System

Abstract

An object of a system according to an embodiment is to automatically generate a travel plan and provide an optimal travel plan based on a user's hobbies and preferences.SOLUTION: A system includes a generation part, a learning part, and a cooperation part. The generation unit automatically generates a travel plan. The learning unit learns hobbies or preferences of the user on the basis of the plan generated by the generation unit. The cooperation unit cooperates with a specific service on the basis of the information learned by the learning 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 techniques require a lot of time and effort when planning a trip, which can lead to planning fatigue.

[0005] The system according to the embodiment aims to automatically generate a travel plan and provide an optimal travel plan based on the user's hobbies and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a learning unit, and a linking unit. The generation unit automatically generates a travel plan. The learning unit learns the user's hobbies or preferences based on the plan generated by the generation unit. The linking unit links with specific services based on the information learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a travel plan and provide an optimal travel plan based on the user's hobbies and preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An automatic travel plan generation system according to an embodiment of the present invention is a system designed to eliminate travel planning fatigue and allow users to enjoy traveling more easily. The automatic travel plan generation system analyzes information simply by inputting information such as "when, with whom, and where to go" and automatically generates a travel plan including a round trip schedule, transportation options, accommodations, recommended tourist spots, and more. The automatic travel plan generation system can also learn the user's hobbies and preferences to suggest more personalized travel plans and tourist spots. For example, it can prioritize suggestions of activities and tourist spots that the user prefers based on past travel history and reviews. Furthermore, by linking with other related services, the automatic travel plan generation system can not only automatically generate plans but also complete reservations on a single platform. For example, accommodation and restaurant reservations can be made simultaneously, significantly reducing the user's effort. This simplifies travel planning and allows users to enjoy traveling more easily. The automatic travel plan generation system thus eliminates travel planning fatigue and allows users to enjoy traveling more easily. For example, a travel plan can be automatically generated simply by the user entering "when, with whom, and where" information, eliminating the need for planning. Furthermore, by learning the user's hobbies and preferences, the platform can suggest more personalized travel plans. Furthermore, by linking with other related services, the entire process, right down to booking, can be completed on a single platform, significantly reducing the user's workload.

[0029] An automatic travel plan generation system according to an embodiment includes a generation unit, a learning unit, and a linking unit. The generation unit automatically generates a travel schedule, transportation options, accommodations, recommended tourist spots, and the like based on information input by a user, such as "when, with whom, and where to go." For example, the generation unit analyzes the information input by the user and generates an optimal travel plan. The generation unit can also calculate an optimal route based on map data and generate a video along that route. For example, the generation unit can calculate an efficient route based on map data and generate a video along that route. The learning unit analyzes a user's preferences based on the user's past travel history and ratings. For example, the learning unit can refer to the user's past travel history to learn the user's hobbies and preferences. The learning unit can also estimate a user's emotions and select learning data based on the estimated user emotions. For example, the learning unit can estimate a user's emotions and select learning data based on the estimated user emotions. The linking unit uses an API to automatically make accommodation reservations, restaurant reservations, and the like. For example, the linking unit may use an API to automatically make reservations for accommodations and restaurants. The linking unit may also link with other related services to complete the automatic generation of plans and reservations on a single platform. For example, the linking unit may link with other related services to complete the automatic generation of plans and reservations on a single platform. This enables the automatic travel plan generation system according to the embodiment to automatically generate travel plans, learn user hobbies and preferences, and link with other services.

[0030] The generation unit can automatically generate a tour schedule, transportation methods, accommodations, and recommended tourist spots based on the information of "when, with whom, and where to go" input by the user. The generation unit automatically generates a tour schedule based on the information of "when, with whom, and where to go" input by the user. For example, the generation unit creates an optimal tour schedule by taking into consideration the order of visits and the length of stay. The generation unit can also automatically select transportation methods. For example, the generation unit can consider transportation methods such as trains, buses, and taxis and suggest the optimal transportation method. The generation unit can also automatically select accommodations. For example, the generation unit considers accommodations such as hotels, inns, and guesthouses and suggests the optimal accommodation. The generation unit can also automatically select recommended tourist spots. For example, the generation unit considers popularity and ease of access and suggests recommended tourist spots. In this way, a travel plan can be automatically generated based on the information input by the user.

[0031] The learning unit can analyze the user's preferences based on the user's past travel history and ratings. The learning unit analyzes the user's preferences based on, for example, the user's past travel history. For example, the learning unit collects information such as places the user has visited in the past, the length of stay, and activities, and analyzes the user's preferences. The learning unit can also analyze the user's preferences based on the user's ratings. For example, the learning unit collects information such as the user's satisfaction level and reviews, and analyzes the user's preferences. This makes it possible to analyze the user's preferences based on the user's past travel history and ratings.

[0032] The linking unit can automatically make accommodation reservations or restaurant reservations using the API. The linking unit, for example, uses an API to automatically make accommodation reservations. For example, the linking unit can automatically make accommodation reservations using an accommodation reservation API. The linking unit can also automatically make restaurant reservations. For example, the linking unit can automatically make restaurant reservations using a restaurant reservation API. This makes it possible to automatically make accommodation and restaurant reservations using the API.

