system

Abstract

The system according to the embodiment aims to provide travel plans that meet individual preferences and promote the diversification of travel dates and destinations according to reservation status. [Solution] The system according to the embodiment includes a reception unit, a generation unit, a presentation unit, a monitoring unit, and a distribution unit. The reception unit inputs the user's desired travel conditions. The generation unit analyzes the information received by the reception unit and generates a travel plan. The presentation unit presents the plan generated by the generation unit to the user. The monitoring unit monitors reservation status in real time. The distribution unit promotes distribution of travel dates and travel destinations based on the reservation status monitored by the monitoring unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to provide integrated travel services that meet individual preferences, and that travel dates and destinations are not adequately distributed according to reservation status.

[0005] The system according to the embodiment aims to provide travel plans that meet individual preferences and promote the diversification of travel dates and destinations according to reservation status. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a presentation unit, a monitoring unit, and a distribution unit. The reception unit inputs the user's desired travel conditions. The generation unit analyzes the information received by the reception unit and generates a travel plan. The presentation unit presents the plan generated by the generation unit to the user. The monitoring unit monitors the reservation status in real time. The distribution unit promotes distribution of travel dates and travel destinations based on the reservation status monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide travel plans that meet individual preferences and promote the diversification of travel dates and destinations according to the reservation situation. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) A travel planning system according to an embodiment of the present invention provides a service that integrates travel planning based on individual preferences. In this travel planning system, a user inputs desired travel conditions, and AI analyzes these conditions to generate an optimal travel plan. The generated plans are presented to the user, who then selects the most suitable plan. Furthermore, AI monitors reservation status in real time and encourages users to diversify their travel dates and destinations. This mechanism allows users to use integrated travel services at low cost, simplifying travel planning. Furthermore, promoting diversification of travel dates and destinations contributes to solving social issues. For example, a user inputs desired travel conditions, such as departure point, destination, travel itinerary, budget, and preferred activities. Next, AI analyzes these conditions and generates an optimal travel plan that includes reservations for transportation, accommodation, and activities at the destination. The generated plan is presented to the user, who then selects the most suitable plan. Furthermore, AI monitors reservation status in real time and suggests alternative destinations and dates if a specific destination or date is crowded. This allows users to use integrated travel services at low cost, simplifying travel planning. Furthermore, by promoting the diversification of travel dates and destinations, it also contributes to solving social issues. This allows the travel planning system to process users' desired travel conditions in an integrated manner and provide optimal travel plans.

[0029] A travel planning system according to an embodiment includes a reception unit, a generation unit, a presentation unit, a monitoring unit, and a distribution unit. The reception unit receives a user's desired travel conditions. The user's desired travel conditions include, but are not limited to, a departure point, a destination, a travel itinerary, a budget, and preferred activities. The reception unit, for example, allows the user to input a city name as the departure point and a tourist spot name as the destination. The reception unit also allows the user to input a departure date and a return date as the travel itinerary and a total budget as the budget. The reception unit also allows the user to select sightseeing or sports as the preferred activities. The generation unit analyzes the information received by the reception unit and generates an optimal travel plan. The generation unit generates a travel plan that includes, for example, reservations for transportation, accommodations, and activities at the travel destination. The generation unit, for example, makes reservations for flights from the departure point to the destination entered by the user and reservations for accommodations at the destination. The generation unit can also make reservations for sightseeing tours at the destination. The presentation unit presents the plan generated by the generation unit to the user, allowing the user to select one. The presentation unit, for example, displays the generated plan on the user's smartphone, allowing the user to select one. The presentation unit can also display the generated plan on the user's computer, allowing the user to select one. The monitoring unit monitors reservation status in real time. The monitoring unit, for example, monitors transportation reservation status and accommodation reservation status in real time. The monitoring unit, for example, monitors flight reservation status and can suggest alternative flights if a flight is crowded. The distribution unit promotes distribution of travel dates and travel destinations based on the reservation status monitored by the monitoring unit. For example, if a specific travel destination or date is crowded, the distribution unit can suggest alternative travel destinations or dates. For example, the distribution unit can suggest alternative tourist destinations instead of crowded tourist destinations. The distribution unit can also suggest alternative dates instead of crowded dates. As a result, the travel planning system according to the embodiment can integrate and process the user's desired travel conditions and provide an optimal travel plan.

[0030] The reception unit can accept information such as the user's departure point, destination, travel itinerary, budget, and preferred activities. For example, the reception unit allows the user to input a city name as the departure point and a tourist spot name as the destination. The reception unit also allows the user to input a departure date and return date as the travel itinerary and a total budget as the budget. Furthermore, the reception unit allows the user to select sightseeing or sports as the preferred activities. This allows the user to accept detailed travel requirements. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the information entered by the user into AI, which can analyze the information and generate an optimal travel plan.

[0031] The generation unit can analyze the information received by the reception unit and generate a travel plan that includes reservations for transportation, accommodation, and activities at the travel destination. The generation unit, for example, makes reservations for flights from the departure point entered by the user to the destination and reservations for accommodation at the destination. The generation unit can also make reservations for sightseeing tours at the destination. The generation unit, for example, generates an optimal travel plan based on information entered by the user. The generation unit generates an optimal plan by taking into account, for example, the reservation status of flights and accommodations. The generation unit can, for example, generate a plan that includes activities desired by the user. This allows a comprehensive travel plan to be generated based on the user's desired travel conditions. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input information entered by the user into AI, which analyzes the information and generates an optimal travel plan.

[0032] The presentation unit presents the plan generated by the generation unit to the user, allowing the user to select one. The presentation unit, for example, displays the generated plan on the user's smartphone, allowing the user to select one. The presentation unit can also display the generated plan on the user's personal computer, allowing the user to select one. The presentation unit, for example, presents the generated plan to the user, allowing the user to select one. This allows the user to select the generated plan. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the generated plan to AI, which analyzes the plan and presents the optimal plan.

