System

Abstract

An object of a system according to an embodiment is to provide stopover information on a route during a trip and contribute to regional revitalization.SOLUTION: A system includes a reception unit, an analysis unit, a proposal unit, and a position information unit. The receiving unit receives an input from a user. The analysis unit analyzes the information received by the reception unit. The proposal unit makes a proposal based on the analysis result obtained by the analysis unit. The position information unit uses real-time position information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult to efficiently provide detour information during travel, and there is room for this technology to contribute to regional revitalization.

[0005] The system according to the embodiment aims to provide detour information along the way during travel and contribute to regional revitalization. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a location information unit. The reception unit accepts user input. The analysis unit analyzes the information accepted by the reception unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The location information unit uses real-time location information. [Effects of the Invention]

[0007] The system according to the embodiment can provide detour information along the way during travel, thereby contributing to regional revitalization. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel plan proposal system according to an embodiment of the present invention proposes an appropriate travel plan based on a user's input of a destination and a mode of transportation. The travel plan proposal system analyzes the user's input of the destination and mode of transportation and suggests detour spots along the way and delicious local cuisine. These suggestions also contribute to attracting visitors to depopulated areas that are typically unpopulated. For example, the user inputs information such as "traveling from Tokyo to Osaka by car." This information is input into the travel plan proposal system. The travel plan proposal system then analyzes the input information and suggests detour spots along the way and delicious local cuisine. For example, if a user is traveling from Tokyo to Osaka by car, the system suggests parking areas along the way and delicious local cuisine a little further away. These suggestions select optimal spots based on the user's travel route. Furthermore, the travel plan proposal system also contributes to attracting visitors to depopulated areas that are typically unpopulated. For example, by suggesting tourist spots and restaurants in depopulated areas, the system can revitalize the area. This allows travelers to easily find detour spots along the way and delicious local cuisine, increasing the enjoyment of their trip. It will also contribute to attracting visitors to depopulated areas, thereby revitalizing the region. As a result, the travel plan suggestion system will allow travelers to easily find detour spots along the way and delicious local cuisine, making their trip more enjoyable. It will also contribute to attracting visitors to depopulated areas, thereby revitalizing the region.

[0029] The travel plan proposal system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a location information unit. The reception unit accepts user input. The user input may include, but is not limited to, text input, voice input, image input, and the like. The reception unit accepts, for example, user input such as "travel from Tokyo to Osaka by car." The analysis unit analyzes the information accepted by the reception unit. The analysis may be performed using, for example, but is not limited to, data mining, natural language processing, statistical analysis, and the like. For example, the analysis unit selects optimal detour spots based on the travel route entered by the user. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposals may include, for example, travel plans, tourist spots, restaurants, and the like, but are not limited to, the like. For example, the proposal unit proposes delicious local dishes that the user can stop by during their travel. The location information unit uses real-time location information. The real-time location information may be obtained using, for example, but is not limited to, GPS, Wi-Fi location information, mobile base station information, and the like. For example, the location information unit may suggest optimal detour spots based on the user's current location. As a result, the travel plan suggestion system according to the embodiment suggests appropriate travel plans based on user input and utilizes real-time location information, thereby increasing the enjoyment of travel and contributing to regional revitalization.

[0030] The travel plan suggestion system includes a learning unit that learns the user's past travel history and preferences. The learning unit learns the user's past travel history and preferences. The past travel history and preferences include, but are not limited to, places visited, accommodations, and food preferences. For example, the learning unit collects data on tourist spots and restaurants visited by the user in the past to learn the user's preferences. The learning unit can also suggest similar plans based on travel plans that the user has previously preferred. Furthermore, the learning unit can improve the accuracy of suggestions based on the user's past feedback. For example, the learning unit prioritizes suggestions based on spots that the user has previously rated highly. This allows for more personalized suggestions by learning the user's past travel history and preferences.

[0031] The travel plan proposal system includes a collaboration unit that collaborates with local tourist associations or restaurants. The collaboration unit collaborates with local tourist associations or restaurants. Collaboration includes, but is not limited to, methods such as API collaboration, data sharing, and joint promotion. For example, the collaboration unit collaborates with local tourist associations to provide information on tourist spots. The collaboration unit can also collaborate with local restaurants to suggest delicious local dishes. Furthermore, the collaboration unit can also collaborate with local tourist associations and restaurants to revitalize the region. For example, the collaboration unit can hold events in collaboration with the tourist association to attract tourists. In this way, collaboration with local tourist associations and restaurants contributes to revitalizing the region.

[0032] The travel plan proposal system includes a collection unit that collects information using crowdsourcing. The collection unit collects information using crowdsourcing. Crowdsourcing includes, but is not limited to, examples of task distribution methods, reward settings, and data collection methods. For example, the collection unit uses crowdsourcing to collect the latest information on tourist spots and restaurants. The collection unit can also use crowdsourcing to collect user reviews and ratings. Furthermore, the collection unit can also use crowdsourcing to collect local event information. For example, the collection unit assigns tasks to users and collects photos and reviews of tourist spots. In this way, crowdsourcing can be used to collect the latest information and improve the accuracy of proposals.

[0033] The suggestion unit can select appropriate spots based on the user's travel route. The suggestion unit selects appropriate spots based on the user's travel route. The travel route may include, but is not limited to, GPS data, user input, and past travel history. For example, if the user inputs "travel from Tokyo to Osaka by car," the suggestion unit can suggest parking areas along the way or delicious local cuisine in locations a little further away. The suggestion unit can also select optimal tourist spots based on the user's travel route. Furthermore, the suggestion unit can suggest tourist spots and restaurants in depopulated areas based on the user's travel route. For example, the suggestion unit can suggest tourist spots that the user can stop by during their travel. This can increase the enjoyment of the trip by selecting optimal spots based on the user's travel route.

[0034] The location information unit can improve the accuracy of suggestions by using the user's real-time location information. The location information unit improves the accuracy of suggestions by using the user's real-time location information. Examples of real-time location information include, but are not limited to, GPS, Wi-Fi location information, and mobile base station information. For example, the location information unit can suggest optimal detour spots based on the user's current location. The location information unit can also acquire location information of the user while traveling in real time to improve the accuracy of suggestions. Furthermore, the location information unit can suggest tourist spots and restaurants in sparsely populated areas based on the user's location information. For example, the location information unit suggests tourist spots that the user can stop by while traveling. In this way, by using real-time location information, the accuracy of suggestions is improved, improving the user's travel experience.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit analyzes the user's past input history and suggest the optimal input method. The past input history includes, but is not limited to, past search history, input content, and frequency of use. For example, the reception unit automatically displays destinations and transportation methods that the user has frequently input 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. Furthermore, the reception unit can predict and suggest destinations and transportation methods to be used during a specific time period based on the user's past input history. For example, the reception unit suggests optimal candidate locations based on places the user has visited in the past on a specific day of the week or time period. In this way, the optimal input method can be suggested to the user by analyzing the past input history.

[0036] The reception unit can simplify the input process by automatically acquiring the user's current location information when inputting a destination and a mode of transportation. The reception unit can simplify the input process by automatically acquiring the user's current location information when inputting a destination and a mode of transportation. Current location information includes, but is not limited to, GPS, Wi-Fi location information, and mobile base station information. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the starting location. The reception unit can also suggest optimal candidate locations by taking into account the distance from the current location when the user inputs a destination. Furthermore, when the user uses the app while traveling, the reception unit can update the user's current location in real time and reflect it as the starting location. For example, when the user uses the app while traveling, the reception unit acquires the user's current location in real time and suggests an optimal travel route. This simplifies the input process by automatically acquiring the current location information, thereby improving user convenience.

[0037] The reception unit may support voice input or gesture input when inputting a destination and a method of travel. The reception unit supports voice input or gesture input when inputting a destination and a method of travel. Voice input or gesture input includes, but is not limited to, voice recognition technology and gesture recognition technology. For example, the reception unit automatically sets the destination and the method of travel when the user simply voice-inputs, "From Tokyo to Osaka by car." The reception unit may also allow the user to easily set the destination and the method of travel by performing specific gestures on the smartphone screen. Furthermore, the reception unit may combine voice input and gesture input to more intuitively set the destination and the method of travel. For example, the reception unit may suggest an optimal input method when the user combines voice input and gesture input to set the destination and the method of travel. Thus, by supporting voice input or gesture input, a more intuitive input method can be provided.

[0038] The reception unit can automatically suggest candidate locations by referring to the user's past travel history when the user inputs a destination and a travel method. The reception unit automatically suggests candidate locations by referring to the user's past travel history when the user inputs a destination and a travel method. The past travel history includes, but is not limited to, visited places, means of travel, and travel time. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidate locations. The reception unit can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. Furthermore, the reception unit can analyze the user's past travel patterns and suggest optimal candidate locations. For example, the reception unit suggests optimal candidate locations based on places that the user has visited on specific days of the week or during specific time periods in the past. In this way, the optimal candidate locations can be suggested to the user by referring to the past travel history.

[0039] The reception unit can make a suggestion based on the schedule by referring to the user's calendar information when the destination and the mode of transportation are input. The reception unit can make a suggestion based on the schedule by referring to the user's calendar information when the destination and the mode of transportation are input. The calendar information includes, for example, the type of schedule, the time of the schedule, the location of the schedule, etc., but is not limited to these examples. For example, the reception unit can refer to the schedule registered in the user's calendar and automatically set the destination and the mode of transportation. The reception unit can also suggest locations related to a specific event as candidate locations based on the user's calendar information. Furthermore, the reception unit can suggest an optimal route based on the schedule based on the user's calendar information. For example, the reception unit can suggest locations related to a specific event as candidate locations based on the user's calendar information. This makes it possible to make optimal suggestions based on the schedule by referring to the calendar information.