[0033] The generation unit can calculate an efficient route based on map data and generate a video that follows that route. The generation unit, for example, calculates an efficient route based on map data. For example, the generation unit calculates an efficient route taking into account the shortest distance and shortest time. The generation unit can also generate a video that follows that route. For example, the generation unit generates a video introducing places to visit and a video guiding the route. This makes it possible to calculate an optimal route based on map data and generate a video that follows that route.

[0034] The collaboration unit collaborates with specific related services to complete automatic plan generation and reservations on a single platform. The collaboration unit, for example, collaborates with specific related services to complete automatic plan generation and reservations on a single platform. For example, the collaboration unit collaborates with services such as transportation, tourist guides, and event tickets to complete automatic plan generation and reservations on a single platform. This allows collaboration with other related services to complete automatic plan generation and reservations on a single platform.

[0035] The generation unit can generate a more personalized plan by referring to the user's past travel history at the time of generation. The generation unit can, for example, generate a more personalized plan by referring to the user's past travel history at the time of generation. For example, the generation unit can suggest similar tourist spots based on places the user has visited in the past. The generation unit can also generate a plan including similar activities based on activities the user has liked in the past. The generation unit can also suggest similar accommodations based on accommodations that the user has rated in the past. In this way, a more personalized plan can be generated by referring to the user's past travel history.

[0036] The generation unit can suggest specific activities taking into account the user's current health condition and physical strength at the time of generation. For example, the generation unit can suggest specific activities taking into account the user's current health condition and physical strength at the time of generation. For example, if the user is in good health, the generation unit can suggest activities such as hiking or cycling. Furthermore, if the user is not confident in their physical strength, the generation unit can suggest a light walk or a sightseeing bus tour. Furthermore, if the user has a specific health problem, the generation unit can suggest an activity that takes into account the problem. In this way, appropriate activities can be suggested taking into account the user's health condition and physical strength.

[0037] The generation unit can generate an efficient plan based on the user's budget at the time of generation. For example, the generation unit generates an efficient plan based on the user's budget at the time of generation. For example, if the user's budget is limited, the generation unit can suggest cost-effective accommodations and transportation options. Furthermore, if the user's budget is abundant, the generation unit can suggest a plan including luxurious accommodations and high-end restaurants. Furthermore, the generation unit can suggest free or low-cost tourist spots according to the user's budget. In this way, an optimal plan can be generated based on the user's budget.

[0038] The generation unit can propose efficient tourist spots by taking into account the user's geographical location information during generation. For example, the generation unit proposes efficient tourist spots by taking into account the user's geographical location information during generation. For example, the generation unit preferentially proposes tourist spots close to the user's current location. The generation unit can also propose tourist spots that are easily accessible from the user's current location. The generation unit can also propose optimal tourist spots by taking into account the travel time from the user's current location. In this way, the generation unit can propose optimal tourist spots by taking into account the user's geographical location information.

[0039] The generation unit can analyze the user's social media activity at the time of generation and suggest related tourist spots. For example, the generation unit can analyze the user's social media activity at the time of generation and suggest related tourist spots. For example, the generation unit can suggest related tourist spots based on places where the user has checked in on social media. The generation unit can also analyze the content of the user's social media posts and suggest tourist spots that the user may be interested in. The generation unit can also suggest related tourist spots based on the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related tourist spots can be suggested.

[0040] The generation unit can customize the content of the plan by reflecting the user's past feedback at the time of generation. The generation unit, for example, customizes the content of the plan by reflecting the user's past feedback at the time of generation. For example, the generation unit preferentially suggests tourist spots that the user has previously rated highly. The generation unit can also exclude tourist spots that the user has previously rated poorly. The generation unit can also fine-tune the content of the plan based on the user's past feedback. In this way, the content of the plan can be customized by reflecting the user's past feedback.

[0041] The learning unit can analyze the user's preferences more accurately by referring to the user's past travel history during learning. The learning unit can analyze the user's preferences more accurately by referring to the user's past travel history during learning. For example, the learning unit analyzes preferences based on places the user has visited in the past. The learning unit can also analyze preferences based on activities the user has liked in the past. The learning unit can also analyze preferences based on accommodations the user has rated in the past. This allows the user's preferences to be analyzed more accurately by referring to the user's past travel history.

[0042] The learning unit can improve the efficiency of the learning algorithm by reflecting the user's evaluation during learning. For example, the learning unit improves the efficiency of the learning algorithm by reflecting the user's evaluation during learning. For example, the learning unit prioritizes learning of data that the user has given a high rating and optimizes the algorithm. The learning unit can also exclude data that the user has given a low rating and optimize the algorithm. The learning unit can also adjust the parameters of the learning algorithm based on the user's evaluation. In this way, the learning algorithm can be optimized by reflecting the user's evaluation.