[0033] The monitoring unit can monitor the reservation status in real time. The monitoring unit, for example, monitors the reservation status of transportation means and the reservation status of accommodations in real time. The monitoring unit, for example, monitors the reservation status of airplanes and can suggest other flights if the flight is crowded. The monitoring unit, for example, monitors the reservation status of accommodations and can suggest other accommodations if the flight is crowded. This makes it possible to monitor the reservation status in real time. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the reservation status into AI, which can analyze the reservation status and make optimal suggestions.

[0034] The distribution unit can suggest other travel destinations or itineraries if a specific travel destination or itinerary is crowded based on the reservation status monitored by the monitoring unit. For example, if a specific travel destination or itinerary is crowded, the distribution unit can suggest other travel destinations or itineraries. For example, the distribution unit can suggest other tourist destinations instead of crowded tourist destinations. The distribution unit can also suggest other itineraries instead of crowded itineraries. This makes it possible to suggest other travel destinations or itineraries to avoid crowding. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the reservation status monitored by the monitoring unit into AI, which can analyze the reservation status and make optimal suggestions.

[0035] The reception unit can analyze the user's past travel history and select the optimal input method. For example, the reception unit can automatically display travel destinations that the user has frequently visited in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest travel destinations related to specific seasons or events from the user's past travel history. This makes it possible to select the optimal input method based on the user's past travel history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past travel history into AI, which can analyze the history and suggest the optimal input method.

[0036] When the user inputs desired travel conditions, the reception unit can filter the results based on the user's current living situation and areas of interest. The reception unit can, for example, suggest related travel destinations based on activities the user has recently taken an interest in. The reception unit can also suggest optimal travel itineraries based on the user's current living situation (such as how busy the user is at work and their family structure). The reception unit can also filter activities at travel destinations based on the user's areas of interest (such as history, nature, and art). This makes it possible to filter desired travel conditions based on the user's current living situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, AI, for example. For example, the reception unit can input the user's current living situation and areas of interest into AI, which can then analyze the information and suggest optimal desired travel conditions.

[0037] When inputting desired travel conditions, the reception unit can prioritize inputting highly relevant conditions taking into account the user's geographical location information. For example, the reception unit can prioritize suggesting travel destinations close to the user's current location. The reception unit can also automatically set an optimal departure point based on the user's geographical location information. The reception unit can also suggest transportation options taking into account the user's geographical location information. This allows highly relevant conditions to be prioritized input based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI, which can analyze the information and suggest optimal conditions.

[0038] When inputting desired travel conditions, the reception unit can analyze the user's social media activity and input related conditions. For example, the reception unit can suggest related travel destinations based on travel destinations shared by the user on social media. The reception unit can also analyze activities of interest from the user's social media activity and reflect them in the travel plan. The reception unit can also suggest potential travel destinations based on posts by the user's friends on social media. This makes it possible to input related conditions based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI, which can analyze the information and suggest optimal conditions.

[0039] When generating a travel plan, the generation unit can generate the plan by referring to the user's past travel history. The generation unit can generate a related travel plan, for example, based on travel destinations the user has visited in the past. The generation unit can also suggest routes that avoid crowds based on the user's past travel history. The generation unit can also analyze the user's past travel history and generate the most efficient travel plan. This makes it possible to generate an optimal travel plan based on the user's past travel history. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input the user's past travel history into AI, which can analyze the history and suggest an optimal plan.

[0040] When generating a travel plan, the generation unit can customize the plan based on the user's current lifestyle. For example, if the user is busy with work, the generation unit can generate a travel plan that allows the user to refresh in a short period of time. Furthermore, if the user is traveling with family, the generation unit can generate a plan that includes family activities. Furthermore, if the user is seeking a healthy lifestyle, the generation unit can generate a plan that includes health-conscious activities. This allows the travel plan to be customized based on the user's current lifestyle. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's current lifestyle into AI, which can analyze the information and propose an optimal plan.

[0041] The generation unit can generate a travel plan taking into account the user's geographical location information. For example, the generation unit can prioritize suggesting travel destinations close to the user's current location. The generation unit can also automatically set an optimal departure point based on the user's geographical location information. The generation unit can also suggest transportation options taking into account the user's geographical location information. This makes it possible to generate an optimal travel plan based on the user's geographical location information. Some or all of the above-described processing in the generation unit can be performed using, or without, AI, for example. For example, the generation unit can input the user's geographical location information into AI, which can analyze the information and suggest an optimal plan.

[0042] When generating a travel plan, the generation unit can analyze the user's social media activity and generate a related plan. The generation unit can generate a related travel plan based on, for example, travel destinations shared by the user on social media. The generation unit can also analyze activities of interest from the user's social media activity and reflect them in the travel plan. The generation unit can also suggest potential travel destinations by referring to posts by the user's friends on social media. In this way, a related travel plan can be generated based on the user's social media activity. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's social media activity into AI, which can analyze the information and suggest an optimal plan.

[0043] When presenting a plan, the presentation unit can select the optimal presentation method by referring to the user's past selection history. For example, the presentation unit can preferentially display related plans based on plans previously selected by the user. The presentation unit can also suggest plans including preferred activities from the user's past selection history. The presentation unit can also analyze the user's past selection history and select the most efficient presentation method. This makes it possible to select the optimal presentation method based on the user's past selection history. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's past selection history into AI, which can analyze the history and suggest the optimal presentation method.

[0044] When presenting a plan, the presentation unit can customize the presented content based on the user's current lifestyle. For example, if the user is busy with work, the presentation unit can prioritize displaying plans that allow the user to refresh in a short period of time. Furthermore, if the user is traveling with family, the presentation unit can prioritize displaying plans that include family-friendly activities. Furthermore, if the user is seeking a healthy lifestyle, the presentation unit can prioritize displaying plans that include health-conscious activities. This allows the presented content to be customized based on the user's current lifestyle. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's current lifestyle into AI, which analyzes the information and suggests optimal presented content.

[0045] When presenting a plan, the presentation unit can select the optimal presentation method by taking into account the user's device information. For example, if the user is using a smartphone, the presentation unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the presentation unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the presentation unit can also provide a simple, highly visible display method. This makes it possible to select the optimal presentation method based on the user's device information. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's device information into AI, which analyzes the information and proposes the optimal presentation method.