[0040] The reception unit can analyze the user's social media activity and suggest related places when the user inputs a destination and a mode of transportation. The reception unit analyzes the user's social media activity and suggests related places when the user inputs a destination and a mode of transportation. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit suggests places where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidate locations. Furthermore, the reception unit can suggest related places by referring to the activities of the user's friends on social media. For example, the reception unit suggests related places as candidate locations based on the activities of the user's friends on social media. In this way, it is possible to suggest related places to the user by analyzing social media activity.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the trip during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the trip during the analysis. The importance of the trip includes, but is not limited to, the purpose of the trip, the duration of the trip, and the budget for the trip. For example, the analysis unit may perform a detailed analysis in the case of an important trip and suggest multiple detour spots. In addition, the analysis unit may perform a simplified analysis in the case of a short trip and suggest only the main spots. Furthermore, in the case of a family trip, the analysis unit may prioritize suggesting spots that the whole family can enjoy. For example, in the case of a family trip, the analysis unit prioritizes suggesting spots that the whole family can enjoy. In this way, by adjusting the level of detail of the analysis based on the importance of the trip, more appropriate suggestions can be made.

[0042] The analysis unit can apply different analysis algorithms depending on the travel category during analysis. The analysis unit applies different analysis algorithms depending on the travel category during analysis. Travel categories include, but are not limited to, sightseeing trips, business trips, and family trips, for example. For example, in the case of sightseeing trips, the analysis unit applies an algorithm that prioritizes suggesting tourist spots. In addition, in the case of business trips, the analysis unit can also apply an algorithm that prioritizes suggesting efficient travel routes. Furthermore, in the case of gourmet trips, the analysis unit can apply an algorithm that prioritizes suggesting delicious local dishes. For example, in the case of gourmet trips, the analysis unit prioritizes suggesting delicious local dishes. In this way, by applying different analysis algorithms depending on the travel category, more appropriate suggestions can be made.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during the analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during the analysis. Past analysis results include, but are not limited to, the type of analysis result, the accuracy of the analysis result, and how the analysis result is used. For example, the analysis unit can suggest similar spots based on spots that the user has previously liked. The analysis unit can also analyze the user's past travel history and suggest optimal detour spots. Furthermore, the analysis unit can reflect the user's past feedback and adjust the analysis algorithm. For example, the analysis unit can adjust the analysis algorithm based on the user's past feedback. By referring to past analysis results, the accuracy of the analysis can be improved, enabling more appropriate suggestions.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of trip submission. During analysis, the analysis unit determines the priority of analysis based on the time of trip submission. The time of trip submission includes, but is not limited to, for example, the submission date, submission time, and submission frequency. For example, the analysis unit prioritizes analysis for the most recent trip and quickly makes suggestions. In addition, the analysis unit can perform a detailed analysis and make multiple suggestions for long-term travel plans. Furthermore, in the case of spontaneous trips, the analysis unit can perform a simplified analysis and suggest only major spots. For example, the analysis unit suggests only major spots for spontaneous trips. In this way, by determining the priority of analysis based on the time of trip submission, quick and appropriate suggestions are possible.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the trip during analysis. The analysis unit adjusts the order of analysis based on the relevance of the trip during analysis. Travel relevance includes, but is not limited to, the purpose of the trip, the location of the trip, and the duration of the trip. For example, the analysis unit prioritizes analysis and suggestions of major tourist spots. The analysis unit can also prioritize analysis of highly relevant spots based on the user's interests. Furthermore, the analysis unit can perform analysis in an efficient order based on the user's travel route. For example, the analysis unit performs analysis in an efficient order based on the user's travel route. As a result, adjusting the order of analysis based on the relevance of the trip enables efficient and appropriate suggestions.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, if the user is a tourism novice, the analysis unit can display the analysis results in simple language. Furthermore, if the user is knowledgeable about tourism, the analysis unit can display the analysis results using detailed technical terms. Furthermore, the analysis unit can adjust the use of optimal technical terms based on the user's past feedback. For example, the analysis unit adjusts the use of optimal technical terms based on the user's past feedback. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the spot when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on the importance of the spot when making a suggestion. The importance of a spot includes, but is not limited to, the popularity of a tourist spot, historical value, and ease of access. For example, the suggestion unit can provide detailed information and suggest multiple options for an important spot. Furthermore, the suggestion unit can provide concise information and suggest only the main options for a spot that can be visited in a short time. Furthermore, the suggestion unit can prioritize providing information that the whole family can enjoy for a family spot. For example, the suggestion unit prioritizes providing information that the whole family can enjoy for a family spot. In this way, adjusting the level of detail of the suggestion based on the importance of the spot enables more appropriate suggestions.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the spot when making a suggestion. The suggestion unit applies different suggestion algorithms depending on the category of the spot when making a suggestion. Spot categories include, for example, tourist spots, restaurants, accommodations, etc., but are not limited to these examples. For example, the suggestion unit applies a suggestion algorithm specialized for tourism in the case of a tourist spot. Furthermore, the suggestion unit can also apply a suggestion algorithm specialized for gourmet in the case of a dining spot. Furthermore, the suggestion unit can also apply a suggestion algorithm specialized for shopping in the case of a shopping spot. For example, the suggestion unit applies a suggestion algorithm specialized for shopping in the case of a shopping spot. In this way, by applying different suggestion algorithms depending on the category of the spot, more appropriate suggestions can be made.

[0049] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. Past suggestion results include, but are not limited to, the type of suggestion result, the accuracy of the suggestion result, and how the suggestion result is used. For example, the suggestion unit can suggest similar spots based on spots that the user has previously liked. The suggestion unit can also analyze the user's past travel history to suggest optimal detour spots. Furthermore, the suggestion unit can reflect the user's past feedback and adjust the suggestion algorithm. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past feedback. By referring to past suggestion results, the accuracy of suggestions can be improved, enabling more appropriate suggestions.

[0050] The suggestion unit may determine the priority of the suggestions based on the time of submission of the spots when making the suggestions. The suggestion unit may determine the priority of the suggestions based on the time of submission of the spots when making the suggestions. The time of submission of the spots includes, but is not limited to, for example, the submission date, the submission time, and the submission frequency. For example, the suggestion unit may prioritize suggestions for upcoming spots and provide information quickly. For long-term spots, the suggestion unit may also provide detailed suggestions and multiple options. Furthermore, for unexpected spots, the suggestion unit may provide simplified suggestions and only major options. For example, the suggestion unit may provide only major options for unexpected spots. This allows for prompt and appropriate suggestions by determining the priority of the suggestions based on the time of submission of the spots.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the spots when making suggestions. The suggestion unit can adjust the order of suggestions based on the relevance of the spots when making suggestions. The relevance of the spots includes, but is not limited to, for example, the location of the spots, the theme of the spots, and the popularity of the spots. For example, the suggestion unit can prioritize suggestions of major tourist spots and provide detailed information. The suggestion unit can also prioritize suggestions of highly relevant spots based on the user's interests. Furthermore, the suggestion unit can make suggestions in an efficient order based on the user's travel route. For example, the suggestion unit makes suggestions in an efficient order based on the user's travel route. In this way, adjusting the order of suggestions based on the relevance of the spots enables efficient and appropriate suggestions.

[0052] The suggestion unit may adjust the use of technical terminology in the suggestion according to the user's level of expertise when making a suggestion. The suggestion unit may adjust the use of technical terminology in the suggestion according to the user's level of expertise when making a suggestion. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, if the user is a tourism novice, the suggestion unit may display the suggestion results in simple language. Furthermore, if the user is knowledgeable about tourism, the suggestion unit may display the suggestion results using detailed technical terminology. Furthermore, the suggestion unit may adjust the use of optimal technical terminology based on the user's past feedback. For example, the suggestion unit adjusts the use of optimal technical terminology based on the user's past feedback. This allows for suggestions that are easier to understand by adjusting the use of technical terminology according to the user's level of expertise.

[0053] When acquiring location information, the location information unit can select the optimal acquisition method by referring to the user's past movement history. When acquiring location information, the location information unit selects the optimal acquisition method by referring to the user's past movement history. The past movement history includes, for example, visited places, means of transportation, and travel time, but is not limited to these examples. For example, the location information unit selects the optimal acquisition method based on places the user has frequently visited in the past. The location information unit can also select an acquisition method that avoids congestion based on the user's past movement history. Furthermore, the location information unit can analyze the user's past movement history and select the most efficient acquisition method. For example, the location information unit selects the optimal acquisition method based on the user's past movement history. In this way, the optimal location information acquisition method can be selected by referring to the past movement history.

[0054] The location information unit can customize the acquisition means based on the user's current travel situation when acquiring location information. The location information unit customizes the acquisition means based on the user's current travel situation when acquiring location information. The current travel situation includes, but is not limited to, the travel means, travel speed, and travel direction. For example, if the user is traveling by car, the location information unit can acquire location information using an in-car GPS. Also, if the user is traveling on foot, the location information unit can acquire location information using a smartphone GPS. Furthermore, if the user is using public transportation, the location information unit can customize the acquisition means using location information of the transportation. For example, if the user is using public transportation, the location information unit customizes the acquisition means using location information of the transportation. In this way, by customizing the acquisition means based on the current travel situation, more appropriate location information can be provided.

[0055] The location information unit can improve the acquisition method by reflecting user feedback when acquiring location information. The location information unit improves the acquisition method by reflecting user feedback when acquiring location information. Feedback includes, but is not limited to, user ratings, comments, usage status, etc. For example, the location information unit improves the acquisition method when the user is dissatisfied with the accuracy of location information acquisition. Furthermore, the location information unit can adjust the acquisition frequency when the user is dissatisfied with the frequency of location information acquisition. Furthermore, the location information unit can suggest an optimal acquisition method based on user feedback. For example, the location information unit suggests an optimal acquisition method based on user feedback. In this way, the accuracy of the location information acquisition method is improved by reflecting user feedback.