[0043] The learning unit can collect behavioral data of the user during the trip and analyze the preferences during learning. For example, the learning unit collects behavioral data of the user during the trip and analyzes the preferences during learning. For example, the learning unit collects data on tourist spots visited by the user and analyzes the preferences. The learning unit can also collect data on activities in which the user participated and analyze the preferences. The learning unit can also collect data on facilities where the user stayed and analyze the preferences. In this way, behavioral data of the user during the trip can be collected and the preferences can be analyzed.

[0044] The learning unit can analyze preferences taking into account the user's geographical location information during learning. For example, the learning unit analyzes preferences taking into account the user's geographical location information during learning. For example, the learning unit prioritizes learning data on tourist spots close to the user's current location. The learning unit can also learn data on tourist spots that are easily accessible from the user's current location. The learning unit can also analyze preferences taking into account travel time from the user's current location. This makes it possible to analyze preferences taking into account the user's geographical location information.

[0045] The learning unit can analyze the user's social media activity and analyze their preferences during learning. For example, the learning unit analyzes the user's social media activity and analyzes their preferences during learning. For example, the learning unit learns data on locations where the user has checked in on social media. The learning unit can also analyze the content of the user's posts on social media and analyze their preferences. The learning unit can also analyze their preferences by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and their preferences can be analyzed.

[0046] The learning unit can update the learning data during learning by reflecting the user's past feedback. For example, the learning unit updates the learning data during learning by reflecting the user's past feedback. For example, the learning unit preferentially learns data that the user has given a high rating and updates the data. The learning unit can also update the data by excluding data that the user has given a low rating. The learning unit can also fine-tune the learning data based on the user's past feedback. In this way, the learning data can be updated by reflecting the user's past feedback.

[0047] During collaboration, the collaboration unit can refer to the user's past reservation history and suggest the most suitable service. During collaboration, the collaboration unit, for example, refers to the user's past reservation history and suggest the most suitable service. For example, the collaboration unit suggests similar accommodations based on accommodations that the user has used in the past. The collaboration unit can also suggest similar restaurants based on restaurants that the user has used in the past. The collaboration unit can also suggest the most suitable service based on the user's past reservation history. This makes it possible to suggest the most suitable service by referring to the user's past reservation history.

[0048] The collaboration unit can propose an efficient reservation timing in consideration of the user's current schedule during collaboration. For example, the collaboration unit can propose an efficient reservation timing in consideration of the user's current schedule during collaboration. For example, the collaboration unit can refer to the user's calendar information and propose the optimal reservation timing. The collaboration unit can also propose the optimal reservation timing in accordance with the user's plans. The collaboration unit can also propose the optimal reservation timing based on the user's schedule. This makes it possible to propose the optimal reservation timing in consideration of the user's current schedule.

[0049] The collaboration unit can propose efficient services based on the user's budget during collaboration. For example, the collaboration unit proposes efficient services based on the user's budget during collaboration. For example, when the user's budget is limited, the collaboration unit proposes services with high cost performance. Furthermore, when the user's budget is abundant, the collaboration unit can propose luxurious services. Furthermore, the collaboration unit can propose optimal services according to the user's budget. In this way, optimal services can be proposed based on the user's budget.

[0050] The collaboration unit can propose efficient services by taking into account the user's geographical location information during collaboration. For example, the collaboration unit proposes efficient services by taking into account the user's geographical location information during collaboration. For example, the collaboration unit preferentially proposes services that are close to the user's current location. The collaboration unit can also propose services that are easy to access from the user's current location. The collaboration unit can also propose optimal services by taking into account the travel time from the user's current location. This makes it possible to propose optimal services by taking into account the user's geographical location information.

[0051] The linking unit can analyze the user's social media activity at the time of linking and suggest related services. For example, the linking unit can analyze the user's social media activity at the time of linking and suggest related services. For example, the linking unit can suggest related services based on the location where the user checked in on social media. The linking unit can also analyze the content of the user's posts on social media and suggest services that the user may be interested in. The linking unit can also suggest related services based on the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related services can be suggested.

[0052] The collaboration unit can customize the services to be linked by reflecting the user's past feedback at the time of collaboration. For example, the collaboration unit customizes the services to be linked by reflecting the user's past feedback at the time of collaboration. For example, the collaboration unit preferentially suggests services that the user has previously rated highly. The collaboration unit can also exclude services that the user has previously rated poorly. The collaboration unit can also fine-tune the services to be linked based on the user's past feedback. In this way, the services to be linked can be customized by reflecting the user's past feedback.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The generation unit can generate a plan taking into account not only the user's past travel history but also the user's current lifestyle. For example, if the user has been busy recently, the generation unit can suggest a relaxing travel plan. Also, if the user has started a new hobby, the generation unit can suggest a plan that includes tourist spots and activities related to that hobby. Furthermore, if the user plans to participate in a specific event, the generation unit can generate a travel plan tailored to that event. This makes it possible to provide a travel plan that suits the user's current lifestyle.

[0055] The linking unit can propose optimal travel plans by taking into account not only the user's past travel history but also the travel history of the user's friends and family. For example, it can prioritize tourist spots that have been highly rated by the user's friends. It can also suggest accommodations that are favored by the user's family. Furthermore, it can suggest similar activities based on activities in which the user's friends and family have participated. This makes it possible to provide travel plans that take into account the travel history of the user's friends and family.