[0046] When presenting a plan, the presentation unit can analyze the user's social media activity and present relevant plans. For example, the presentation unit can present relevant plans based on travel destinations shared by the user on social media. The presentation unit can also analyze activities of interest from the user's social media activity and reflect them in the plan. The presentation unit can also present potential travel destinations based on posts by the user's friends on social media. This makes it possible to present relevant plans based on the user's social media activity. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's social media activity into AI, which can analyze the information and propose an optimal plan.

[0047] During monitoring, the monitoring unit can optimize the monitoring algorithm by referring to past reservation data. For example, the monitoring unit can predict busy times and locations based on past reservation data and adjust the monitoring algorithm. The monitoring unit can also extract specific patterns from past reservation data and reflect them in the monitoring algorithm. The monitoring unit can also analyze past reservation data and introduce the most efficient monitoring method. This makes it possible to optimize the monitoring algorithm based on past reservation data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past reservation data into AI, which analyzes the data and proposes an optimal monitoring algorithm.

[0048] During monitoring, the monitoring unit can customize the monitoring content based on the user's current lifestyle. For example, if the user is busy with work, the monitoring unit can prioritize monitoring a travel plan that allows for short-term refreshment. Furthermore, if the user is traveling with family, the monitoring unit can prioritize monitoring a plan that includes family activities. Furthermore, if the user seeks a healthy lifestyle, the monitoring unit can prioritize monitoring a plan that includes health-conscious activities. This allows the monitoring content to be customized based on the user's current lifestyle. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's current lifestyle into AI, which can analyze the information and suggest optimal monitoring content.

[0049] During monitoring, the monitoring unit can optimize the monitoring content by taking into account the user's geographical location information. For example, the monitoring unit prioritizes monitoring of travel destinations that are close to the user's current location. The monitoring unit can also provide an optimal monitoring method based on the user's geographical location information. The monitoring unit can also optimize monitoring of transportation means by taking into account the user's geographical location information. This allows the monitoring content to be optimized based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information into AI, which analyzes the information and suggests optimal monitoring content.

[0050] During monitoring, the monitoring unit can analyze the user's social media activity and provide relevant monitoring content. The monitoring unit can provide relevant monitoring content based on, for example, travel destinations shared by the user on social media. The monitoring unit can also analyze activities of interest from the user's social media activity and reflect these in the monitoring content. The monitoring unit can also provide travel destination monitoring content based on posts by the user's friends on social media. This makes it possible to provide relevant monitoring content based on the user's social media activity. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input the user's social media activity into AI, which can analyze the information and suggest optimal monitoring content.

[0051] During distribution, the distribution unit can optimize the distributed algorithm by referring to past distributed data. For example, the distribution unit predicts times and locations of congestion based on past distributed data and adjusts the distributed algorithm. The distribution unit can also extract specific patterns from past distributed data and reflect them in the distributed algorithm. The distribution unit can also analyze past distributed data and introduce the most efficient distribution method. This makes it possible to optimize the distributed algorithm based on past distributed data. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input past distributed data into AI, which analyzes the data and proposes the optimal distributed algorithm.

[0052] The distribution unit can customize the distribution content based on the user's current living situation during distribution. For example, if the user is busy with work, the distribution unit can prioritize distribution of travel plans that allow the user to refresh in a short period of time. Furthermore, if the user is traveling with family, the distribution unit can prioritize distribution of plans that include family activities. Furthermore, if the user is seeking a healthy lifestyle, the distribution unit can prioritize distribution of plans that include health-conscious activities. This allows the distribution content to be customized based on the user's current living situation. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the user's current living situation into AI, which can analyze the information and suggest optimal distribution content.

[0053] During distribution, the distribution unit can optimize distribution content taking into account the user's geographical location information. For example, the distribution unit prioritizes distribution to travel destinations close to the user's current location. The distribution unit can also provide an optimal distribution method based on the user's geographical location information. The distribution unit can also optimize distribution of transportation means taking into account the user's geographical location information. This makes it possible to optimize distribution content based on the user's geographical location information. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the user's geographical location information into AI, which can analyze the information and propose optimal distribution content.

[0054] The distribution unit can analyze the user's social media activity during distribution and provide related distributed content. For example, the distribution unit can provide related distributed content based on travel destinations shared by the user on social media. The distribution unit can also analyze activities of interest from the user's social media activity and reflect them in the distributed content. The distribution unit can also provide distributed content about travel destinations by referring to posts by the user's friends on social media. This makes it possible to provide related distributed content based on the user's social media activity. Some or all of the above-described processing in the distribution unit can be performed using, for example, AI, or can be performed without using AI. For example, the distribution unit can input the user's social media activity into AI, which can analyze the information and suggest optimal distributed content.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The travel planning system may further include a health management unit that monitors the user's health condition. The health management unit collects the user's health data (e.g., heart rate, blood pressure, number of steps, etc.) and reflects it in the travel plan. For example, if the user has high blood pressure, the health management unit can suggest travel destinations and activities that will help them relax. Also, if the user is not getting enough exercise, the health management unit can generate a plan that includes activities such as walking tours and hiking. This makes it possible to provide the user with an optimal travel plan based on their health condition.

[0057] The travel planning system may further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit analyzes the user's past travel history and social media posts to identify the user's hobbies and interests. For example, if the user is interested in history, the hobby analysis unit may suggest a travel plan that includes historical tourist sites and museums. Also, if the user likes outdoor activities, the hobby analysis unit may generate a plan that includes camping and trekking. This makes it possible to provide a travel plan based on the user's hobbies and interests.

[0058] The travel planning system may further include a safety management unit to ensure the safety of the user during the trip. The safety management unit collects security and weather information for the travel destination in real time and provides it to the user. For example, if security at the travel destination deteriorates, the safety management unit can suggest an alternative travel destination. Also, if the weather worsens, the safety management unit can suggest indoor activities that can be enjoyed. This ensures the user's safety during the trip and allows them to enjoy their trip with peace of mind.