[0056] The location information unit can select an optimal acquisition method by taking into consideration the user's geographical location information when acquiring location information. The location information unit can select an optimal acquisition method by taking into consideration the user's geographical location information when acquiring location information. Geographical location information includes, but is not limited to, GPS data, address information, map data, etc. For example, when the user is in an urban area, the location information unit can acquire location information by using Wi-Fi or cell tower information. Furthermore, when the user is in a suburban area, the location information unit can acquire location information by using GPS. Furthermore, when the user is in a mountainous area, the location information unit can acquire location information by using satellite communication. For example, when the user is in a mountainous area, the location information unit acquires location information by using satellite communication. In this way, the optimal location information acquisition method can be selected by taking into consideration the geographical location information.

[0057] The location information unit can analyze the user's social media activity when acquiring location information and suggest a means for acquiring location information. The location information unit can analyze the user's social media activity when acquiring location information and suggest a means for acquiring location information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the location information unit can suggest an optimal acquisition means based on the locations where the user checked in on social media. The location information unit can also analyze the content of the user's social media posts and acquire location information of related locations. Furthermore, the location information unit can acquire location information of related locations by referring to the activities of the user's friends on social media. For example, the location information unit acquires location information of related locations based on the activities of the user's friends on social media. In this way, the optimal location information acquisition means can be suggested by analyzing social media activity.

[0058] The location information unit can customize the acquisition method by reflecting the user's past feedback when acquiring location information. The location information unit customizes the acquisition method by reflecting the user's past feedback when acquiring location information. Feedback includes, but is not limited to, user ratings, comments, usage status, etc. For example, the location information unit suggests an optimal acquisition method based on acquisition methods that the user has preferred in the past. The location information unit can also adjust the acquisition frequency based on the user's past feedback. Furthermore, the location information unit can improve acquisition accuracy by reflecting the user's past feedback. For example, the location information unit suggests an optimal acquisition method based on the user's past feedback. In this way, the accuracy of the acquisition method is improved by reflecting the past feedback.

[0059] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit optimizes the learning algorithm by referring to past learning data during learning. Past learning data includes, but is not limited to, past travel history, user preferences, feedback, etc. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also extract effective learning patterns from the past learning data and optimize the algorithm. Furthermore, the learning unit can analyze the past learning data and improve the accuracy of the learning algorithm. For example, the learning unit selects an optimal learning algorithm based on the past learning data. In this way, the accuracy of the learning algorithm is improved by referring to the past learning data.

[0060] The learning unit can analyze fluctuations in the user's travel history during learning and adjust the update frequency of the learning data. The learning unit can analyze fluctuations in the user's travel history during learning and adjust the update frequency of the learning data. Fluctuations in the travel history include, but are not limited to, for example, visit frequency, changes in visited locations, and changes in the purpose of travel. For example, the learning unit can set the update frequency of the learning data to be high if the user's travel history changes frequently. Furthermore, the learning unit can also set the update frequency of the learning data to be low if the user's travel history is stable. Furthermore, the learning unit can analyze the change pattern of the user's travel history and set an optimal update frequency. For example, the learning unit can set the update frequency of the learning data to be high if the user's travel history changes frequently. In this way, the update frequency of the learning data can be optimized by analyzing the fluctuations in the travel history.

[0061] During learning, the learning unit can weight the learning data based on the time of submission of the travel history. During learning, the learning unit weights the learning data based on the time of submission of the travel history. The time of submission of the travel history includes, but is not limited to, for example, the submission date, the submission time, and the submission frequency. For example, the learning unit weights the learning data based on the most recent travel history. The learning unit can also weight the learning data based on long-term travel history. Furthermore, the learning unit can weight the learning data based on sudden travel history. For example, the learning unit weights the learning data based on the most recent travel history. In this way, weighting the learning data based on the time of submission of the travel history enables more appropriate learning.

[0062] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. The learning unit adjusts the learning algorithm by reflecting user feedback during learning. Feedback includes, for example, user ratings, comments, usage status, etc., but is not limited to these examples. For example, the learning unit optimizes the learning algorithm based on user feedback. The learning unit can also extract effective learning patterns from user feedback and adjust the algorithm. Furthermore, the learning unit can analyze user feedback and improve the accuracy of the learning algorithm. For example, the learning unit optimizes the learning algorithm based on user feedback. In this way, the accuracy of the learning algorithm is improved by reflecting user feedback.

[0063] The collaboration unit can select the optimal collaboration method by referring to past collaboration data when collaborating. The collaboration unit selects the optimal collaboration method by referring to past collaboration data when collaborating. The collaboration data includes, for example, collaboration results, collaboration evaluations, collaboration history, etc., but is not limited to these examples. For example, the collaboration unit selects the optimal collaboration method based on past collaboration data. The collaboration unit can also extract effective collaboration patterns from the past collaboration data and optimize the collaboration method. Furthermore, the collaboration unit can analyze past collaboration data and improve the accuracy of the collaboration method. For example, the collaboration unit selects the optimal collaboration method based on past collaboration data. In this way, the accuracy of the collaboration method is improved by referring to the past collaboration data.

[0064] The linking unit can customize the linking content based on the user's current travel plan during linking. The linking unit customizes the linking content based on the user's current travel plan during linking. The current travel plan includes, but is not limited to, the purpose of the trip, the duration of the trip, and the travel budget. For example, the linking unit selects optimal tourist associations and restaurants based on the user's travel plan. The linking unit can also customize the linking content based on the user's travel plan and provide optimal services. Furthermore, the linking unit can analyze the user's travel plan and improve the accuracy of the linking content. For example, the linking unit selects optimal tourist associations and restaurants based on the user's travel plan. This enables more appropriate suggestions to be made by customizing the linking content based on the user's current travel plan.

[0065] The collaboration unit can improve the collaboration method by reflecting user feedback during collaboration. The collaboration unit improves the collaboration method by reflecting user feedback during collaboration. Feedback includes, for example, user ratings, comments, usage status, etc., but is not limited to these examples. For example, the collaboration unit optimizes the collaboration method based on user feedback. The collaboration unit can also extract effective collaboration patterns from user feedback and improve the collaboration method. Furthermore, the collaboration unit can analyze user feedback and improve the accuracy of the collaboration method. For example, the collaboration unit optimizes the collaboration method based on user feedback. In this way, the accuracy of the collaboration method is improved by reflecting user feedback.

[0066] The collaboration unit can select the optimal collaboration method in consideration of the user's geographical location information when collaborating. The collaboration unit selects the optimal collaboration method in consideration of the user's geographical location information when collaborating. Geographical location information includes, but is not limited to, GPS data, address information, map data, etc. For example, if the user is in an urban area, the collaboration unit selects a collaboration method specialized for urban areas. Furthermore, if the user is in a suburban area, the collaboration unit can also select a collaboration method specialized for suburban areas. Furthermore, if the user is in a mountainous area, the collaboration unit can select a collaboration method specialized for mountainous areas. For example, if the user is in a mountainous area, the collaboration unit selects a collaboration method specialized for mountainous areas. In this way, the optimal collaboration method can be selected by taking the geographical location information into consideration.

[0067] The linking unit can analyze the user's social media activity at the time of linking and suggest linking content. The linking unit analyzes the user's social media activity at the time of linking and suggesting linking content. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, the number of followers, etc. For example, the linking unit can suggest optimal linking content based on the location where the user checked in on social media. The linking unit can also analyze the content of the user's posts on social media and suggest related linking content. Furthermore, the linking unit can also suggest related linking content based on the activity of the user's friends on social media. For example, the linking unit can suggest related linking content based on the activity of the user's friends on social media. In this way, optimal linking content can be suggested by analyzing social media activity.

[0068] The collaboration unit can customize the collaboration method by reflecting the user's past feedback at the time of collaboration. The collaboration unit customizes the collaboration method by reflecting the user's past feedback at the time of collaboration. Feedback includes, for example, the user's ratings, comments, usage status, etc., but is not limited to these examples. For example, the collaboration unit proposes an optimal collaboration method based on the user's past feedback. The collaboration unit can also customize the collaboration content based on the user's past feedback. Furthermore, the collaboration unit can reflect the user's past feedback to improve the accuracy of the collaboration method. For example, the collaboration unit proposes an optimal collaboration method based on the user's past feedback. In this way, the accuracy of the collaboration method is improved by reflecting the past feedback.

[0069] The collection unit can select the optimal collection method by referring to past collected data when collecting information. The collection unit selects the optimal collection method by referring to past collected data when collecting information. The collected data includes, for example, the type of collected data, the accuracy of the collected data, and how the collected data is used, but is not limited to these examples. For example, the collection unit selects the optimal collection method based on the past collected data. The collection unit can also extract effective collection patterns from the past collected data and optimize the collection method. Furthermore, the collection unit can analyze the past collected data and improve the accuracy of the collection method. For example, the collection unit selects the optimal collection method based on the past collected data. In this way, the accuracy of the collection method is improved by referring to the past collected data.

[0070] The collection unit can customize the collected content based on the user's current travel plan when collecting information. The collection unit customizes the collected content based on the user's current travel plan when collecting information. The current travel plan includes, but is not limited to, for example, the purpose of the trip, the duration of the trip, and the travel budget. For example, the collection unit collects optimal information based on the user's travel plan. The collection unit can also customize the collected content based on the user's travel plan and provide optimal information. Furthermore, the collection unit can analyze the user's travel plan and improve the accuracy of the collected content. For example, the collection unit collects optimal information based on the user's travel plan. As a result, more appropriate information can be provided by customizing the collected content based on the current travel plan.