[0056] The linking unit can propose optimal travel plans taking into account the user's current health condition. For example, if the user is in good health, it can propose plans that include active activities. If the user is not in good health, it can also suggest tourist spots and accommodations where the user can relax. Furthermore, if the user has a specific health problem, it can also propose travel plans that take that problem into consideration. This makes it possible to provide travel plans that are suited to the user's health condition.

[0057] The learning unit can propose optimal travel plans by taking into consideration not only the user's past travel history but also the user's occupation and lifestyle. For example, if the user is a businessman, it can propose business-related tourist spots and accommodations. If the user is an artist, it can also propose art galleries and creative activities. Furthermore, if the user is an outdoor enthusiast, it can also propose activities to enjoy nature. This makes it possible to provide travel plans that suit the user's occupation and lifestyle.

[0058] The generation unit can propose optimal travel plans by taking into consideration not only the user's past travel history but also the user's future goals and dreams. For example, if the user has a future goal of visiting a specific country, a travel plan including that country can be proposed. Also, if the user wants to experience a specific activity, a plan including that activity can be proposed. Furthermore, if the user wants to learn about a specific culture, tourist spots related to that culture can be proposed. In this way, it is possible to provide travel plans that match the user's future goals and dreams.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The generator automatically generates a travel schedule, transportation options, accommodations, recommended tourist spots, and other information based on the user's input information about "when, with whom, and where to go." For example, the generator analyzes the information entered by the user and generates an optimal travel plan. The generator can also calculate the optimal route based on map data and generate a video that follows that route. Step 2: The learning unit analyzes the user's preferences based on the user's past travel history and ratings. For example, the learning unit refers to the user's past travel history to learn the user's hobbies and preferences. The learning unit can also estimate the user's emotions and select learning data based on the estimated user emotions. Step 3: The integration unit uses the API to automatically make accommodation and restaurant reservations. For example, the integration unit uses the API to automatically make accommodation and restaurant reservations. The integration unit can also integrate with other related services to complete the automatic generation of plans and reservations on a single platform.

[0061] (Example 2) An automatic travel plan generation system according to an embodiment of the present invention is a system designed to eliminate travel planning fatigue and allow users to enjoy traveling more easily. The automatic travel plan generation system analyzes information simply by inputting information such as "when, with whom, and where to go" and automatically generates a travel plan including a round trip schedule, transportation options, accommodations, recommended tourist spots, and more. The automatic travel plan generation system can also learn the user's hobbies and preferences to suggest more personalized travel plans and tourist spots. For example, it can prioritize suggestions of activities and tourist spots that the user prefers based on past travel history and reviews. Furthermore, by linking with other related services, the automatic travel plan generation system can not only automatically generate plans but also complete reservations on a single platform. For example, accommodation and restaurant reservations can be made simultaneously, significantly reducing the user's effort. This simplifies travel planning and allows users to enjoy traveling more easily. The automatic travel plan generation system thus eliminates travel planning fatigue and allows users to enjoy traveling more easily. For example, a travel plan can be automatically generated simply by the user entering "when, with whom, and where" information, eliminating the need for planning. Furthermore, by learning the user's hobbies and preferences, the platform can suggest more personalized travel plans. Furthermore, by linking with other related services, the entire process, right down to booking, can be completed on a single platform, significantly reducing the user's workload.

[0062] An automatic travel plan generation system according to an embodiment includes a generation unit, a learning unit, and a linking unit. The generation unit automatically generates a travel schedule, transportation options, accommodations, recommended tourist spots, and the like based on information input by a user, such as "when, with whom, and where to go." For example, the generation unit analyzes the information input by the user and generates an optimal travel plan. The generation unit can also calculate an optimal route based on map data and generate a video along that route. For example, the generation unit can calculate an efficient route based on map data and generate a video along that route. The learning unit analyzes a user's preferences based on the user's past travel history and ratings. For example, the learning unit can refer to the user's past travel history to learn the user's hobbies and preferences. The learning unit can also estimate a user's emotions and select learning data based on the estimated user emotions. For example, the learning unit can estimate a user's emotions and select learning data based on the estimated user emotions. The linking unit uses an API to automatically make accommodation reservations, restaurant reservations, and the like. For example, the linking unit may use an API to automatically make reservations for accommodations and restaurants. The linking unit may also link with other related services to complete the automatic generation of plans and reservations on a single platform. For example, the linking unit may link with other related services to complete the automatic generation of plans and reservations on a single platform. This enables the automatic travel plan generation system according to the embodiment to automatically generate travel plans, learn user hobbies and preferences, and link with other services.

[0063] The generation unit can automatically generate a tour schedule, transportation methods, accommodations, and recommended tourist spots based on the information of "when, with whom, and where to go" input by the user. The generation unit automatically generates a tour schedule based on the information of "when, with whom, and where to go" input by the user. For example, the generation unit creates an optimal tour schedule by taking into consideration the order of visits and the length of stay. The generation unit can also automatically select transportation methods. For example, the generation unit can consider transportation methods such as trains, buses, and taxis and suggest the optimal transportation method. The generation unit can also automatically select accommodations. For example, the generation unit considers accommodations such as hotels, inns, and guesthouses and suggests the optimal accommodation. The generation unit can also automatically select recommended tourist spots. For example, the generation unit considers popularity and ease of access and suggests recommended tourist spots. In this way, a travel plan can be automatically generated based on the information input by the user.