[0059] The travel planning system may further include a communication support unit that supports communication during the user's trip. The communication support unit provides information about the language and culture of the travel destination, helping the user to communicate smoothly with local people. For example, the communication support unit may provide a guide to learning basic greetings and phrases at the travel destination. It may also provide information about the local culture and customs and give advice to the user to avoid cultural misunderstandings. This allows the user to communicate smoothly at the travel destination.

[0060] The travel planning system may further include an entertainment provider that provides entertainment for the user during their trip. The entertainment provider provides entertainment content such as movies, music, and books according to the user's preferences. For example, the entertainment provider can suggest movies and music that the user can enjoy during a long flight. It can also provide travel-related books, such as tourist guides and history books for the travel destination. This allows the user to spend their time during their trip meaningfully.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit inputs the user's desired travel conditions. The user's desired travel conditions include the departure point, destination, travel itinerary, budget, and preferred activities. For example, the user can input the name of a city as the departure point and the name of a tourist spot as the destination. The user can also input the departure date and return date as the travel itinerary and the total budget as the budget. Furthermore, the user can select sightseeing or sports as the preferred activities. Step 2: The generation unit analyzes the information received by the reception unit and generates an optimal travel plan. The generation unit generates a travel plan that includes reservations for transportation, accommodation, and activities at the travel destination. For example, the generation unit makes reservations for flights from the departure point entered by the user to the destination, and for accommodation at the destination. It can also make reservations for sightseeing tours at the destination. Step 3: The presentation unit presents the plans generated by the generation unit to the user, and the user selects one. The presentation unit displays the generated plans on the user's smartphone or computer, allowing the user to select one. Step 4: The monitoring unit monitors the reservation status in real time. The monitoring unit monitors the reservation status of transportation and accommodation in real time. For example, it can monitor the reservation status of flights and suggest alternative flights if they are crowded. Step 5: The distribution unit promotes distribution of travel dates and travel destinations based on the reservation status monitored by the monitoring unit. If a specific travel destination or date is crowded, the distribution unit suggests other travel destinations or dates. For example, instead of a crowded tourist destination, the distribution unit can suggest other tourist destinations. Also, instead of a crowded date, the distribution unit can suggest other dates.

[0063] (Example 2) A travel planning system according to an embodiment of the present invention provides a service that integrates travel planning based on individual preferences. In this travel planning system, a user inputs desired travel conditions, and AI analyzes these conditions to generate an optimal travel plan. The generated plans are presented to the user, who then selects the most suitable plan. Furthermore, AI monitors reservation status in real time and encourages users to diversify their travel dates and destinations. This mechanism allows users to use integrated travel services at low cost, simplifying travel planning. Furthermore, promoting diversification of travel dates and destinations contributes to solving social issues. For example, a user inputs desired travel conditions, such as departure point, destination, travel itinerary, budget, and preferred activities. Next, AI analyzes these conditions and generates an optimal travel plan that includes reservations for transportation, accommodation, and activities at the destination. The generated plan is presented to the user, who then selects the most suitable plan. Furthermore, AI monitors reservation status in real time and suggests alternative destinations and dates if a specific destination or date is crowded. This allows users to use integrated travel services at low cost, simplifying travel planning. Furthermore, by promoting the diversification of travel dates and destinations, it also contributes to solving social issues. This allows the travel planning system to process users' desired travel conditions in an integrated manner and provide optimal travel plans.

[0064] A travel planning system according to an embodiment includes a reception unit, a generation unit, a presentation unit, a monitoring unit, and a distribution unit. The reception unit receives a user's desired travel conditions. The user's desired travel conditions include, but are not limited to, a departure point, a destination, a travel itinerary, a budget, and preferred activities. The reception unit, for example, allows the user to input a city name as the departure point and a tourist spot name as the destination. The reception unit also allows the user to input a departure date and a return date as the travel itinerary and a total budget as the budget. The reception unit also allows the user to select sightseeing or sports as the preferred activities. The generation unit analyzes the information received by the reception unit and generates an optimal travel plan. The generation unit generates a travel plan that includes, for example, reservations for transportation, accommodations, and activities at the travel destination. The generation unit, for example, makes reservations for flights from the departure point to the destination entered by the user and reservations for accommodations at the destination. The generation unit can also make reservations for sightseeing tours at the destination. The presentation unit presents the plan generated by the generation unit to the user, allowing the user to select one. The presentation unit, for example, displays the generated plan on the user's smartphone, allowing the user to select one. The presentation unit can also display the generated plan on the user's computer, allowing the user to select one. The monitoring unit monitors reservation status in real time. The monitoring unit, for example, monitors transportation reservation status and accommodation reservation status in real time. The monitoring unit, for example, monitors flight reservation status and can suggest alternative flights if a flight is crowded. The distribution unit promotes distribution of travel dates and travel destinations based on the reservation status monitored by the monitoring unit. For example, if a specific travel destination or date is crowded, the distribution unit can suggest alternative travel destinations or dates. For example, the distribution unit can suggest alternative tourist destinations instead of crowded tourist destinations. The distribution unit can also suggest alternative dates instead of crowded dates. As a result, the travel planning system according to the embodiment can integrate and process the user's desired travel conditions and provide an optimal travel plan.

[0065] The reception unit can accept information such as the user's departure point, destination, travel itinerary, budget, and preferred activities. For example, the reception unit allows the user to input a city name as the departure point and a tourist spot name as the destination. The reception unit also allows the user to input a departure date and return date as the travel itinerary and a total budget as the budget. Furthermore, the reception unit allows the user to select sightseeing or sports as the preferred activities. This allows the user to accept detailed travel requirements. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the information entered by the user into AI, which can analyze the information and generate an optimal travel plan.

[0066] The generation unit can analyze the information received by the reception unit and generate a travel plan that includes reservations for transportation, accommodation, and activities at the travel destination. The generation unit, for example, makes reservations for flights from the departure point entered by the user to the destination and reservations for accommodation at the destination. The generation unit can also make reservations for sightseeing tours at the destination. The generation unit, for example, generates an optimal travel plan based on information entered by the user. The generation unit generates an optimal plan by taking into account, for example, the reservation status of flights and accommodations. The generation unit can, for example, generate a plan that includes activities desired by the user. This allows a comprehensive travel plan to be generated based on the user's desired travel conditions. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input information entered by the user into AI, which analyzes the information and generates an optimal travel plan.

