themselves. In addition, the preference analysis unit analyzes the travel history and preferences of the user's family and friends and customizes the travel plan based on the preferences of the entire group. For example, it proposes a plan that the whole group can enjoy based on places visited in the past and events attended. In this way, the optimal plan for a group trip can be proposed by taking into account the preferences of family and friends.

[0061] The preference analysis unit can consider the influence of seasons and weather when analyzing the user's preferences and personality, and suggest the optimal travel time. The preference analysis unit, for example, analyzes the user's preferences and personality, and suggests the optimal travel time by considering the influence of seasons and weather. For example, it suggests a season in which the user can enjoy their favorite activities. The preference analysis unit also analyzes the user's past travel history and identifies the optimal travel time by considering the influence of seasons and weather. For example, it suggests the optimal travel time based on weather data of places the user has visited in the past. The preference analysis unit also customizes the travel plan based on the user's preferences and personality, taking into account the influence of seasons and weather. For example, it suggests tourist spots and activities that match the user's preferred seasons and weather. In this way, it is possible to suggest the optimal travel time by considering the influence of seasons and weather.

[0062] The preference analysis unit can use the emotion estimation function to propose a plan to reduce the stress and anxiety the user feels while traveling. The preference analysis unit, for example, uses the emotion estimation function to analyze the stress and anxiety the user feels while traveling in real time and propose a plan to reduce that stress. For example, it proposes relaxing spots and activities. The preference analysis unit also analyzes the user's past travel history, identifies situations in which the user felt stressed or anxious, and proposes a plan to reduce that stress. For example, it creates a plan that avoids places where the user felt stressed in the past. The preference analysis unit also uses the emotion estimation function to propose activities and spots to reduce the stress and anxiety the user feels while traveling. For example, it proposes relaxing cafes and natural scenery. In this way, the emotion estimation function can be used to propose a plan to reduce the stress and anxiety the user feels while traveling.

[0063] The route optimization unit can propose an optimal route depending on the user's means of transportation. The route optimization unit, for example, builds a system that proposes an optimal route depending on the user's means of transportation. For example, when traveling on foot, it proposes an easy-to-walk route. The route optimization unit also proposes an optimal route that includes information on bicycle-only roads and bicycle parking areas for users traveling by bicycle. For example, it proposes tourist spots that are easily accessible by bicycle. The route optimization unit also proposes a route that takes into account optimal transfer routes and timetables for users using public transportation. For example, it proposes a route that includes information on bus and train transfers. This makes it possible to propose an optimal route depending on the user's means of transportation.

[0064] The route optimization unit can propose a reasonable travel plan by taking into account the user's physical strength and health condition. The route optimization unit, for example, builds a system that proposes a reasonable travel plan by taking into account the user's physical strength and health condition. For example, it proposes a route that avoids long travel times. The route optimization unit also proposes a travel plan that includes rest spots and places to relax based on the user's health condition. For example, it proposes cafes and parks where users can take a break along the way. The route optimization unit also proposes a plan that adjusts travel distance and travel time according to the user's physical strength. For example, it proposes shorter distance travel for users who are not confident in their physical strength. In this way, a reasonable travel plan can be proposed by taking into account the user's physical strength and health condition.

[0065] The route optimization unit can use the emotion estimation function to suggest routes that reduce fatigue and stress felt by the user while traveling. For example, the route optimization unit uses the emotion estimation function to analyze the fatigue and stress felt by the user while traveling in real time and suggest routes that reduce that fatigue. For example, it suggests a scenic route that allows for relaxation. The route optimization unit also analyzes the user's past travel history, identifies situations in which the user felt fatigue or stress, and suggests routes that reduce that fatigue. For example, it creates a route that avoids crowds. The route optimization unit also uses the emotion estimation function to suggest rest spots and activities that reduce fatigue and stress felt by the user while traveling. For example, it suggests cafes and parks where the user can take a break along the way. In this way, the emotion estimation function can suggest routes that reduce fatigue and stress felt by the user while traveling.