[0071] The collection unit can improve the collection method by reflecting user feedback when collecting information. The collection unit improves the collection method by reflecting user feedback when collecting information. Feedback includes, but is not limited to, user ratings, comments, usage status, etc. For example, the collection unit optimizes the collection method based on user feedback. The collection unit can also extract effective collection patterns from user feedback and improve the collection method. Furthermore, the collection unit can analyze user feedback and improve the accuracy of the collection method. For example, the collection unit optimizes the collection method based on user feedback. In this way, the accuracy of the collection method is improved by reflecting user feedback.

[0072] The collection unit can select the optimal collection method in consideration of the user's geographical location information when collecting information. The collection unit selects the optimal collection method in consideration of the user's geographical location information when collecting information. Geographical location information includes, but is not limited to, GPS data, address information, map data, etc. For example, if the user is in an urban area, the collection unit selects a collection method specialized for urban areas. Furthermore, if the user is in a suburban area, the collection unit can also select a collection method specialized for suburban areas. Furthermore, if the user is in a mountainous area, the collection unit can select a collection method specialized for mountainous areas. For example, if the user is in a mountainous area, the collection unit selects a collection method specialized for mountainous areas. In this way, the optimal information collection method can be selected by taking the geographical location information into consideration.

[0073] The collection unit can analyze the user's social media activity when collecting information and suggest content to be collected. The collection unit analyzes the user's social media activity when collecting information and suggest content to be collected. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, the number of followers, etc. For example, the collection unit can suggest optimal content to be collected based on the location where the user checked in on social media. The collection unit can also analyze the content of posts on social media by collecting related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. For example, the collection unit collects related information based on the activities of the user's friends on social media. In this way, optimal information to be collected can be suggested by analyzing social media activity.

[0074] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. The collection unit customizes the collection method by reflecting the user's past feedback when collecting information. Feedback includes, for example, the user's ratings, comments, usage status, etc., but is not limited to these examples. For example, the collection unit suggests an optimal collection method based on the user's past feedback. The collection unit can also customize the collection content based on the user's past feedback. Furthermore, the collection unit can reflect the user's past feedback to improve the accuracy of the collection method. For example, the collection unit suggests an optimal collection method based on the user's past feedback. In this way, the accuracy of the collection method is improved by reflecting the past feedback.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The suggestion unit can suggest similar travel plans based on the user's past travel history. For example, it can collect data on tourist spots and restaurants visited by the user in the past and learn the user's preferences. It can also prioritize and suggest spots that the user has previously given high ratings. Furthermore, it can improve the accuracy of suggestions based on the user's past feedback. This allows the system to learn the user's past travel history and preferences, making it possible to make more personalized suggestions.

[0077] The Collaboration Department can revitalize the region by conducting joint promotions with local tourist associations and restaurants. For example, it can hold events in collaboration with the tourist association to attract tourists. It can also collaborate with local restaurants to propose delicious local dishes. It can also share data with tourist associations and restaurants to provide the latest information on tourist spots and restaurants. In this way, collaboration with local tourist associations and restaurants can contribute to the revitalization of the region.

[0078] The collection unit can use crowdsourcing to collect user word-of-mouth reviews and ratings. For example, it can assign tasks to users to collect photos and reviews of tourist spots. It can also collect information on local events. It can also collect information on the latest tourist spots and restaurants. In this way, by using crowdsourcing, it is possible to collect the latest information and improve the accuracy of suggestions.

[0079] The location information unit can customize the means for acquiring location information based on the user's current movement status. For example, if the user is traveling by car, the location information can be acquired using the in-car GPS. If the user is traveling on foot, the location information can also be acquired using the smartphone's GPS. Furthermore, if the user is using public transportation, the acquisition means can also be customized using the transportation's location information. In this way, by customizing the acquisition means based on the user's current movement status, more appropriate location information can be provided.

[0080] The learning unit can analyze fluctuations in the user's travel history and adjust the update frequency of the learning data. For example, if the user's travel history changes frequently, the update frequency of the learning data can be set high. Also, if the user's travel history is stable, the update frequency of the learning data can be set low. Furthermore, the learning unit can analyze the fluctuation patterns of the user's travel history and set an optimal update frequency. In this way, the update frequency of the learning data can be optimized by analyzing fluctuations in the travel history.

[0081] The processing flow of the first embodiment will be briefly explained below.

[0082] Step 1: The reception unit receives user input. User input includes text input, voice input, image input, etc. For example, the reception unit receives user input such as "travel from Tokyo to Osaka by car." Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as data mining, natural language processing, and statistical analysis. For example, the analysis unit selects optimal detour spots based on the travel route entered by the user. Step 3: The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. The suggestions include travel plans, tourist spots, restaurants, etc. For example, it suggests delicious local dishes that the user can stop by during their travels. Step 4: The location information module uses real-time location information. Real-time location information can be obtained using methods such as GPS, Wi-Fi location information, and mobile base station information. For example, the module suggests optimal detour spots based on the user's current location.

[0083] (Example 2) A travel plan proposal system according to an embodiment of the present invention proposes an appropriate travel plan based on a user's input of a destination and a mode of transportation. The travel plan proposal system analyzes the user's input of the destination and mode of transportation and suggests detour spots along the way and delicious local cuisine. These suggestions also contribute to attracting visitors to depopulated areas that are typically unpopulated. For example, the user inputs information such as "traveling from Tokyo to Osaka by car." This information is input into the travel plan proposal system. The travel plan proposal system then analyzes the input information and suggests detour spots along the way and delicious local cuisine. For example, if a user is traveling from Tokyo to Osaka by car, the system suggests parking areas along the way and delicious local cuisine a little further away. These suggestions select optimal spots based on the user's travel route. Furthermore, the travel plan proposal system also contributes to attracting visitors to depopulated areas that are typically unpopulated. For example, by suggesting tourist spots and restaurants in depopulated areas, the system can revitalize the area. This allows travelers to easily find detour spots along the way and delicious local cuisine, increasing the enjoyment of their trip. It will also contribute to attracting visitors to depopulated areas, thereby revitalizing the region. As a result, the travel plan suggestion system will allow travelers to easily find detour spots along the way and delicious local cuisine, making their trip more enjoyable. It will also contribute to attracting visitors to depopulated areas, thereby revitalizing the region.

[0084] The travel plan proposal system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a location information unit. The reception unit accepts user input. The user input may include, but is not limited to, text input, voice input, image input, and the like. The reception unit accepts, for example, user input such as "travel from Tokyo to Osaka by car." The analysis unit analyzes the information accepted by the reception unit. The analysis may be performed using, for example, but is not limited to, data mining, natural language processing, statistical analysis, and the like. For example, the analysis unit selects optimal detour spots based on the travel route entered by the user. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposals may include, for example, travel plans, tourist spots, restaurants, and the like, but are not limited to, the like. For example, the proposal unit proposes delicious local dishes that the user can stop by during their travel. The location information unit uses real-time location information. The real-time location information may be obtained using, for example, but is not limited to, GPS, Wi-Fi location information, mobile base station information, and the like. For example, the location information unit may suggest optimal detour spots based on the user's current location. As a result, the travel plan suggestion system according to the embodiment suggests appropriate travel plans based on user input and utilizes real-time location information, thereby increasing the enjoyment of travel and contributing to regional revitalization.

[0085] The travel plan suggestion system includes a learning unit that learns the user's past travel history and preferences. The learning unit learns the user's past travel history and preferences. The past travel history and preferences include, but are not limited to, places visited, accommodations, and food preferences. For example, the learning unit collects data on tourist spots and restaurants visited by the user in the past to learn the user's preferences. The learning unit can also suggest similar plans based on travel plans that the user has previously preferred. Furthermore, the learning unit can improve the accuracy of suggestions based on the user's past feedback. For example, the learning unit prioritizes suggestions based on spots that the user has previously rated highly. This allows for more personalized suggestions by learning the user's past travel history and preferences.

[0086] The travel plan proposal system includes a collaboration unit that collaborates with local tourist associations or restaurants. The collaboration unit collaborates with local tourist associations or restaurants. Collaboration includes, but is not limited to, methods such as API collaboration, data sharing, and joint promotion. For example, the collaboration unit collaborates with local tourist associations to provide information on tourist spots. The collaboration unit can also collaborate with local restaurants to suggest delicious local dishes. Furthermore, the collaboration unit can also collaborate with local tourist associations and restaurants to revitalize the region. For example, the collaboration unit can hold events in collaboration with the tourist association to attract tourists. In this way, collaboration with local tourist associations and restaurants contributes to revitalizing the region.

[0087] The travel plan proposal system includes a collection unit that collects information using crowdsourcing. The collection unit collects information using crowdsourcing. Crowdsourcing includes, but is not limited to, examples of task distribution methods, reward settings, and data collection methods. For example, the collection unit uses crowdsourcing to collect the latest information on tourist spots and restaurants. The collection unit can also use crowdsourcing to collect user reviews and ratings. Furthermore, the collection unit can also use crowdsourcing to collect local event information. For example, the collection unit assigns tasks to users and collects photos and reviews of tourist spots. In this way, crowdsourcing can be used to collect the latest information and improve the accuracy of proposals.

[0088] The suggestion unit can select appropriate spots based on the user's travel route. The suggestion unit selects appropriate spots based on the user's travel route. The travel route may include, but is not limited to, GPS data, user input, and past travel history. For example, if the user inputs "travel from Tokyo to Osaka by car," the suggestion unit can suggest parking areas along the way or delicious local cuisine in locations a little further away. The suggestion unit can also select optimal tourist spots based on the user's travel route. Furthermore, the suggestion unit can suggest tourist spots and restaurants in depopulated areas based on the user's travel route. For example, the suggestion unit can suggest tourist spots that the user can stop by during their travel. This can increase the enjoyment of the trip by selecting optimal spots based on the user's travel route.