[0067] The presentation unit presents the plan generated by the generation unit to the user, allowing the user to select one. The presentation unit, for example, displays the generated plan on the user's smartphone, allowing the user to select one. The presentation unit can also display the generated plan on the user's personal computer, allowing the user to select one. The presentation unit, for example, presents the generated plan to the user, allowing the user to select one. This allows the user to select the generated plan. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the generated plan to AI, which analyzes the plan and presents the optimal plan.

[0068] The monitoring unit can monitor the reservation status in real time. The monitoring unit, for example, monitors the reservation status of transportation means and the reservation status of accommodations in real time. The monitoring unit, for example, monitors the reservation status of airplanes and can suggest other flights if the flight is crowded. The monitoring unit, for example, monitors the reservation status of accommodations and can suggest other accommodations if the flight is crowded. This makes it possible to monitor the reservation status in real time. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the reservation status into AI, which can analyze the reservation status and make optimal suggestions.

[0069] The distribution unit can suggest other travel destinations or itineraries if a specific travel destination or itinerary is crowded based on the reservation status monitored by the monitoring unit. For example, if a specific travel destination or itinerary is crowded, the distribution unit can suggest other travel destinations or itineraries. For example, the distribution unit can suggest other tourist destinations instead of crowded tourist destinations. The distribution unit can also suggest other itineraries instead of crowded itineraries. This makes it possible to suggest other travel destinations or itineraries to avoid crowding. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the reservation status monitored by the monitoring unit into AI, which can analyze the reservation status and make optimal suggestions.

[0070] The reception unit can estimate the user's emotions and adjust the input method for desired travel conditions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of desired travel conditions. This allows the input method for desired travel conditions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotions into an AI, which can analyze the emotions and suggest the optimal input method.

[0071] The reception unit can analyze the user's past travel history and select the optimal input method. For example, the reception unit can automatically display travel destinations that the user has frequently visited in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest travel destinations related to specific seasons or events from the user's past travel history. This makes it possible to select the optimal input method based on the user's past travel history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past travel history into AI, which can analyze the history and suggest the optimal input method.

[0072] When the user inputs desired travel conditions, the reception unit can filter the results based on the user's current living situation and areas of interest. The reception unit can, for example, suggest related travel destinations based on activities the user has recently taken an interest in. The reception unit can also suggest optimal travel itineraries based on the user's current living situation (such as how busy the user is at work and their family structure). The reception unit can also filter activities at travel destinations based on the user's areas of interest (such as history, nature, and art). This makes it possible to filter desired travel conditions based on the user's current living situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, AI, for example. For example, the reception unit can input the user's current living situation and areas of interest into AI, which can then analyze the information and suggest optimal desired travel conditions.

[0073] The reception unit can estimate the user's emotions and determine the priority of the desired travel conditions to be input based on the estimated user emotions. For example, when the user is relaxed, the reception unit can prioritize input of detailed desired travel conditions. Furthermore, when the user is in a hurry, the reception unit can prioritize input of important conditions (such as departure point, destination, and schedule). Furthermore, when the user is stressed, the reception unit can prioritize input of simple conditions (such as budget and favorite activities). This allows the priority of the desired travel conditions to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotions into an AI, which can analyze the emotions and suggest optimal priorities.

[0074] When inputting desired travel conditions, the reception unit can prioritize inputting highly relevant conditions taking into account the user's geographical location information. For example, the reception unit can prioritize suggesting travel destinations close to the user's current location. The reception unit can also automatically set an optimal departure point based on the user's geographical location information. The reception unit can also suggest transportation options taking into account the user's geographical location information. This allows highly relevant conditions to be prioritized input based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI, which can analyze the information and suggest optimal conditions.

[0075] When inputting desired travel conditions, the reception unit can analyze the user's social media activity and input related conditions. For example, the reception unit can suggest related travel destinations based on travel destinations shared by the user on social media. The reception unit can also analyze activities of interest from the user's social media activity and reflect them in the travel plan. The reception unit can also suggest potential travel destinations based on posts by the user's friends on social media. This makes it possible to input related conditions based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI, which can analyze the information and suggest optimal conditions.

[0076] The generation unit can estimate the user's emotions and adjust the itinerary generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an itinerary that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate an itinerary that emphasizes the shortest route. If the user is excited, the generation unit can generate an itinerary that includes visually stimulating activities. This allows the itinerary generation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotions into an AI, which can analyze the emotions and suggest an optimal generation method.

[0077] When generating a travel plan, the generation unit can generate the plan by referring to the user's past travel history. The generation unit can generate a related travel plan, for example, based on travel destinations the user has visited in the past. The generation unit can also suggest routes that avoid crowds based on the user's past travel history. The generation unit can also analyze the user's past travel history and generate the most efficient travel plan. This makes it possible to generate an optimal travel plan based on the user's past travel history. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input the user's past travel history into AI, which can analyze the history and suggest an optimal plan.

[0078] When generating a travel plan, the generation unit can customize the plan based on the user's current lifestyle. For example, if the user is busy with work, the generation unit can generate a travel plan that allows the user to refresh in a short period of time. Furthermore, if the user is traveling with family, the generation unit can generate a plan that includes family activities. Furthermore, if the user is seeking a healthy lifestyle, the generation unit can generate a plan that includes health-conscious activities. This allows the travel plan to be customized based on the user's current lifestyle. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's current lifestyle into AI, which can analyze the information and propose an optimal plan.

[0079] The generation unit can estimate the user's emotions and determine the priority of the travel plans to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation unit can prioritize generating detailed travel plans. Furthermore, if the user is in a hurry, the generation unit can prioritize generating important elements (such as transportation and accommodations). Furthermore, if the user is stressed, the generation unit can prioritize generating simple travel plans. This allows the prioritization of travel plans to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotions into an AI, which can analyze the emotions and suggest optimal priorities.