[0066] The route optimization unit can take into account not only tourist spots but also local events and festivals when optimizing a route. For example, the route optimization unit considers information about local events and festivals when optimizing a route and proposes a route that the user can enjoy. For example, it proposes festivals that can be stopped off on the way to an event venue. The route optimization unit also analyzes the event calendar of the area the user will be visiting and reflects this in route optimization. For example, it proposes a route taking into account events held on a specific day. The route optimization unit also builds a system that collects information about local events and festivals in real time and reflects this in route optimization. For example, it proposes a route based on events held on the day the user will visit. In this way, it is possible to propose a route that the user can enjoy by considering not only tourist spots but also local events and festivals when optimizing a route.

[0067] The route optimization unit can include historical buildings and natural scenery that interest the user when optimizing the route. For example, when optimizing the route, the route optimization unit proposes a route that includes historical buildings and natural scenery that interest the user. For example, if the user is interested in history, it proposes a route that goes around historical buildings. The route optimization unit also analyzes the user's preferences and personality and optimizes a route that includes spots of interest. For example, it proposes a scenic route for a user who wants to enjoy natural scenery. The route optimization unit also builds a system that collects information on historical buildings and natural scenery and reflects it in route optimization. For example, it proposes a route based on tourist spots in the area that the user visits. In this way, by including historical buildings and natural scenery that the user is interested in when optimizing the route, it is possible to propose a route that the user will enjoy.

[0068] The route optimization unit can use the emotion estimation function to suggest activities and entertainment that the user can enjoy while traveling. For example, the route optimization unit uses the emotion estimation function to suggest activities and entertainment that the user can enjoy while traveling in real time. For example, it can suggest music or podcasts that can be enjoyed while traveling. The route optimization unit also analyzes the user's past travel history to identify activities and entertainment that can be enjoyed and optimizes the route based on that. For example, it can suggest activity spots that can be stopped at while traveling. The route optimization unit also uses the emotion estimation function to build a system that suggests activities and entertainment that the user can enjoy while traveling. For example, it can suggest events and shows that can be enjoyed while traveling. In this way, the emotion estimation function can be used to suggest activities and entertainment that the user can enjoy while traveling.

[0069] The congestion inference unit can analyze past event data and traffic data to predict congestion patterns for specific time periods and locations. The congestion inference unit, for example, analyzes past event data to build a system that predicts congestion patterns for specific time periods and locations. For example, congestion predictions are made based on the number of event participants in past events and traffic data. The congestion inference unit also analyzes traffic data to predict congestion patterns for specific time periods and locations. For example, it identifies time periods when congestion is expected based on past traffic volume data. The congestion inference unit also integrates event data and traffic data to develop a system that predicts congestion patterns for specific time periods and locations. For example, it makes congestion predictions based on traffic conditions at the time an event is held. In this way, congestion patterns for specific time periods and locations can be predicted by analyzing past event data and traffic data.

[0070] The congestion inference unit can dynamically suggest routes to avoid congestion based on the user's real-time location information. The congestion inference unit, for example, builds a system that dynamically suggests routes to avoid congestion based on the user's real-time location information. For example, it suggests routes that avoid places where congestion is expected in real time. The congestion inference unit also integrates the user's location information with congestion prediction data to suggest the optimal route to avoid congestion. For example, it monitors congestion conditions in real time and dynamically adjusts the optimal route. The congestion inference unit also develops an application that suggests routes to avoid congestion in real time based on the user's location information. For example, it suggests routes to avoid congestion while the user is traveling. This makes it possible to dynamically suggest routes to avoid congestion based on the user's real-time location information.

[0071] The congestion inference unit can use the emotion inference function to suggest optimal time periods and locations for the user to avoid crowds. The congestion inference unit, for example, uses the emotion inference function to suggest optimal time periods and locations for the user to avoid crowds in real time. For example, it suggests a schedule to avoid times when crowds are expected. The congestion inference unit also analyzes the user's past movement history to identify optimal time periods and locations for avoiding crowds. For example, it makes suggestions based on places and time periods where the user has avoided crowds in the past. The congestion inference unit also uses the emotion inference function to build a system that suggests optimal time periods and locations for the user to avoid crowds. For example, it suggests a route to avoid crowds based on the user's emotional response. In this way, the emotion inference function can suggest optimal time periods and locations for the user to avoid crowds.