[0064] The learning unit can analyze the user's preferences based on the user's past travel history and ratings. The learning unit analyzes the user's preferences based on, for example, the user's past travel history. For example, the learning unit collects information such as places the user has visited in the past, the length of stay, and activities, and analyzes the user's preferences. The learning unit can also analyze the user's preferences based on the user's ratings. For example, the learning unit collects information such as the user's satisfaction level and reviews, and analyzes the user's preferences. This makes it possible to analyze the user's preferences based on the user's past travel history and ratings.

[0065] The linking unit can automatically make accommodation reservations or restaurant reservations using the API. The linking unit, for example, uses an API to automatically make accommodation reservations. For example, the linking unit can automatically make accommodation reservations using an accommodation reservation API. The linking unit can also automatically make restaurant reservations. For example, the linking unit can automatically make restaurant reservations using a restaurant reservation API. This makes it possible to automatically make accommodation and restaurant reservations using the API.

[0066] The generation unit can calculate an efficient route based on map data and generate a video that follows that route. The generation unit, for example, calculates an efficient route based on map data. For example, the generation unit calculates an efficient route taking into account the shortest distance and shortest time. The generation unit can also generate a video that follows that route. For example, the generation unit generates a video introducing places to visit and a video guiding the route. This makes it possible to calculate an optimal route based on map data and generate a video that follows that route.

[0067] The collaboration unit collaborates with specific related services to complete automatic plan generation and reservations on a single platform. The collaboration unit, for example, collaborates with specific related services to complete automatic plan generation and reservations on a single platform. For example, the collaboration unit collaborates with services such as transportation, tourist guides, and event tickets to complete automatic plan generation and reservations on a single platform. This allows collaboration with other related services to complete automatic plan generation and reservations on a single platform.

[0068] The generation unit can estimate the user's emotions and adjust the contents of the travel plan based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the contents of the travel plan based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can prioritize suggesting relaxing tourist spots and activities. If the user is excited, the generation unit can also suggest a plan including adventurous or exciting activities. If the user is tired, the generation unit can also suggest a plan including relaxing accommodations and spas. This makes it possible to adjust the contents of the travel plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The generation unit can generate a more personalized plan by referring to the user's past travel history at the time of generation. The generation unit can, for example, generate a more personalized plan by referring to the user's past travel history at the time of generation. For example, the generation unit can suggest similar tourist spots based on places the user has visited in the past. The generation unit can also generate a plan including similar activities based on activities the user has liked in the past. The generation unit can also suggest similar accommodations based on accommodations that the user has rated in the past. In this way, a more personalized plan can be generated by referring to the user's past travel history.

[0070] The generation unit can suggest specific activities taking into account the user's current health condition and physical strength at the time of generation. For example, the generation unit can suggest specific activities taking into account the user's current health condition and physical strength at the time of generation. For example, if the user is in good health, the generation unit can suggest activities such as hiking or cycling. Furthermore, if the user is not confident in their physical strength, the generation unit can suggest a light walk or a sightseeing bus tour. Furthermore, if the user has a specific health problem, the generation unit can suggest an activity that takes into account the problem. In this way, appropriate activities can be suggested taking into account the user's health condition and physical strength.

[0071] The generation unit can generate an efficient plan based on the user's budget at the time of generation. For example, the generation unit generates an efficient plan based on the user's budget at the time of generation. For example, if the user's budget is limited, the generation unit can suggest cost-effective accommodations and transportation options. Furthermore, if the user's budget is abundant, the generation unit can suggest a plan including luxurious accommodations and high-end restaurants. Furthermore, the generation unit can suggest free or low-cost tourist spots according to the user's budget. In this way, an optimal plan can be generated based on the user's budget.

[0072] The generation unit can estimate the user's emotions and prioritize the travel plans based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and prioritize the travel plans based on the estimated user emotions. For example, if the user wants to relax, the generation unit can prioritize relaxing activities. Also, if the user wants to be active, the generation unit can prioritize active activities. Also, if the user wants a cultural experience, the generation unit can prioritize cultural tourist spots. This makes it possible to prioritize the travel plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The generation unit can propose efficient tourist spots by taking into account the user's geographical location information during generation. For example, the generation unit proposes efficient tourist spots by taking into account the user's geographical location information during generation. For example, the generation unit preferentially proposes tourist spots close to the user's current location. The generation unit can also propose tourist spots that are easily accessible from the user's current location. The generation unit can also propose optimal tourist spots by taking into account the travel time from the user's current location. In this way, the generation unit can propose optimal tourist spots by taking into account the user's geographical location information.