[0080] The generation unit can generate a travel plan taking into account the user's geographical location information. For example, the generation unit can prioritize suggesting travel destinations close to the user's current location. The generation unit can also automatically set an optimal departure point based on the user's geographical location information. The generation unit can also suggest transportation options taking into account the user's geographical location information. This makes it possible to generate an optimal travel plan based on the user's geographical location information. Some or all of the above-described processing in the generation unit can be performed using, or without, AI, for example. For example, the generation unit can input the user's geographical location information into AI, which can analyze the information and suggest an optimal plan.

[0081] When generating a travel plan, the generation unit can analyze the user's social media activity and generate a related plan. The generation unit can generate a related travel plan based on, for example, travel destinations shared by the user on social media. The generation unit can also analyze activities of interest from the user's social media activity and reflect them in the travel plan. The generation unit can also suggest potential travel destinations by referring to posts by the user's friends on social media. In this way, a related travel plan can be generated based on the user's social media activity. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's social media activity into AI, which can analyze the information and suggest an optimal plan.

[0082] The presentation unit can estimate the user's emotions and adjust the plan presentation method based on the estimated user emotions. For example, if the user is nervous, the presentation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the presentation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the presentation unit can provide a display method that focuses on the main points. This allows the plan presentation method to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the presentation unit can input the user's emotions into an AI, which can analyze the emotions and suggest an optimal presentation method.

[0083] When presenting a plan, the presentation unit can select the optimal presentation method by referring to the user's past selection history. For example, the presentation unit can preferentially display related plans based on plans previously selected by the user. The presentation unit can also suggest plans including preferred activities from the user's past selection history. The presentation unit can also analyze the user's past selection history and select the most efficient presentation method. This makes it possible to select the optimal presentation method based on the user's past selection history. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's past selection history into AI, which can analyze the history and suggest the optimal presentation method.

[0084] When presenting a plan, the presentation unit can customize the presented content based on the user's current lifestyle. For example, if the user is busy with work, the presentation unit can prioritize displaying plans that allow the user to refresh in a short period of time. Furthermore, if the user is traveling with family, the presentation unit can prioritize displaying plans that include family-friendly activities. Furthermore, if the user is seeking a healthy lifestyle, the presentation unit can prioritize displaying plans that include health-conscious activities. This allows the presented content to be customized based on the user's current lifestyle. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's current lifestyle into AI, which analyzes the information and suggests optimal presented content.

[0085] The presentation unit can estimate the user's emotions and determine the order in which the plans are presented based on the estimated user emotions. For example, if the user is relaxed, the presentation unit can prioritize presenting detailed plans. Furthermore, if the user is in a hurry, the presentation unit can prioritize presenting important elements (such as transportation and accommodations). Furthermore, if the user is feeling stressed, the presentation unit can prioritize presenting simple plans. This allows the presentation order of the plans to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the user's emotions into an AI, which then analyzes the emotions and proposes an optimal presentation order.

[0086] When presenting a plan, the presentation unit can select the optimal presentation method by taking into account the user's device information. For example, if the user is using a smartphone, the presentation unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the presentation unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the presentation unit can also provide a simple, highly visible display method. This makes it possible to select the optimal presentation method based on the user's device information. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's device information into AI, which analyzes the information and proposes the optimal presentation method.

[0087] When presenting a plan, the presentation unit can analyze the user's social media activity and present relevant plans. For example, the presentation unit can present relevant plans based on travel destinations shared by the user on social media. The presentation unit can also analyze activities of interest from the user's social media activity and reflect them in the plan. The presentation unit can also present potential travel destinations based on posts by the user's friends on social media. This makes it possible to present relevant plans based on the user's social media activity. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's social media activity into AI, which can analyze the information and propose an optimal plan.

[0088] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. For example, if the user is nervous, the monitoring unit can provide a simple, highly visible monitoring method. Furthermore, if the user is relaxed, the monitoring unit can provide a monitoring method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a monitoring method that focuses on the main points. This allows the monitoring method to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the monitoring unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the monitoring unit can input the user's emotions into an AI, which can analyze the emotions and suggest an optimal monitoring method.

[0089] During monitoring, the monitoring unit can optimize the monitoring algorithm by referring to past reservation data. For example, the monitoring unit can predict busy times and locations based on past reservation data and adjust the monitoring algorithm. The monitoring unit can also extract specific patterns from past reservation data and reflect them in the monitoring algorithm. The monitoring unit can also analyze past reservation data and introduce the most efficient monitoring method. This makes it possible to optimize the monitoring algorithm based on past reservation data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past reservation data into AI, which analyzes the data and proposes an optimal monitoring algorithm.

[0090] During monitoring, the monitoring unit can customize the monitoring content based on the user's current lifestyle. For example, if the user is busy with work, the monitoring unit can prioritize monitoring a travel plan that allows for short-term refreshment. Furthermore, if the user is traveling with family, the monitoring unit can prioritize monitoring a plan that includes family activities. Furthermore, if the user seeks a healthy lifestyle, the monitoring unit can prioritize monitoring a plan that includes health-conscious activities. This allows the monitoring content to be customized based on the user's current lifestyle. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's current lifestyle into AI, which can analyze the information and suggest optimal monitoring content.

[0091] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is nervous, the monitoring unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the monitoring unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a display method that focuses on the main points. This allows the display method of the monitoring results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotions into an AI, which can analyze the emotions and suggest an optimal display method.

[0092] During monitoring, the monitoring unit can optimize the monitoring content by taking into account the user's geographical location information. For example, the monitoring unit prioritizes monitoring of travel destinations that are close to the user's current location. The monitoring unit can also provide an optimal monitoring method based on the user's geographical location information. The monitoring unit can also optimize monitoring of transportation means by taking into account the user's geographical location information. This allows the monitoring content to be optimized based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information into AI, which analyzes the information and suggests optimal monitoring content.

[0093] During monitoring, the monitoring unit can analyze the user's social media activity and provide relevant monitoring content. The monitoring unit can provide relevant monitoring content based on, for example, travel destinations shared by the user on social media. The monitoring unit can also analyze activities of interest from the user's social media activity and reflect these in the monitoring content. The monitoring unit can also provide travel destination monitoring content based on posts by the user's friends on social media. This makes it possible to provide relevant monitoring content based on the user's social media activity. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input the user's social media activity into AI, which can analyze the information and suggest optimal monitoring content.