[0072] The congestion inference unit can suggest cafes and rest spots where the user can spend time comfortably based on the inference result of the congestion level. The congestion inference unit, for example, builds a system that suggests cafes and rest spots where the user can spend time comfortably based on the inference result of the congestion level. For example, it suggests cafes and rest spots that are not too crowded. The congestion inference unit also takes into account the user's preferences and personality and suggests spots where the user can spend time comfortably based on the inference result of the congestion level. For example, it suggests cafes where you can relax and quiet parks. The congestion inference unit also develops an application that analyzes the inference result of the congestion level in real time and suggests cafes and rest spots where the user can spend time comfortably. For example, it suggests spots that can be visited during times when it is not too crowded. In this way, it is possible to suggest cafes and rest spots where the user can spend time comfortably based on the inference result of the congestion level.

[0073] The congestion inference unit can suggest alternative tourist spots that the user can enjoy and avoid crowds based on the inference result of the congestion level. The congestion inference unit, for example, builds a system that suggests alternative tourist spots that the user can enjoy and avoid crowds based on the inference result of the congestion level. For example, it suggests tourist spots that are less crowded. The congestion inference unit also suggests alternative tourist spots based on the inference result of the congestion level, taking into account the user's preferences and personality. For example, it suggests places that are less crowded from among spots that the user is interested in. The congestion inference unit also develops an application that analyzes the inference result of the congestion level in real time and suggests alternative tourist spots that the user can enjoy and avoid crowds. For example, it suggests spots that can be visited during times when they are less crowded. In this way, it is possible to suggest alternative tourist spots that the user can enjoy and avoid crowds based on the inference result of the congestion level.

[0074] The congestion inference unit can use the emotion inference function to suggest the optimal means of transportation for the user to avoid congestion. The congestion inference unit, for example, uses the emotion inference function to suggest the optimal means of transportation for the user to avoid congestion in real time. For example, it suggests a means of transportation that avoids time periods when congestion is expected. The congestion inference unit also analyzes the user's past travel history to identify the optimal means of transportation to avoid congestion. For example, it suggests a means of transportation that the user has used to avoid congestion in the past. The congestion inference unit also uses the emotion inference function to build a system that suggests the optimal means of transportation for the user to avoid congestion. For example, it suggests a means of transportation that avoids congestion based on the user's emotional reaction. In this way, the emotion inference function can be used to suggest the optimal means of transportation for the user to avoid congestion.

[0075] The suggestion unit can analyze the user's past event participation history and suggest the optimal way to spend their time. The suggestion unit, for example, analyzes the user's past event participation history and builds a system that suggests the optimal way to spend their time. For example, suggestions are made based on spots and activities visited before and after events attended in the past. The suggestion unit also identifies ways of spending time that have been well-received in the past based on the user's event participation history and suggests the optimal way to spend their time based on that. For example, it suggests cafes and tourist spots where people have relaxed in the past. The suggestion unit also develops a system that analyzes the event participation history and suggests the optimal plan based on ways of spending time that the user has enjoyed in the past. For example, it makes suggestions based on places visited in the past and activities participated in. In this way, the optimal way to spend their time can be suggested by analyzing the user's past event participation history.

[0076] The suggestion unit can suggest ways to relax by taking into account the user's physical condition and mood. The suggestion unit, for example, builds a system that suggests ways to relax by taking into account the user's physical condition and mood. For example, it suggests relaxing cafes and spas. The suggestion unit also identifies ways to relax based on the user's physical condition data and suggests the optimal plan based on that. For example, if the user is not feeling well, it suggests taking a break in a quiet place. The suggestion unit also develops a system that analyzes the user's mood in real time and suggests ways to relax. For example, if the user is feeling down, it suggests a refreshing activity. In this way, it is possible to suggest ways to relax by taking into account the user's physical condition and mood.