[0089] The location information unit can improve the accuracy of suggestions by using the user's real-time location information. The location information unit improves the accuracy of suggestions by using the user's real-time location information. Examples of real-time location information include, but are not limited to, GPS, Wi-Fi location information, and mobile base station information. For example, the location information unit can suggest optimal detour spots based on the user's current location. The location information unit can also acquire location information of the user while traveling in real time to improve the accuracy of suggestions. Furthermore, the location information unit can suggest tourist spots and restaurants in sparsely populated areas based on the user's location information. For example, the location information unit suggests tourist spots that the user can stop by while traveling. In this way, by using real-time location information, the accuracy of suggestions is improved, improving the user's travel experience.

[0090] The reception unit can estimate a user's emotion and adjust the input method for the destination and the mode of travel based on the estimated user emotion. The reception unit can estimate a user's emotion and adjust the input method for the destination and the mode of travel based on the estimated user emotion. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Also, 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 the user to quickly input the destination and the mode of travel. For example, the reception unit can automatically set the destination and the mode of travel by the user simply inputting "from Tokyo to Osaka by car" by voice. This allows the user to provide a more comfortable input experience by adjusting the input method according to the user's emotion.

[0091] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit analyzes the user's past input history and suggest the optimal input method. The past input history includes, but is not limited to, past search history, input content, and frequency of use. For example, the reception unit automatically displays destinations and transportation methods that the user has frequently input 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. Furthermore, the reception unit can predict and suggest destinations and transportation methods to be used during a specific time period based on the user's past input history. For example, the reception unit suggests optimal candidate locations based on places the user has visited in the past on a specific day of the week or time period. In this way, the optimal input method can be suggested to the user by analyzing the past input history.

[0092] The reception unit can simplify the input process by automatically acquiring the user's current location information when inputting a destination and a mode of transportation. The reception unit can simplify the input process by automatically acquiring the user's current location information when inputting a destination and a mode of transportation. Current location information includes, but is not limited to, GPS, Wi-Fi location information, and mobile base station information. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the starting location. The reception unit can also suggest optimal candidate locations by taking into account the distance from the current location when the user inputs a destination. Furthermore, when the user uses the app while traveling, the reception unit can update the user's current location in real time and reflect it as the starting location. For example, when the user uses the app while traveling, the reception unit acquires the user's current location in real time and suggests an optimal travel route. This simplifies the input process by automatically acquiring the current location information, thereby improving user convenience.

[0093] The reception unit may support voice input or gesture input when inputting a destination and a method of travel. The reception unit supports voice input or gesture input when inputting a destination and a method of travel. Voice input or gesture input includes, but is not limited to, voice recognition technology and gesture recognition technology. For example, the reception unit automatically sets the destination and the method of travel when the user simply voice-inputs, "From Tokyo to Osaka by car." The reception unit may also allow the user to easily set the destination and the method of travel by performing specific gestures on the smartphone screen. Furthermore, the reception unit may combine voice input and gesture input to more intuitively set the destination and the method of travel. For example, the reception unit may suggest an optimal input method when the user combines voice input and gesture input to set the destination and the method of travel. Thus, by supporting voice input or gesture input, a more intuitive input method can be provided.

[0094] The reception unit can estimate a user's emotion and adjust the design of the input interface based on the estimated user's emotion. The reception unit can estimate a user's emotion and adjust the design of the input interface based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. Also, if the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. For example, if the user is tired, the reception unit can provide an interface with a simple design to enable input work to be performed efficiently. In this way, by adjusting the interface design according to the user's emotion, a more comfortable input experience can be provided.

[0095] The reception unit can automatically suggest candidate locations by referring to the user's past travel history when the user inputs a destination and a travel method. The reception unit automatically suggests candidate locations by referring to the user's past travel history when the user inputs a destination and a travel method. The past travel history includes, but is not limited to, visited places, means of travel, and travel time. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidate locations. The reception unit can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. Furthermore, the reception unit can analyze the user's past travel patterns and suggest optimal candidate locations. For example, the reception unit suggests optimal candidate locations based on places that the user has visited on specific days of the week or during specific time periods in the past. In this way, the optimal candidate locations can be suggested to the user by referring to the past travel history.

[0096] The reception unit can make a suggestion based on the schedule by referring to the user's calendar information when the destination and the mode of transportation are input. The reception unit can make a suggestion based on the schedule by referring to the user's calendar information when the destination and the mode of transportation are input. The calendar information includes, for example, the type of schedule, the time of the schedule, the location of the schedule, etc., but is not limited to these examples. For example, the reception unit can refer to the schedule registered in the user's calendar and automatically set the destination and the mode of transportation. The reception unit can also suggest locations related to a specific event as candidate locations based on the user's calendar information. Furthermore, the reception unit can suggest an optimal route based on the schedule based on the user's calendar information. For example, the reception unit can suggest locations related to a specific event as candidate locations based on the user's calendar information. This makes it possible to make optimal suggestions based on the schedule by referring to the calendar information.

[0097] The reception unit can analyze the user's social media activity and suggest related places when the user inputs a destination and a mode of transportation. The reception unit analyzes the user's social media activity and suggests related places when the user inputs a destination and a mode of transportation. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit suggests places where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidate locations. Furthermore, the reception unit can suggest related places by referring to the activities of the user's friends on social media. For example, the reception unit suggests related places as candidate locations based on the activities of the user's friends on social media. In this way, it is possible to suggest related places to the user by analyzing social media activity.

[0098] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. Emotion estimation can be performed using methods such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and suggest more detour spots. Furthermore, if the user is in a hurry, the analysis unit can prioritize the shortest route and suggest the minimum number of detour spots. Furthermore, if the user is excited, the analysis unit can prioritize suggesting visually appealing spots. For example, if the user is excited, the analysis unit prioritizes suggesting visually appealing spots. This allows the analysis algorithm to be adjusted according to the user's emotions, thereby providing more appropriate analysis results.

[0099] The analysis unit can adjust the level of detail of the analysis based on the importance of the trip during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the trip during the analysis. The importance of the trip includes, but is not limited to, the purpose of the trip, the duration of the trip, and the budget for the trip. For example, the analysis unit may perform a detailed analysis in the case of an important trip and suggest multiple detour spots. In addition, the analysis unit may perform a simplified analysis in the case of a short trip and suggest only the main spots. Furthermore, in the case of a family trip, the analysis unit may prioritize suggesting spots that the whole family can enjoy. For example, in the case of a family trip, the analysis unit prioritizes suggesting spots that the whole family can enjoy. In this way, by adjusting the level of detail of the analysis based on the importance of the trip, more appropriate suggestions can be made.

[0100] The analysis unit can apply different analysis algorithms depending on the travel category during analysis. The analysis unit applies different analysis algorithms depending on the travel category during analysis. Travel categories include, but are not limited to, sightseeing trips, business trips, and family trips, for example. For example, in the case of sightseeing trips, the analysis unit applies an algorithm that prioritizes suggesting tourist spots. In addition, in the case of business trips, the analysis unit can also apply an algorithm that prioritizes suggesting efficient travel routes. Furthermore, in the case of gourmet trips, the analysis unit can apply an algorithm that prioritizes suggesting delicious local dishes. For example, in the case of gourmet trips, the analysis unit prioritizes suggesting delicious local dishes. In this way, by applying different analysis algorithms depending on the travel category, more appropriate suggestions can be made.

[0101] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during the analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during the analysis. Past analysis results include, but are not limited to, the type of analysis result, the accuracy of the analysis result, and how the analysis result is used. For example, the analysis unit can suggest similar spots based on spots that the user has previously liked. The analysis unit can also analyze the user's past travel history and suggest optimal detour spots. Furthermore, the analysis unit can reflect the user's past feedback and adjust the analysis algorithm. For example, the analysis unit can adjust the analysis algorithm based on the user's past feedback. By referring to past analysis results, the accuracy of the analysis can be improved, enabling more appropriate suggestions.

[0102] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, by adjusting the display method according to the user's emotion, more appropriate analysis results can be provided.

[0103] During analysis, the analysis unit can determine the priority of analysis based on the time of trip submission. During analysis, the analysis unit determines the priority of analysis based on the time of trip submission. The time of trip submission includes, but is not limited to, for example, the submission date, submission time, and submission frequency. For example, the analysis unit prioritizes analysis for the most recent trip and quickly makes suggestions. In addition, the analysis unit can perform a detailed analysis and make multiple suggestions for long-term travel plans. Furthermore, in the case of spontaneous trips, the analysis unit can perform a simplified analysis and suggest only major spots. For example, the analysis unit suggests only major spots for spontaneous trips. In this way, by determining the priority of analysis based on the time of trip submission, quick and appropriate suggestions are possible.

[0104] The analysis unit can adjust the order of analysis based on the relevance of the trip during analysis. The analysis unit adjusts the order of analysis based on the relevance of the trip during analysis. Travel relevance includes, but is not limited to, the purpose of the trip, the location of the trip, and the duration of the trip. For example, the analysis unit prioritizes analysis and suggestions of major tourist spots. The analysis unit can also prioritize analysis of highly relevant spots based on the user's interests. Furthermore, the analysis unit can perform analysis in an efficient order based on the user's travel route. For example, the analysis unit performs analysis in an efficient order based on the user's travel route. As a result, adjusting the order of analysis based on the relevance of the trip enables efficient and appropriate suggestions.

[0105] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, if the user is a tourism novice, the analysis unit can display the analysis results in simple language. Furthermore, if the user is knowledgeable about tourism, the analysis unit can display the analysis results using detailed technical terms. Furthermore, the analysis unit can adjust the use of optimal technical terms based on the user's past feedback. For example, the analysis unit adjusts the use of optimal technical terms based on the user's past feedback. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0106] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, when the user is relaxed, the suggestion unit can make detailed suggestions and provide multiple options. Furthermore, when the user is in a hurry, the suggestion unit can make concise suggestions and provide only key options. Furthermore, when the user is excited, the suggestion unit can make visually appealing suggestions and prioritize suggesting interesting spots. For example, when the user is excited, the suggestion unit can make visually appealing suggestions and prioritize suggesting interesting spots. This allows more appropriate suggestions to be made by adjusting the way suggestions are expressed based on the user's emotions.