[0094] The distribution unit can estimate the user's emotions and adjust the distribution method based on the estimated user emotions. For example, if the user is nervous, the distribution unit can provide a simple, highly visible distribution method. Furthermore, if the user is relaxed, the distribution unit can provide a distribution method that includes detailed information. Furthermore, if the user is in a hurry, the distribution unit can provide a distribution method that focuses on the main points. This allows the distribution method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the distribution unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the distribution unit can input the user's emotions into an AI, which can analyze the emotions and propose an optimal distribution method.

[0095] During distribution, the distribution unit can optimize the distributed algorithm by referring to past distributed data. For example, the distribution unit predicts times and locations of congestion based on past distributed data and adjusts the distributed algorithm. The distribution unit can also extract specific patterns from past distributed data and reflect them in the distributed algorithm. The distribution unit can also analyze past distributed data and introduce the most efficient distribution method. This makes it possible to optimize the distributed algorithm based on past distributed data. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input past distributed data into AI, which analyzes the data and proposes the optimal distributed algorithm.

[0096] The distribution unit can customize the distribution content based on the user's current living situation during distribution. For example, if the user is busy with work, the distribution unit can prioritize distribution of travel plans that allow the user to refresh in a short period of time. Furthermore, if the user is traveling with family, the distribution unit can prioritize distribution of plans that include family activities. Furthermore, if the user is seeking a healthy lifestyle, the distribution unit can prioritize distribution of plans that include health-conscious activities. This allows the distribution content to be customized based on the user's current living situation. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the user's current living situation into AI, which can analyze the information and suggest optimal distribution content.

[0097] The distribution unit can estimate the user's emotions and adjust the display method of the distribution results based on the estimated user emotions. For example, if the user is nervous, the distribution unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the distribution unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the distribution unit can provide a display method that focuses on the main points. This allows the display method of the distribution results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the distribution unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the distribution unit can input the user's emotions into an AI, which can analyze the emotions and propose an optimal display method.

[0098] During distribution, the distribution unit can optimize distribution content taking into account the user's geographical location information. For example, the distribution unit prioritizes distribution to travel destinations close to the user's current location. The distribution unit can also provide an optimal distribution method based on the user's geographical location information. The distribution unit can also optimize distribution of transportation means taking into account the user's geographical location information. This makes it possible to optimize distribution content based on the user's geographical location information. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the user's geographical location information into AI, which can analyze the information and propose optimal distribution content.

[0099] The distribution unit can analyze the user's social media activity during distribution and provide related distributed content. For example, the distribution unit can provide related distributed content based on travel destinations shared by the user on social media. The distribution unit can also analyze activities of interest from the user's social media activity and reflect them in the distributed content. The distribution unit can also provide distributed content about travel destinations by referring to posts by the user's friends on social media. This makes it possible to provide related distributed content based on the user's social media activity. Some or all of the above-described processing in the distribution unit can be performed using, for example, AI, or can be performed without using AI. For example, the distribution unit can input the user's social media activity into AI, which can analyze the information and suggest optimal distributed content. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, presentation unit, monitoring unit, and distribution 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 reception unit is realized by the reception device 38 of the smart device 14, and the user inputs desired travel conditions. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input conditions to generate an optimal travel plan. The presentation unit is realized by the output device 40 of the smart device 14, and presents the generated plan to the user. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12, and monitors reservation status in real time. The distribution unit is realized by the specific processing unit 290 of the data processing device 12, and promotes distribution of travel dates and travel destinations according to congestion status. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, presentation unit, monitoring unit, and distribution 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 reception unit is realized by the microphone 238 of the smart glasses 214, and the user inputs desired travel conditions by voice. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input conditions to generate an optimal travel plan. The presentation unit is realized by the speaker 240 of the smart glasses 214, and presents the generated plan to the user. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12, and monitors reservation status in real time. The distribution unit is realized by the specific processing unit 290 of the data processing device 12, and promotes distribution of travel dates and travel destinations according to congestion status. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, presentation unit, monitoring unit, and distribution unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, and the user inputs desired travel conditions by voice. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input conditions to generate an optimal travel plan. The presentation unit is realized by the display 343 of the headset-type terminal 314, and presents the generated plan to the user. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12, and monitors the reservation status in real time. The distribution unit is realized by the specific processing unit 290 of the data processing device 12, and promotes distribution of travel dates and travel destinations according to the congestion situation. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, presentation unit, monitoring unit, and distribution unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and the user inputs desired travel conditions by voice. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input conditions and generates an optimal travel plan. The presentation unit is realized by the speaker 240 of the robot 414, and presents the generated plan to the user. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12, and monitors reservation status in real time. The distribution unit is realized by the specific processing unit 290 of the data processing device 12, and promotes distribution of travel dates and travel destinations according to congestion status.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The travel planning system may further include a health management unit that monitors the user's health condition. The health management unit collects the user's health data (e.g., heart rate, blood pressure, number of steps, etc.) and reflects it in the travel plan. For example, if the user has high blood pressure, the health management unit can suggest travel destinations and activities that will help them relax. Also, if the user is not getting enough exercise, the health management unit can generate a plan that includes activities such as walking tours and hiking. This makes it possible to provide the user with an optimal travel plan based on their health condition.

[0102] The travel planning system may further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit analyzes the user's past travel history and social media posts to identify the user's hobbies and interests. For example, if the user is interested in history, the hobby analysis unit may suggest a travel plan that includes historical tourist sites and museums. Also, if the user likes outdoor activities, the hobby analysis unit may generate a plan that includes camping and trekking. This makes it possible to provide a travel plan based on the user's hobbies and interests.

[0103] The travel planning system may further include a safety management unit to ensure the safety of the user during the trip. The safety management unit collects security and weather information for the travel destination in real time and provides it to the user. For example, if security at the travel destination deteriorates, the safety management unit can suggest an alternative travel destination. Also, if the weather worsens, the safety management unit can suggest indoor activities that can be enjoyed. This ensures the user's safety during the trip and allows them to enjoy their trip with peace of mind.