[0077] The suggestion unit can use the emotion estimation function to suggest ways for the user to spend time that will increase the anticipation and excitement the user feels before and after an event. For example, the suggestion unit uses the emotion estimation function to suggest ways for the user to spend time that will increase the anticipation and excitement the user feels before and after an event in real time. For example, the suggestion unit suggests activities to raise the mood before the event. The suggestion unit also analyzes the user's past event participation history to identify ways to spend time that will increase the anticipation and excitement, and proposes an optimal plan based on that. For example, the suggestion unit makes suggestions based on activities that excited the user in the past. The suggestion unit also uses the emotion estimation function to build a system that suggests ways for the user to spend time that will increase the anticipation and excitement the user feels before and after an event. For example, the suggestion unit suggests spots and activities that will increase the anticipation and excitement the user feels based on the user's emotional reaction. In this way, the emotion estimation function can be used to suggest ways for the user to spend time that will increase the anticipation and excitement the user feels before and after an event.

[0078] The suggestion unit can suggest activities that allow users to experience local culture and traditions before or after participating in an event. The suggestion unit, for example, builds a system that suggests activities that allow users to experience local culture and traditions before or after participating in an event. For example, it suggests local festivals and experiences of traditional crafts. The suggestion unit also takes into account the user's preferences and personality to suggest activities that allow users to experience local culture and traditions. For example, it suggests activities related to culture and traditions that interest the user. The suggestion unit also collects information about local culture and traditions and develops a system that suggests activities that can be experienced before or after participating in an event. For example, it suggests spots where users can experience local history and culture. This makes it possible to suggest activities that allow users to experience local culture and traditions before or after participating in an event.

[0079] The suggestion unit can suggest workshops and seminars that the user may be interested in before or after participating in an event. The suggestion unit, for example, builds a system that suggests workshops and seminars that the user may be interested in before or after participating in an event. For example, it suggests workshops related to themes that the user is interested in. The suggestion unit also takes into account the user's preferences and personality to suggest workshops and seminars that the user may be interested in. For example, it suggests seminars related to fields that the user is interested in. The suggestion unit also collects information about workshops and seminars, and develops a system that suggests activities that the user can participate in before or after participating in an event. For example, it suggests events related to themes that the user is interested in. This makes it possible to suggest workshops and seminars that the user may be interested in before or after participating in an event.

[0080] The suggestion unit can use the emotion estimation function to suggest ways for the user to spend time to reduce fatigue and stress felt before and after an event. For example, the suggestion unit uses the emotion estimation function to analyze the fatigue and stress felt by the user before and after an event in real time and suggest ways to reduce it. For example, the suggestion unit can suggest relaxing cafes and spas. The suggestion unit can also analyze the user's past event participation history to identify situations in which fatigue and stress were felt and suggest ways to reduce them. For example, the suggestion unit can make suggestions based on places and activities where the user was able to relax in the past. The suggestion unit can also use the emotion estimation function to build a system that suggests activities and spots for reducing fatigue and stress felt by the user before and after an event. For example, the suggestion unit can suggest natural landscapes and quiet places where the user can relax. In this way, the emotion estimation function can be used to suggest ways for the user to spend time to reduce fatigue and stress felt before and after an event.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The suggestion unit can analyze the user's past event participation history and suggest the optimal way to spend their time. For example, suggestions can be made based on spots and activities visited before and after events previously attended. The suggestion unit can also identify ways of spending time that have been well-received in the past based on the user's event participation history and suggest the optimal way to spend their time based on that. For example, it can suggest cafes and tourist spots where users have previously enjoyed relaxing. The suggestion unit can also analyze the event participation history and develop a system that suggests the optimal plan based on ways of spending time that the user has previously enjoyed. For example, it can make suggestions based on places visited and activities participated in in the past. In this way, the optimal way to spend their time can be suggested by analyzing the user's past event participation history.