[0107] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the spot when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on the importance of the spot when making a suggestion. The importance of a spot includes, but is not limited to, the popularity of a tourist spot, historical value, and ease of access. For example, the suggestion unit can provide detailed information and suggest multiple options for an important spot. Furthermore, the suggestion unit can provide concise information and suggest only the main options for a spot that can be visited in a short time. Furthermore, the suggestion unit can prioritize providing information that the whole family can enjoy for a family spot. For example, the suggestion unit prioritizes providing information that the whole family can enjoy for a family spot. In this way, adjusting the level of detail of the suggestion based on the importance of the spot enables more appropriate suggestions.

[0108] The suggestion unit can apply different suggestion algorithms depending on the category of the spot when making a suggestion. The suggestion unit applies different suggestion algorithms depending on the category of the spot when making a suggestion. Spot categories include, for example, tourist spots, restaurants, accommodations, etc., but are not limited to these examples. For example, the suggestion unit applies a suggestion algorithm specialized for tourism in the case of a tourist spot. Furthermore, the suggestion unit can also apply a suggestion algorithm specialized for gourmet in the case of a dining spot. Furthermore, the suggestion unit can also apply a suggestion algorithm specialized for shopping in the case of a shopping spot. For example, the suggestion unit applies a suggestion algorithm specialized for shopping in the case of a shopping spot. In this way, by applying different suggestion algorithms depending on the category of the spot, more appropriate suggestions can be made.

[0109] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. Past suggestion results include, but are not limited to, the type of suggestion result, the accuracy of the suggestion result, and how the suggestion result is used. For example, the suggestion unit can suggest similar spots based on spots that the user has previously liked. The suggestion unit can also analyze the user's past travel history to suggest optimal detour spots. Furthermore, the suggestion unit can reflect the user's past feedback and adjust the suggestion algorithm. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past feedback. By referring to past suggestion results, the accuracy of suggestions can be improved, enabling more appropriate suggestions.

[0110] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, if the user is in a hurry, the suggestion unit can make a short and to-the-point suggestion. Also, if the user is relaxed, the suggestion unit can make a longer suggestion including detailed explanations. Furthermore, if the user is excited, the suggestion unit can make a suggestion with a visually stimulating effect. For example, if the user is excited, the suggestion unit can make a suggestion with a visually stimulating effect. This allows for more appropriate suggestions to be made by adjusting the length of the suggestion according to the user's emotion.

[0111] The suggestion unit may determine the priority of the suggestions based on the time of submission of the spots when making the suggestions. The suggestion unit may determine the priority of the suggestions based on the time of submission of the spots when making the suggestions. The time of submission of the spots includes, but is not limited to, for example, the submission date, the submission time, and the submission frequency. For example, the suggestion unit may prioritize suggestions for upcoming spots and provide information quickly. For long-term spots, the suggestion unit may also provide detailed suggestions and multiple options. Furthermore, for unexpected spots, the suggestion unit may provide simplified suggestions and only major options. For example, the suggestion unit may provide only major options for unexpected spots. This allows for prompt and appropriate suggestions by determining the priority of the suggestions based on the time of submission of the spots.

[0112] The suggestion unit can adjust the order of suggestions based on the relevance of the spots when making suggestions. The suggestion unit can adjust the order of suggestions based on the relevance of the spots when making suggestions. The relevance of the spots includes, but is not limited to, for example, the location of the spots, the theme of the spots, and the popularity of the spots. For example, the suggestion unit can prioritize suggestions of major tourist spots and provide detailed information. The suggestion unit can also prioritize suggestions of highly relevant spots based on the user's interests. Furthermore, the suggestion unit can make suggestions in an efficient order based on the user's travel route. For example, the suggestion unit makes suggestions in an efficient order based on the user's travel route. In this way, adjusting the order of suggestions based on the relevance of the spots enables efficient and appropriate suggestions.

[0113] The suggestion unit may adjust the use of technical terminology in the suggestion according to the user's level of expertise when making a suggestion. The suggestion unit may adjust the use of technical terminology in the suggestion according to the user's level of expertise when making a suggestion. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, if the user is a tourism novice, the suggestion unit may display the suggestion results in simple language. Furthermore, if the user is knowledgeable about tourism, the suggestion unit may display the suggestion results using detailed technical terminology. Furthermore, the suggestion unit may adjust the use of optimal technical terminology based on the user's past feedback. For example, the suggestion unit adjusts the use of optimal technical terminology based on the user's past feedback. This allows for suggestions that are easier to understand by adjusting the use of technical terminology according to the user's level of expertise.

[0114] The location information unit can estimate the user's emotion and adjust the frequency of acquiring location information based on the estimated user's emotion. The location information unit can estimate the user's emotion and adjust the frequency of acquiring location information based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, the location information unit can set the frequency of acquiring location information low when the user is relaxed. Furthermore, the location information unit can set the frequency of acquiring location information high when the user is in a hurry. Furthermore, the location information unit can set the frequency of acquiring location information to medium when the user is excited. For example, the location information unit can set the frequency of acquiring location information to medium when the user is excited. In this way, by adjusting the frequency of acquiring location information according to the user's emotion, more appropriate location information can be provided.

[0115] When acquiring location information, the location information unit can select the optimal acquisition method by referring to the user's past movement history. When acquiring location information, the location information unit selects the optimal acquisition method by referring to the user's past movement history. The past movement history includes, for example, visited places, means of transportation, and travel time, but is not limited to these examples. For example, the location information unit selects the optimal acquisition method based on places the user has frequently visited in the past. The location information unit can also select an acquisition method that avoids congestion based on the user's past movement history. Furthermore, the location information unit can analyze the user's past movement history and select the most efficient acquisition method. For example, the location information unit selects the optimal acquisition method based on the user's past movement history. In this way, the optimal location information acquisition method can be selected by referring to the past movement history.

[0116] The location information unit can customize the acquisition means based on the user's current travel situation when acquiring location information. The location information unit customizes the acquisition means based on the user's current travel situation when acquiring location information. The current travel situation includes, but is not limited to, the travel means, travel speed, and travel direction. For example, if the user is traveling by car, the location information unit can acquire location information using an in-car GPS. Also, if the user is traveling on foot, the location information unit can acquire location information using a smartphone GPS. Furthermore, if the user is using public transportation, the location information unit can customize the acquisition means using location information of the transportation. For example, if the user is using public transportation, the location information unit customizes the acquisition means using location information of the transportation. In this way, by customizing the acquisition means based on the current travel situation, more appropriate location information can be provided.

[0117] The location information unit can improve the acquisition method by reflecting user feedback when acquiring location information. The location information unit improves the acquisition method by reflecting user feedback when acquiring location information. Feedback includes, but is not limited to, user ratings, comments, usage status, etc. For example, the location information unit improves the acquisition method when the user is dissatisfied with the accuracy of location information acquisition. Furthermore, the location information unit can adjust the acquisition frequency when the user is dissatisfied with the frequency of location information acquisition. Furthermore, the location information unit can suggest an optimal acquisition method based on user feedback. For example, the location information unit suggests an optimal acquisition method based on user feedback. In this way, the accuracy of the location information acquisition method is improved by reflecting user feedback.

[0118] The location information unit can estimate the user's emotion and adjust the display method of the location information based on the estimated user's emotion. The location information unit can estimate the user's emotion and adjust the display method of the location information based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, if the user is nervous, the location information unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the location information unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the location information unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the location information unit can provide a display method that focuses on the main points. In this way, by adjusting the display method according to the user's emotion, more appropriate location information can be provided.

[0119] The location information unit can select an optimal acquisition method by taking into consideration the user's geographical location information when acquiring location information. The location information unit can select an optimal acquisition method by taking into consideration the user's geographical location information when acquiring location information. Geographical location information includes, but is not limited to, GPS data, address information, map data, etc. For example, when the user is in an urban area, the location information unit can acquire location information by using Wi-Fi or cell tower information. Furthermore, when the user is in a suburban area, the location information unit can acquire location information by using GPS. Furthermore, when the user is in a mountainous area, the location information unit can acquire location information by using satellite communication. For example, when the user is in a mountainous area, the location information unit acquires location information by using satellite communication. In this way, the optimal location information acquisition method can be selected by taking into consideration the geographical location information.

[0120] The location information unit can analyze the user's social media activity when acquiring location information and suggest a means for acquiring location information. The location information unit can analyze the user's social media activity when acquiring location information and suggest a means for acquiring location information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the location information unit can suggest an optimal acquisition means based on the locations where the user checked in on social media. The location information unit can also analyze the content of the user's social media posts and acquire location information of related locations. Furthermore, the location information unit can acquire location information of related locations by referring to the activities of the user's friends on social media. For example, the location information unit acquires location information of related locations based on the activities of the user's friends on social media. In this way, the optimal location information acquisition means can be suggested by analyzing social media activity.

[0121] The location information unit can customize the acquisition method by reflecting the user's past feedback when acquiring location information. The location information unit customizes the acquisition method by reflecting the user's past feedback when acquiring location information. Feedback includes, but is not limited to, user ratings, comments, usage status, etc. For example, the location information unit suggests an optimal acquisition method based on acquisition methods that the user has preferred in the past. The location information unit can also adjust the acquisition frequency based on the user's past feedback. Furthermore, the location information unit can improve acquisition accuracy by reflecting the user's past feedback. For example, the location information unit suggests an optimal acquisition method based on the user's past feedback. In this way, the accuracy of the acquisition method is improved by reflecting the past feedback.