[0074] The generation unit can analyze the user's social media activity at the time of generation and suggest related tourist spots. For example, the generation unit can analyze the user's social media activity at the time of generation and suggest related tourist spots. For example, the generation unit can suggest related tourist spots based on places where the user has checked in on social media. The generation unit can also analyze the content of the user's social media posts and suggest tourist spots that the user may be interested in. The generation unit can also suggest related tourist spots based on the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related tourist spots can be suggested.

[0075] The generation unit can customize the content of the plan by reflecting the user's past feedback at the time of generation. The generation unit, for example, customizes the content of the plan by reflecting the user's past feedback at the time of generation. For example, the generation unit preferentially suggests tourist spots that the user has previously rated highly. The generation unit can also exclude tourist spots that the user has previously rated poorly. The generation unit can also fine-tune the content of the plan based on the user's past feedback. In this way, the content of the plan can be customized by reflecting the user's past feedback.

[0076] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit, for example, estimates the user's emotions and selects training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit can prioritize learning data on relaxing tourist spots. Also, if the user is excited, the learning unit can prioritize learning data on adventure and exciting activities. Also, if the user is tired, the learning unit can prioritize learning data on relaxing accommodations and spas. This makes it possible to select training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The learning unit can analyze the user's preferences more accurately by referring to the user's past travel history during learning. The learning unit can analyze the user's preferences more accurately by referring to the user's past travel history during learning. For example, the learning unit analyzes preferences based on places the user has visited in the past. The learning unit can also analyze preferences based on activities the user has liked in the past. The learning unit can also analyze preferences based on accommodations the user has rated in the past. This allows the user's preferences to be analyzed more accurately by referring to the user's past travel history.

[0078] The learning unit can improve the efficiency of the learning algorithm by reflecting the user's evaluation during learning. For example, the learning unit improves the efficiency of the learning algorithm by reflecting the user's evaluation during learning. For example, the learning unit prioritizes learning of data that the user has given a high rating and optimizes the algorithm. The learning unit can also exclude data that the user has given a low rating and optimize the algorithm. The learning unit can also adjust the parameters of the learning algorithm based on the user's evaluation. In this way, the learning algorithm can be optimized by reflecting the user's evaluation.

[0079] The learning unit can collect behavioral data of the user during the trip and analyze the preferences during learning. For example, the learning unit collects behavioral data of the user during the trip and analyzes the preferences during learning. For example, the learning unit collects data on tourist spots visited by the user and analyzes the preferences. The learning unit can also collect data on activities in which the user participated and analyze the preferences. The learning unit can also collect data on facilities where the user stayed and analyze the preferences. In this way, behavioral data of the user during the trip can be collected and the preferences can be analyzed.

[0080] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit, for example, estimates the user's emotions and adjusts the frequency of learning based on the estimated user emotions. For example, the learning unit sets the learning frequency low when the user is relaxed. The learning unit can also set the learning frequency high when the user is excited. The learning unit can also set the learning frequency to medium when the user is tired. This makes it possible to adjust the frequency of learning based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] The learning unit can analyze preferences taking into account the user's geographical location information during learning. For example, the learning unit analyzes preferences taking into account the user's geographical location information during learning. For example, the learning unit prioritizes learning data on tourist spots close to the user's current location. The learning unit can also learn data on tourist spots that are easily accessible from the user's current location. The learning unit can also analyze preferences taking into account travel time from the user's current location. This makes it possible to analyze preferences taking into account the user's geographical location information.

[0082] The learning unit can analyze the user's social media activity and analyze their preferences during learning. For example, the learning unit analyzes the user's social media activity and analyzes their preferences during learning. For example, the learning unit learns data on locations where the user has checked in on social media. The learning unit can also analyze the content of the user's posts on social media and analyze their preferences. The learning unit can also analyze their preferences by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and their preferences can be analyzed.

[0083] The learning unit can update the learning data during learning by reflecting the user's past feedback. For example, the learning unit updates the learning data during learning by reflecting the user's past feedback. For example, the learning unit preferentially learns data that the user has given a high rating and updates the data. The learning unit can also update the data by excluding data that the user has given a low rating. The learning unit can also fine-tune the learning data based on the user's past feedback. In this way, the learning data can be updated by reflecting the user's past feedback.

[0084] The linking unit can estimate the user's emotion and select a service to link with based on the estimated user emotion. For example, the linking unit estimates the user's emotion and selects a service to link with based on the estimated user emotion. For example, if the user wants to relax, the linking unit links with a service that provides relaxing accommodations and spas. Furthermore, if the user wants to stay active, the linking unit can link with a service that provides activities. Furthermore, if the user wants a cultural experience, the linking unit can link with a service that provides cultural tourist spots. In this way, a service to link with based on the user's emotion can be selected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] During collaboration, the collaboration unit can refer to the user's past reservation history and suggest the most suitable service. During collaboration, the collaboration unit, for example, refers to the user's past reservation history and suggest the most suitable service. For example, the collaboration unit suggests similar accommodations based on accommodations that the user has used in the past. The collaboration unit can also suggest similar restaurants based on restaurants that the user has used in the past. The collaboration unit can also suggest the most suitable service based on the user's past reservation history. This makes it possible to suggest the most suitable service by referring to the user's past reservation history.