[0104] The travel planning system may further include a communication support unit that supports communication during the user's trip. The communication support unit provides information about the language and culture of the travel destination, helping the user to communicate smoothly with local people. For example, the communication support unit may provide a guide to learning basic greetings and phrases at the travel destination. It may also provide information about the local culture and customs and give advice to the user to avoid cultural misunderstandings. This allows the user to communicate smoothly at the travel destination.

[0105] The travel planning system may further include an entertainment provider that provides entertainment for the user during their trip. The entertainment provider provides entertainment content such as movies, music, and books according to the user's preferences. For example, the entertainment provider can suggest movies and music that the user can enjoy during a long flight. It can also provide travel-related books, such as tourist guides and history books for the travel destination. This allows the user to spend their time during their trip meaningfully.

[0106] The travel planning system may further include an emotion response unit that estimates the user's emotion and selects a travel destination based on the estimated emotion. If the user is feeling stressed, the emotion response unit may suggest a travel destination where the user can relax. If the user is excited, the emotion response unit may suggest a travel destination that includes active activities. For example, if the user is tired, the emotion response unit may suggest a hot spring resort or a resort, and if the user is energetic, the emotion response unit may suggest a travel destination that includes an adventure tour or a sporting event. This makes it possible to provide the optimal travel destination according to the user's emotion.

[0107] The travel planning system may further include an emotion customization unit that estimates the user's emotions and customizes the travel plan based on the estimated emotions. If the user is relaxed, the emotion customization unit may suggest a leisurely travel plan. If the user is in a hurry, the emotion customization unit may suggest a plan for efficiently touring tourist spots. For example, if the user wants to relax, the emotion customization unit may suggest a plan including a spa or massage, and if the user wants to be active, the emotion customization unit may suggest a plan including sports or adventure activities. This makes it possible to provide a travel plan customized according to the user's emotions.

[0108] The travel planning system may further include an emotion support unit that estimates the user's emotions and provides support during the trip based on the estimated emotions. If the user feels anxious, the emotion support unit provides information that gives the user a sense of security. Also, if the user feels excited, the emotion support unit can suggest activities to maintain the user's excitement. For example, if the user feels anxious, the emotion support unit can provide local security information and emergency contact information, and if the user feels excited, the emotion support unit can suggest local events and festivals. In this way, support can be provided according to the user's emotions.

[0109] The travel planning system may further include an emotional activity unit that estimates the user's emotions and adjusts activities during the trip based on the estimated emotions. The emotional activity unit may suggest relaxing activities if the user is relaxed, or suggest active activities if the user is excited. For example, if the user wants to relax, yoga or meditation classes may be suggested, and if the user wants to be active, sports or adventure activities may be suggested. In this way, activities can be provided according to the user's emotions.

[0110] The travel planning system may further include an emotional meal unit that estimates the user's emotions and adjusts the meal plan for the trip based on the estimated emotions. The emotional meal unit may suggest a relaxing meal plan if the user is relaxed, or an energetic meal plan if the user is excited. For example, if the user wants to relax, it may suggest a healthy and relaxing restaurant, or if the user wants to be active, it may suggest an energy-replenishing meal plan. In this way, it is possible to provide a meal plan that corresponds to the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit inputs the user's desired travel conditions. The user's desired travel conditions include the departure point, destination, travel itinerary, budget, and preferred activities. For example, the user can input the name of a city as the departure point and the name of a tourist spot as the destination. The user can also input the departure date and return date as the travel itinerary and the total budget as the budget. Furthermore, the user can select sightseeing or sports as the preferred activities. Step 2: The generation unit analyzes the information received by the reception unit and generates an optimal travel plan. The generation unit generates a travel plan that includes reservations for transportation, accommodation, and activities at the travel destination. For example, the generation unit makes reservations for flights from the departure point entered by the user to the destination, and for accommodation at the destination. It can also make reservations for sightseeing tours at the destination. Step 3: The presentation unit presents the plans generated by the generation unit to the user, and the user selects one. The presentation unit displays the generated plans on the user's smartphone or computer, allowing the user to select one. Step 4: The monitoring unit monitors the reservation status in real time. The monitoring unit monitors the reservation status of transportation and accommodation in real time. For example, it can monitor the reservation status of flights and suggest alternative flights if they are crowded. Step 5: The distribution unit promotes distribution of travel dates and travel destinations based on the reservation status monitored by the monitoring unit. If a specific travel destination or date is crowded, the distribution unit suggests other travel destinations or dates. For example, instead of a crowded tourist destination, the distribution unit can suggest other tourist destinations. Also, instead of a crowded date, the distribution unit can suggest other dates.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 reception unit for inputting user's desired travel conditions; a generation unit that analyzes the information received by the reception unit and generates a travel plan; a presentation unit that presents the plan generated by the generation unit to a user; A monitoring unit that monitors reservation status in real time; a distribution unit that promotes distribution of travel dates and travel destinations based on the reservation status monitored by the monitoring unit; A system characterized by:

2. The reception unit Accepts information about the user's origin, destination, travel dates, budget, and preferred activities 2. The system of claim 1.

3. The generation unit Analyzing the information received by the reception unit, and generating a travel plan including reservations for transportation, accommodation, and activities at the travel destination.

2. The system of claim 1.

4. The presentation unit The plan generated by the generation unit is presented to the user, and the user selects one.

2. The system of claim 1.

5. The monitoring unit Monitor booking status in real time 2. The system of claim 1.

6. The dispersion unit is Based on the reservation status monitored by the monitoring unit, if a specific travel destination or itinerary is crowded, other travel destinations or itineraries can be suggested.

2. The system of claim 1.

7. The reception unit The system estimates the user's emotions and adjusts the input method for desired travel conditions based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past travel history and select the input method 2. The system of claim 1.

9. The reception unit When entering travel preferences, filters are performed based on the user's current living situation and areas of interest.

2. The system of claim 1.

10. The reception unit The system estimates the user's emotions and determines the priority of the travel requirements based on the estimated user emotions.

2. The system of claim 1.

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