[0122] The learning unit can estimate the user's emotions and select learning data based on the estimated user emotions. The learning unit can estimate the user's emotions and select learning data based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, the learning unit can select detailed learning data when the user is relaxed. The learning unit can also select concise learning data when the user is in a hurry. Furthermore, the learning unit can select visually stimulating learning data when the user is excited. For example, the learning unit can select visually stimulating learning data when the user is excited. This enables more appropriate learning by selecting learning data according to the user's emotions.

[0123] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit optimizes the learning algorithm by referring to past learning data during learning. Past learning data includes, but is not limited to, past travel history, user preferences, feedback, etc. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also extract effective learning patterns from the past learning data and optimize the algorithm. Furthermore, the learning unit can analyze the past learning data and improve the accuracy of the learning algorithm. For example, the learning unit selects an optimal learning algorithm based on the past learning data. In this way, the accuracy of the learning algorithm is improved by referring to the past learning data.

[0124] The learning unit can analyze fluctuations in the user's travel history during learning and adjust the update frequency of the learning data. The learning unit can analyze fluctuations in the user's travel history during learning and adjust the update frequency of the learning data. Fluctuations in the travel history include, but are not limited to, for example, visit frequency, changes in visited locations, and changes in the purpose of travel. For example, the learning unit can set the update frequency of the learning data to be high if the user's travel history changes frequently. Furthermore, the learning unit can also set the update frequency of the learning data to be low if the user's travel history is stable. Furthermore, the learning unit can analyze the change pattern of the user's travel history and set an optimal update frequency. For example, the learning unit can set the update frequency of the learning data to be high if the user's travel history changes frequently. In this way, the update frequency of the learning data can be optimized by analyzing the fluctuations in the travel history.

[0125] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, text analysis, and the like, but is not limited to these examples. For example, the learning unit sets the learning frequency low when the user is relaxed. The learning unit can also set the learning frequency high when the user is in a hurry. Furthermore, the learning unit can set the learning frequency to medium when the user is excited. For example, the learning unit sets the learning frequency to medium when the user is excited. This allows for more appropriate learning by adjusting the learning frequency according to the user's emotions.

[0126] During learning, the learning unit can weight the learning data based on the time of submission of the travel history. During learning, the learning unit weights the learning data based on the time of submission of the travel history. The time of submission of the travel history includes, but is not limited to, for example, the submission date, the submission time, and the submission frequency. For example, the learning unit weights the learning data based on the most recent travel history. The learning unit can also weight the learning data based on long-term travel history. Furthermore, the learning unit can weight the learning data based on sudden travel history. For example, the learning unit weights the learning data based on the most recent travel history. In this way, weighting the learning data based on the time of submission of the travel history enables more appropriate learning.

[0127] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. The learning unit adjusts the learning algorithm by reflecting user feedback during learning. Feedback includes, for example, user ratings, comments, usage status, etc., but is not limited to these examples. For example, the learning unit optimizes the learning algorithm based on user feedback. The learning unit can also extract effective learning patterns from user feedback and adjust the algorithm. Furthermore, the learning unit can analyze user feedback and improve the accuracy of the learning algorithm. For example, the learning unit optimizes the learning algorithm based on user feedback. In this way, the accuracy of the learning algorithm is improved by reflecting user feedback.

[0128] The linking unit can estimate the user's emotions and select a tourism association or restaurant to link with based on the estimated user's emotions. The linking unit can estimate the user's emotions and select a tourism association or restaurant to link with based on the estimated user's emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, if the user is relaxed, the linking unit can select a tourism association or restaurant with a calm atmosphere. Furthermore, if the user is in a hurry, the linking unit can select a tourism association or restaurant that provides quick service. Furthermore, if the user is excited, the linking unit can select a lively tourism association or restaurant. For example, if the user is excited, the linking unit selects a lively tourism association or restaurant. This allows for more appropriate suggestions by selecting a link destination according to the user's emotions.

[0129] The collaboration unit can select the optimal collaboration method by referring to past collaboration data when collaborating. The collaboration unit selects the optimal collaboration method by referring to past collaboration data when collaborating. The collaboration data includes, for example, collaboration results, collaboration evaluations, collaboration history, etc., but is not limited to these examples. For example, the collaboration unit selects the optimal collaboration method based on past collaboration data. The collaboration unit can also extract effective collaboration patterns from the past collaboration data and optimize the collaboration method. Furthermore, the collaboration unit can analyze past collaboration data and improve the accuracy of the collaboration method. For example, the collaboration unit selects the optimal collaboration method based on past collaboration data. In this way, the accuracy of the collaboration method is improved by referring to the past collaboration data.

[0130] The linking unit can customize the linking content based on the user's current travel plan during linking. The linking unit customizes the linking content based on the user's current travel plan during linking. The current travel plan includes, but is not limited to, the purpose of the trip, the duration of the trip, and the travel budget. For example, the linking unit selects optimal tourist associations and restaurants based on the user's travel plan. The linking unit can also customize the linking content based on the user's travel plan and provide optimal services. Furthermore, the linking unit can analyze the user's travel plan and improve the accuracy of the linking content. For example, the linking unit selects optimal tourist associations and restaurants based on the user's travel plan. This enables more appropriate suggestions to be made by customizing the linking content based on the user's current travel plan.

[0131] The collaboration unit can improve the collaboration method by reflecting user feedback during collaboration. The collaboration unit improves the collaboration method by reflecting user feedback during collaboration. Feedback includes, for example, user ratings, comments, usage status, etc., but is not limited to these examples. For example, the collaboration unit optimizes the collaboration method based on user feedback. The collaboration unit can also extract effective collaboration patterns from user feedback and improve the collaboration method. Furthermore, the collaboration unit can analyze user feedback and improve the accuracy of the collaboration method. For example, the collaboration unit optimizes the collaboration method based on user feedback. In this way, the accuracy of the collaboration method is improved by reflecting user feedback.

[0132] The collaboration unit can estimate the user's emotion and determine a collaboration priority based on the estimated user's emotion. The collaboration unit estimates the user's emotion and determines a collaboration priority based on the estimated user's emotion. The emotion estimation is performed by, for example, but not limited to, methods such as facial expression recognition, voice analysis, and text analysis. For example, the collaboration unit sets the collaboration priority to low when the user is relaxed. The collaboration unit can also set the collaboration priority to high when the user is in a hurry. Furthermore, the collaboration unit can also set the collaboration priority to medium when the user is excited. For example, the collaboration unit sets the collaboration priority to medium when the user is excited. This enables more appropriate suggestions to be made by determining the collaboration priority according to the user's emotion.

[0133] The collaboration unit can select the optimal collaboration method in consideration of the user's geographical location information when collaborating. The collaboration unit selects the optimal collaboration method in consideration of the user's geographical location information when collaborating. Geographical location information includes, but is not limited to, GPS data, address information, map data, etc. For example, if the user is in an urban area, the collaboration unit selects a collaboration method specialized for urban areas. Furthermore, if the user is in a suburban area, the collaboration unit can also select a collaboration method specialized for suburban areas. Furthermore, if the user is in a mountainous area, the collaboration unit can select a collaboration method specialized for mountainous areas. For example, if the user is in a mountainous area, the collaboration unit selects a collaboration method specialized for mountainous areas. In this way, the optimal collaboration method can be selected by taking the geographical location information into consideration.

[0134] The linking unit can analyze the user's social media activity at the time of linking and suggest linking content. The linking unit analyzes the user's social media activity at the time of linking and suggesting linking content. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, the number of followers, etc. For example, the linking unit can suggest optimal linking content based on the location where the user checked in on social media. The linking unit can also analyze the content of the user's posts on social media and suggest related linking content. Furthermore, the linking unit can also suggest related linking content based on the activity of the user's friends on social media. For example, the linking unit can suggest related linking content based on the activity of the user's friends on social media. In this way, optimal linking content can be suggested by analyzing social media activity.

[0135] The collaboration unit can customize the collaboration method by reflecting the user's past feedback at the time of collaboration. The collaboration unit customizes the collaboration method by reflecting the user's past feedback at the time of collaboration. Feedback includes, for example, the user's ratings, comments, usage status, etc., but is not limited to these examples. For example, the collaboration unit proposes an optimal collaboration method based on the user's past feedback. The collaboration unit can also customize the collaboration content based on the user's past feedback. Furthermore, the collaboration unit can reflect the user's past feedback to improve the accuracy of the collaboration method. For example, the collaboration unit proposes an optimal collaboration method based on the user's past feedback. In this way, the accuracy of the collaboration method is improved by reflecting the past feedback.

[0136] The collection unit can estimate the user's emotions and adjust the information collection method based on the estimated user's emotions. The collection unit can estimate the user's emotions and adjust the information collection method based on the estimated user's emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, the collection unit can collect detailed information when the user is relaxed. Furthermore, the collection unit can collect concise information when the user is in a hurry. Furthermore, the collection unit can collect visually stimulating information when the user is excited. For example, the collection unit can collect visually stimulating information when the user is excited. In this way, more appropriate information can be collected by adjusting the information collection method according to the user's emotions.

[0137] The collection unit can select the optimal collection method by referring to past collected data when collecting information. The collection unit selects the optimal collection method by referring to past collected data when collecting information. The collected data includes, for example, the type of collected data, the accuracy of the collected data, and how the collected data is used, but is not limited to these examples. For example, the collection unit selects the optimal collection method based on the past collected data. The collection unit can also extract effective collection patterns from the past collected data and optimize the collection method. Furthermore, the collection unit can analyze the past collected data and improve the accuracy of the collection method. For example, the collection unit selects the optimal collection method based on the past collected data. In this way, the accuracy of the collection method is improved by referring to the past collected data.