[0083] The suggestion unit can take into account the user's physical condition and mood and suggest ways to relax. For example, it can suggest cafes or spas where you can relax. The suggestion unit can also identify ways to relax based on the user's physical condition data and suggest the optimal plan based on that. For example, if the user is not feeling well, it can suggest taking a break in a quiet place. The suggestion unit can also analyze the user's mood in real time and develop a system that suggests ways to relax. For example, if the user is feeling down, it can suggest refreshing activities. This makes it possible to suggest ways to relax by taking the user's physical condition and mood into consideration.

[0084] The suggestion unit can suggest activities that allow users to experience local culture and traditions before or after participating in an event. For example, it can suggest local festivals or experiences with traditional crafts. The suggestion unit also considers the user's preferences and personality to suggest activities that allow users to experience local culture and traditions. For example, it can suggest activities related to culture and traditions that interest the user. The suggestion unit also collects information about local culture and traditions, and develops a system that suggests activities that can be experienced before or after participating in an event. For example, it can suggest spots where users can experience local history and culture. This makes it possible to suggest activities that allow users to experience local culture and traditions before or after participating in an event.

[0085] The suggestion unit can suggest workshops and seminars that the user may be interested in before or after attending an event. For example, it can suggest workshops related to themes that interest the user. The suggestion unit also considers the user's preferences and personality to suggest workshops and seminars that the user may be interested in. For example, it can suggest seminars related to fields that the user is interested in. The suggestion unit also collects information about workshops and seminars, and develops a system that suggests activities that the user can participate in before or after attending an event. For example, it can suggest events related to themes that the user is interested in. This makes it possible to suggest workshops and seminars that the user may be interested in before or after attending an event.

[0086] The suggestion unit can use the emotion estimation function to suggest ways to spend time that will increase the anticipation and excitement the user feels before and after an event. For example, it can suggest activities to boost the mood before the event. The suggestion unit can also analyze the user's past event participation history to identify ways to spend time that will increase anticipation and excitement, and based on that, suggest an optimal plan. For example, it can make suggestions based on activities that have excited the user in the past. The suggestion unit can also use the emotion estimation function to build a system that suggests ways to spend time that will increase the anticipation and excitement the user feels before and after an event. For example, it can suggest spots and activities that will increase anticipation and excitement based on the user's emotional reactions. In this way, the emotion estimation function can be used to suggest ways to spend time that will increase the anticipation and excitement the user feels before and after an event.

[0087] The suggestion unit can use the emotion estimation function to suggest ways for the user to spend time to reduce the fatigue and stress they feel before and after an event. For example, it can suggest cafes and spas where they can relax. The suggestion unit can also analyze the user's past event participation history to identify situations in which they felt fatigue or stress, and suggest ways for them to reduce that fatigue or stress. For example, it can make suggestions based on places and activities where the user has found relaxation in the past. The suggestion unit can also use the emotion estimation function to build a system that suggests activities and spots for the user to reduce the fatigue and stress they feel before and after an event. For example, it can suggest natural landscapes and quiet places where they can relax. In this way, the emotion estimation function can be used to suggest ways for the user to spend time to reduce the fatigue and stress they feel before and after an event.

[0088] The route optimization unit can propose the optimal route depending on the user's means of transportation. For example, if the user is traveling on foot, it will propose an easy-to-walk route. The route optimization unit will also propose the optimal route including information on bicycle-only roads and bicycle parking areas to a user traveling by bicycle. For example, it will propose tourist spots that are easily accessible by bicycle. The route optimization unit will also propose a route that takes into account the optimal transfer routes and timetables to a user using public transportation. For example, it will propose a route that includes information on bus and train transfers. This makes it possible to propose the optimal route depending on the user's means of transportation.

[0089] The route optimization unit can propose a reasonable travel plan by taking into account the user's physical strength and health condition. For example, it can propose a route that avoids long travel times. The route optimization unit also proposes a travel plan that includes rest spots and places to relax based on the user's health condition. For example, it can propose cafes and parks where users can take a break along the way. The route optimization unit also proposes a plan that adjusts travel distance and travel time according to the user's physical strength. For example, it can propose shorter distance travel for users who are not confident in their physical strength. In this way, it is possible to propose a reasonable travel plan by taking into account the user's physical strength and health condition.