[0086] The collaboration unit can propose an efficient reservation timing in consideration of the user's current schedule during collaboration. For example, the collaboration unit can propose an efficient reservation timing in consideration of the user's current schedule during collaboration. For example, the collaboration unit can refer to the user's calendar information and propose the optimal reservation timing. The collaboration unit can also propose the optimal reservation timing in accordance with the user's plans. The collaboration unit can also propose the optimal reservation timing based on the user's schedule. This makes it possible to propose the optimal reservation timing in consideration of the user's current schedule.

[0087] The collaboration unit can propose efficient services based on the user's budget during collaboration. For example, the collaboration unit proposes efficient services based on the user's budget during collaboration. For example, when the user's budget is limited, the collaboration unit proposes services with high cost performance. Furthermore, when the user's budget is abundant, the collaboration unit can propose luxurious services. Furthermore, the collaboration unit can propose optimal services according to the user's budget. In this way, optimal services can be proposed based on the user's budget.

[0088] The linking unit can estimate the user's emotion and determine the priority of linked services based on the estimated user's emotion. The linking unit, for example, estimates the user's emotion and determines the priority of linked services based on the estimated user's emotion. For example, if the user wants to relax, the linking unit can preferentially suggest relaxing services. Furthermore, if the user wants to be active, the linking unit can preferentially suggest active services. Furthermore, if the user wants a cultural experience, the linking unit can preferentially suggest cultural services. This makes it possible to determine the priority of linked services based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The collaboration unit can propose efficient services by taking into account the user's geographical location information during collaboration. For example, the collaboration unit proposes efficient services by taking into account the user's geographical location information during collaboration. For example, the collaboration unit preferentially proposes services that are close to the user's current location. The collaboration unit can also propose services that are easy to access from the user's current location. The collaboration unit can also propose optimal services by taking into account the travel time from the user's current location. This makes it possible to propose optimal services by taking into account the user's geographical location information.

[0090] The linking unit can analyze the user's social media activity at the time of linking and suggest related services. For example, the linking unit can analyze the user's social media activity at the time of linking and suggest related services. For example, the linking unit can suggest related services based on the location where the user checked in on social media. The linking unit can also analyze the content of the user's posts on social media and suggest services that the user may be interested in. The linking unit can also suggest related services based on the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related services can be suggested.

[0091] The collaboration unit can customize the services to be linked by reflecting the user's past feedback at the time of collaboration. For example, the collaboration unit customizes the services to be linked by reflecting the user's past feedback at the time of collaboration. For example, the collaboration unit preferentially suggests services that the user has previously rated highly. The collaboration unit can also exclude services that the user has previously rated poorly. The collaboration unit can also fine-tune the services to be linked based on the user's past feedback. In this way, the services to be linked can be customized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the generation unit, learning unit, and linking unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 and analyzes information entered by the user to generate an optimal travel plan. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's preferences based on the user's past travel history and ratings. The linking unit is realized, for example, by the control unit 46A of the smart device 14 and automatically makes reservations for accommodations and restaurants using APIs. === Hard Collateral 1-2 === Each of the multiple elements including the generation unit, learning unit, and linking unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 and analyzes information input by the user to generate an optimal travel plan. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's preferences based on the user's past travel history and ratings. The linking unit is realized, for example, by the control unit 46A of the smart glasses 214 and automatically makes reservations for accommodations and restaurants using APIs. === Hard Collateral 1-3 === Each of the multiple elements including the generation unit, learning unit, and linking unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 and analyzes information input by the user to generate an optimal travel plan. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's preferences based on the user's past travel history and ratings. The linking unit is realized, for example, by the control unit 46A of the headset type terminal 314 and automatically makes reservations for accommodations and restaurants using APIs. === Hard Collateral 1-4 === Each of the multiple elements including the generation unit, learning unit, and linking unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414, and analyzes information input by the user to generate an optimal travel plan. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's preferences based on the user's past travel history and ratings. The linking unit is realized, for example, by the control unit 46A of the robot 414, and automatically makes reservations for accommodations and restaurants using APIs.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The generation unit can change the theme of the travel plan based on the user's current mood. For example, if the user is in a relaxing mood, the generation unit can create a plan centered around relaxing tourist spots and activities. If the user is seeking adventure, the generation unit can also suggest a plan that includes adventure and exciting activities. Furthermore, if the user is seeking a cultural experience, the generation unit can also suggest a plan that includes museums and historical tourist spots. This makes it possible to provide a themed travel plan that matches the user's mood.

[0094] The generation unit can generate a plan taking into account not only the user's past travel history but also the user's current lifestyle. For example, if the user has been busy recently, the generation unit can suggest a relaxing travel plan. Also, if the user has started a new hobby, the generation unit can suggest a plan that includes tourist spots and activities related to that hobby. Furthermore, if the user plans to participate in a specific event, the generation unit can generate a travel plan tailored to that event. This makes it possible to provide a travel plan that suits the user's current lifestyle.