[0138] The collection unit can customize the collected content based on the user's current travel plan when collecting information. The collection unit customizes the collected content based on the user's current travel plan when collecting information. The current travel plan includes, but is not limited to, for example, the purpose of the trip, the duration of the trip, and the travel budget. For example, the collection unit collects optimal information based on the user's travel plan. The collection unit can also customize the collected content based on the user's travel plan and provide optimal information. Furthermore, the collection unit can analyze the user's travel plan and improve the accuracy of the collected content. For example, the collection unit collects optimal information based on the user's travel plan. As a result, more appropriate information can be provided by customizing the collected content based on the current travel plan.

[0139] The collection unit can improve the collection method by reflecting user feedback when collecting information. The collection unit improves the collection method by reflecting user feedback when collecting information. Feedback includes, but is not limited to, user ratings, comments, usage status, etc. For example, the collection unit optimizes the collection method based on user feedback. The collection unit can also extract effective collection patterns from user feedback and improve the collection method. Furthermore, the collection unit can analyze user feedback and improve the accuracy of the collection method. For example, the collection unit optimizes the collection method based on user feedback. In this way, the accuracy of the collection method is improved by reflecting user feedback.

[0140] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, the collection unit can set the priority of information to be collected low when the user is relaxed. The collection unit can also set the priority of information to be collected high when the user is in a hurry. Furthermore, the collection unit can set the priority of information to be collected medium when the user is excited. For example, the collection unit can set the priority of information to be collected medium when the user is excited. In this way, more appropriate information can be collected by determining the priority of information according to the user's emotions.

[0141] The collection unit can select the optimal collection method in consideration of the user's geographical location information when collecting information. The collection unit selects the optimal collection method in consideration of the user's geographical location information when collecting information. Geographical location information includes, but is not limited to, GPS data, address information, map data, etc. For example, if the user is in an urban area, the collection unit selects a collection method specialized for urban areas. Furthermore, if the user is in a suburban area, the collection unit can also select a collection method specialized for suburban areas. Furthermore, if the user is in a mountainous area, the collection unit can select a collection method specialized for mountainous areas. For example, if the user is in a mountainous area, the collection unit selects a collection method specialized for mountainous areas. In this way, the optimal information collection method can be selected by taking the geographical location information into consideration.

[0142] The collection unit can analyze the user's social media activity when collecting information and suggest content to be collected. The collection unit analyzes the user's social media activity when collecting information and suggest content to be collected. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, the number of followers, etc. For example, the collection unit can suggest optimal content to be collected based on the location where the user checked in on social media. The collection unit can also analyze the content of posts on social media by collecting related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. For example, the collection unit collects related information based on the activities of the user's friends on social media. In this way, optimal information to be collected can be suggested by analyzing social media activity.

[0143] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. The collection unit customizes the collection method by reflecting the user's past feedback when collecting information. Feedback includes, for example, the user's ratings, comments, usage status, etc., but is not limited to these examples. For example, the collection unit suggests an optimal collection method based on the user's past feedback. The collection unit can also customize the collection content based on the user's past feedback. Furthermore, the collection unit can reflect the user's past feedback to improve the accuracy of the collection method. For example, the collection unit suggests an optimal collection method based on the user's past feedback. In this way, the accuracy of the collection method is improved by reflecting the past feedback. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, suggestion unit, location information unit, learning unit, linking unit, and collection unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts user input. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input information. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes a travel plan based on the analysis results. For example, the location information unit acquires real-time location information via the communication I / F 44 of the smart device 14 and suggests optimal detour spots based on the user's current location. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's past travel history and preferences. The linking unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collaborates with local tourist associations and restaurants. The collection unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and collects information using crowdsourcing. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, location information unit, learning unit, linking unit, and collection unit, 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 accepts voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a travel plan based on the analysis results. For example, the location information unit acquires real-time location information via the communication I / F 44 of the smart glasses 214 and suggests optimal detour spots based on the user's current location. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's past travel history and preferences. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collaborates with local tourist associations and restaurants. The collection unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and collects information using crowdsourcing. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, location information unit, learning unit, linking unit, and collection unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and accepts voice input from the user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input information. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes a travel plan based on the analysis results. For example, the location information unit acquires real-time location information via the communication I / F 44 of the headset terminal 314 and suggests optimal detour spots based on the user's current location. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's past travel history and preferences. The linking unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collaborates with local tourist associations and restaurants. The collection unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and collects information using crowdsourcing. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, location information unit, learning unit, collaboration unit, and collection unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and accepts voice input from the user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input information. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes a travel plan based on the analysis results. For example, the location information unit acquires real-time location information via the communication I / F 44 of the robot 414 and suggests optimal detour spots based on the user's current location. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's past travel history and preferences. The collaboration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collaborates with local tourist associations and restaurants. The collection unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and collects information using crowdsourcing.

[0144] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0145] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed and more detour spots can be suggested. If the user is in a hurry, the shortest route can be prioritized and a minimum number of detour spots can be suggested. Furthermore, if the user is excited, visually appealing spots can be prioritized and suggested. By adjusting the analysis algorithm according to the user's emotions, more appropriate analysis results can be provided.

[0146] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, detailed suggestions can be made and multiple options can be provided. If the user is in a hurry, concise suggestions can be made and only key options can be provided. Furthermore, if the user is excited, visually appealing suggestions can be made and interesting spots can be prioritized. This allows more appropriate suggestions to be made by adjusting the way suggestions are expressed according to the user's emotions.

[0147] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is nervous, a calm-colored interface can be provided to reduce visual stress. If the user is having fun, a bright-colored interface can be provided to make input work more enjoyable. Furthermore, if the user is tired, a simple, highly visible interface can be provided to make input work easier. In this way, a more comfortable input experience can be provided by adjusting the interface design according to the user's emotions.

[0148] The location information unit can estimate the user's emotions and adjust the frequency of location information acquisition based on the estimated user's emotions. For example, if the user is relaxed, the frequency of location information acquisition can be set low. If the user is in a hurry, the frequency of location information acquisition can be set high. Furthermore, if the user is excited, the frequency of location information acquisition can be set to a medium level. In this way, by adjusting the frequency of location information acquisition according to the user's emotions, more appropriate location information can be provided.

[0149] The learning unit can estimate the user's emotions and select learning data based on the estimated user emotions. For example, if the user is relaxed, detailed learning data can be selected. If the user is in a hurry, concise learning data can be selected. Furthermore, if the user is excited, visually stimulating learning data can be selected. This allows for more appropriate learning by selecting learning data according to the user's emotions.

[0150] The suggestion unit can suggest similar travel plans based on the user's past travel history. For example, it can collect data on tourist spots and restaurants visited by the user in the past and learn the user's preferences. It can also prioritize and suggest spots that the user has previously given high ratings. Furthermore, it can improve the accuracy of suggestions based on the user's past feedback. This allows the system to learn the user's past travel history and preferences, making it possible to make more personalized suggestions.

[0151] The Collaboration Department can revitalize the region by conducting joint promotions with local tourist associations and restaurants. For example, it can hold events in collaboration with the tourist association to attract tourists. It can also collaborate with local restaurants to propose delicious local dishes. It can also share data with tourist associations and restaurants to provide the latest information on tourist spots and restaurants. In this way, collaboration with local tourist associations and restaurants can contribute to the revitalization of the region.

[0152] The collection unit can use crowdsourcing to collect user word-of-mouth reviews and ratings. For example, it can assign tasks to users to collect photos and reviews of tourist spots. It can also collect information on local events. It can also collect information on the latest tourist spots and restaurants. In this way, by using crowdsourcing, it is possible to collect the latest information and improve the accuracy of suggestions.

[0153] The location information unit can customize the means for acquiring location information based on the user's current movement status. For example, if the user is traveling by car, the location information can be acquired using the in-car GPS. If the user is traveling on foot, the location information can also be acquired using the smartphone's GPS. Furthermore, if the user is using public transportation, the acquisition means can also be customized using the transportation's location information. In this way, by customizing the acquisition means based on the user's current movement status, more appropriate location information can be provided.

[0154] The learning unit can analyze fluctuations in the user's travel history and adjust the update frequency of the learning data. For example, if the user's travel history changes frequently, the update frequency of the learning data can be set high. Also, if the user's travel history is stable, the update frequency of the learning data can be set low. Furthermore, the learning unit can analyze the fluctuation patterns of the user's travel history and set an optimal update frequency. In this way, the update frequency of the learning data can be optimized by analyzing fluctuations in the travel history.

[0155] The processing flow of the second embodiment will be briefly explained below.

[0156] Step 1: The reception unit receives user input. User input includes text input, voice input, image input, etc. For example, the reception unit receives user input such as "travel from Tokyo to Osaka by car." Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as data mining, natural language processing, and statistical analysis. For example, the analysis unit selects optimal detour spots based on the travel route entered by the user. Step 3: The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. The suggestions include travel plans, tourist spots, restaurants, etc. For example, it suggests delicious local dishes that the user can stop by during their travels. Step 4: The location information module uses real-time location information. Real-time location information can be obtained using methods such as GPS, Wi-Fi location information, and mobile base station information. For example, the module suggests optimal detour spots based on the user's current location.

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0161] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0162] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0177] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0191] 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.

[0192] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0193] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0208] 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.

[0209] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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."

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Explanation of symbols]

[0229] 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 that receives input from a user; an analysis unit that analyzes the information received by the reception unit; a proposal unit that makes a proposal based on the analysis result obtained by the analysis unit; a location information unit that uses real-time location information; A system characterized by:

2. Equipped with a learning module that learns the user's past travel history and preferences 2. The system of claim 1.

3. Have a liaison department that coordinates with local tourist associations or restaurants 2. The system of claim 1.

4. Equipped with a collection department that collects information using crowdsourcing 2. The system of claim 1.

5. The proposal unit Selecting the appropriate spot based on the user's travel route 2. The system of claim 1.

6. The location information unit Use your real-time location to improve suggestions 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the destination and travel method input method based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

9. The reception unit Simplify input by automatically obtaining the user's current location when entering a destination and transportation method.

2. The system of claim 1.

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