[0090] The route optimization unit can use the emotion estimation function to suggest routes that reduce fatigue and stress felt by the user while traveling. For example, it can suggest routes with beautiful scenery that allow for relaxation. The route optimization unit can also analyze the user's past travel history, identify situations in which fatigue or stress was felt, and suggest routes to reduce that fatigue. For example, it can create routes that avoid crowds. The route optimization unit can also use the emotion estimation function to suggest rest spots and activities that reduce fatigue and stress felt by the user while traveling. For example, it can suggest cafes or parks where users can take a break along the way. In this way, the emotion estimation function can suggest routes that reduce fatigue and stress felt by the user while traveling.

[0091] The route optimization unit can take into account not only tourist spots but also local events and festivals when optimizing routes. For example, when optimizing routes, it takes into account information about local events and festivals and suggests routes that the user can enjoy. For example, it suggests festivals that can be stopped off on the way to an event venue. The route optimization unit also analyzes the event calendar of the area the user will be visiting and reflects this in route optimization. For example, it suggests routes that take into account events held on specific days. The route optimization unit also builds a system that collects information on local events and festivals in real time and reflects this in route optimization. For example, it suggests routes based on events held on the day the user will visit. This allows it to take into account not only tourist spots but also local events and festivals when optimizing routes, making it possible to suggest routes that the user can enjoy.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The preference analysis unit analyzes the user's preferences and personality. For example, it collects and analyzes data on the user's past travel history, preferences, and personality, and performs analysis based on information such as the places the user has visited, events they have participated in, and their favorite activities. Furthermore, it can also use generative AI (e.g., text generation AI or multimodal generation AI) to perform analysis. Step 2: The route optimization unit optimizes the routes between each visit spot and the event venue based on the information analyzed by the preference analysis unit. For example, if the route between the spot the user wants to visit and the event venue is optimized, and the user specifies tourist spots or restaurants they want to visit on the way to the event venue, the system will optimize that route and suggest an efficient route. In addition, route optimization can also be performed using generative AI. Step 3: The congestion inference unit infers the level of congestion in each location based on the route optimized by the route optimization unit. For example, it can predict the level of congestion at a specific time or location based on past data and real-time information, and then use generative AI to infer the level of congestion. It can also infer the level of congestion so that users can avoid crowds and enjoy a more comfortable experience. Step 4: The proposal unit suggests optimal ways to spend time before and after participating in the event based on the congestion level inferred by the congestion inference unit. For example, it suggests optimal ways to spend time before and after participating in the event based on the user's preferences and personality, route optimization, and the inferred congestion level. Furthermore, it can also use generative AI to suggest optimal ways to spend time. It can also suggest cafes and tourist spots where users can relax before the event, or restaurants where they can enjoy dinner after the event.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

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

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

[0099] The data processing device 12 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0120] 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.

[0121] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0135] 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.

[0136] 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.

[0137] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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. [Explanation of symbols]

[0161] 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. a preference analysis unit that analyzes the user's preferences and personality; a route optimization unit that optimizes routes between each of the visiting spots and the event venue based on the information analyzed by the preference analysis unit; a congestion inference unit that infers the degree of congestion in each location based on the route optimized by the route optimization unit; a suggestion unit that suggests optimal ways to spend time before and after participating in the event based on the degree of congestion inferred by the congestion inference unit. A system characterized by:

2. The route optimization unit Proposing the optimal route based on the user's mode of transportation 2. The system of claim 1.

3. The congestion inference unit Analyzing past event and traffic data to predict congestion patterns at specific times and locations 2. The system of claim 1.

4. The proposal unit Analyzing users' past event participation history and suggesting optimal ways to spend their time 2. The system of claim 1.

5. The preference analysis unit Analyzes the user's emotional reactions to places they have visited in the past and suggests travel plans that evoke positive emotions 2. The system of claim 1.

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