[0095] The learning unit can estimate the user's emotions and incorporate the user's favorite music and videos into the travel plan based on the estimated emotions. For example, if the user wants to relax, the learning unit can suggest relaxing music and videos. If the user is excited, the learning unit can suggest energetic music or action movies. Furthermore, if the user is looking for an emotional experience, the learning unit can suggest emotional movies or documentaries. This makes it possible to provide entertainment that matches the user's emotions.

[0096] The linking unit can propose optimal travel plans by taking into account not only the user's past travel history but also the travel history of the user's friends and family. For example, it can prioritize tourist spots that have been highly rated by the user's friends. It can also suggest accommodations that are favored by the user's family. Furthermore, it can suggest similar activities based on activities in which the user's friends and family have participated. This makes it possible to provide travel plans that take into account the travel history of the user's friends and family.

[0097] The generation unit can estimate the user's emotions and suggest surprise events during the trip based on the estimated emotions. For example, if the user wants to relax, the generation unit can suggest a relaxing surprise event. If the user is excited, the generation unit can suggest an adventurous or exciting surprise event. Furthermore, if the user wants to be moved, the generation unit can suggest an inspiring surprise event. In this way, surprise events can be provided that match the user's emotions.

[0098] The linking unit can propose optimal travel plans taking into account the user's current health condition. For example, if the user is in good health, it can propose plans that include active activities. If the user is not in good health, it can also suggest tourist spots and accommodations where the user can relax. Furthermore, if the user has a specific health problem, it can also propose travel plans that take that problem into consideration. This makes it possible to provide travel plans that are suited to the user's health condition.

[0099] The generation unit can estimate the user's emotions and adjust the meal plan for the trip based on the estimated emotions. For example, if the user wants to relax, the generation unit can suggest restaurants and cafes where they can relax. If the user is excited, the generation unit can suggest restaurants and bars with an energetic atmosphere. Furthermore, if the user is looking for an emotional experience, the generation unit can suggest restaurants that offer an emotional dining experience. In this way, a meal plan can be provided that matches the user's emotions.

[0100] The learning unit can propose optimal travel plans by taking into consideration not only the user's past travel history but also the user's occupation and lifestyle. For example, if the user is a businessman, it can propose business-related tourist spots and accommodations. If the user is an artist, it can also propose art galleries and creative activities. Furthermore, if the user is an outdoor enthusiast, it can also propose activities to enjoy nature. This makes it possible to provide travel plans that suit the user's occupation and lifestyle.

[0101] The linking unit can estimate the user's emotions and provide emergency response plans for the user's trip based on the estimated emotions. For example, if the user is feeling anxious, the linking unit can suggest support services that will reassure the user. If the user is feeling stressed, the linking unit can provide an emergency response plan that will relax the user. Furthermore, if the user is excited, the linking unit can provide an energetic emergency response plan. In this way, an emergency response plan can be provided that matches the user's emotions.

[0102] The generation unit can propose optimal travel plans by taking into consideration not only the user's past travel history but also the user's future goals and dreams. For example, if the user has a future goal of visiting a specific country, a travel plan including that country can be proposed. Also, if the user wants to experience a specific activity, a plan including that activity can be proposed. Furthermore, if the user wants to learn about a specific culture, tourist spots related to that culture can be proposed. In this way, it is possible to provide travel plans that match the user's future goals and dreams.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The generator automatically generates a travel schedule, transportation options, accommodations, recommended tourist spots, and other information based on the user's input information about "when, with whom, and where to go." For example, the generator analyzes the information entered by the user and generates an optimal travel plan. The generator can also calculate the optimal route based on map data and generate a video that follows that route. Step 2: The learning unit analyzes the user's preferences based on the user's past travel history and ratings. For example, the learning unit refers to the user's past travel history to learn the user's hobbies and preferences. The learning unit can also estimate the user's emotions and select learning data based on the estimated user emotions. Step 3: The integration unit uses the API to automatically make accommodation and restaurant reservations. For example, the integration unit uses the API to automatically make accommodation and restaurant reservations. The integration unit can also integrate with other related services to complete the automatic generation of plans and reservations on a single platform.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 generation unit that automatically generates a travel plan; a learning unit that learns the user's hobbies or preferences based on the plan generated by the generation unit; a linking unit that links with a specific service based on the information learned by the learning unit. A system characterized by:

2. The generation unit Based on the information entered by the user, the system automatically generates travel schedules, transportation options, accommodations, and recommended tourist spots.

2. The system of claim 1.

3. The learning unit Analyze user preferences based on their past travel history and ratings 2. The system of claim 1.

4. The linking unit is Use APIs to automatically make accommodation or restaurant reservations 2. The system of claim 1.

5. The generation unit Calculates efficient routes based on map data and generates videos along those routes 2. The system of claim 1.

6. The linking unit is Link with specific related services to automate plan generation and booking on a single platform 2. The system of claim 1.

7. The generation unit Estimate user emotions and adjust travel plans based on the estimated user emotions 2. The system of claim 1.

8. The generation unit At the time of generation, the plan references the user's past travel history to generate a more personalized plan.

2. The system of claim 1.

Images