System

Abstract

To provide a system for the automatic generation of an efficient travel plan in accordance with user preferences.SOLUTION: A system according to an embodiment comprises a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives travel preference information from a user. The collection unit collects related information from each database based on the information received by the reception unit. The analysis unit analyzes the information collected by the collection unit and generates an efficient travel plan. The provision unit provides the travel plan generated by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is difficult to automatically generate an efficient travel plan based on a user's wishes.

[0005] The system according to the embodiment aims to automatically generate an efficient travel plan based on the user's wishes. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives a user's desired travel information. The collection unit collects related information from each database based on the information received by the reception unit. The analysis unit analyzes the information collected by the collection unit and generates an efficient travel plan. The provision unit provides the travel plan generated by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can automatically generate an efficient travel plan based on the user's wishes. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The travel plan proposal system according to the embodiment of the present invention is a system that integrates an airplane ticket database, a train and bus database, a hotel reservation database, and a tourist information database of each region, and proposes an optimal travel plan based on the date (period), number of people, and budget input by the user. The travel plan proposal system can propose a more personalized trip by allowing the user to specify a place that the user always wants to go to and excluding places that the user has been to before. For example, the travel plan proposal system allows the user to input the desired date (period), number of people, and budget for the trip. This information is input to a reception unit of the system. Next, the reception unit analyzes the input information and collects related information from each database. For example, flight information is collected from the airplane ticket database, transportation information is collected from the train and bus database, accommodation information is collected from the hotel reservation database, and tourist destination information is collected from the tourist information database. The collected information is analyzed by an analysis unit, and a travel plan that is most suitable for the user's wishes is generated. For example, a combination of the most efficient means of transportation, accommodation, and tourist destinations within the budget is proposed. Also, if the user specifies a place that the user always wants to go to, a travel plan that includes that place is generated. Conversely, if the user excludes a place that the user has been to before, a travel plan that does not include that place is generated. The generated travel plan is provided to the user by the providing unit. The user can review the proposed travel plan and make corrections or additions as necessary. For example, the user can add specific tourist attractions or change accommodations. This system allows the user to easily create an optimal travel plan, significantly reducing the time and effort required for planning a trip. In addition, the personalized suggestions increase user satisfaction. This allows the travel plan suggestion system to suggest an optimal travel plan based on the user's travel desire information.

[0029] The travel plan proposal system according to the embodiment includes a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives a user's desired travel information. The desired travel information includes, for example, a travel destination, a budget, a schedule, a desired activity, and the like, but is not limited to such examples. The reception unit provides an interface for the user to input, for example, a desired travel date (period), the number of people, and a budget. The collection unit collects related information from each database based on the information received by the reception unit. The collection unit collects flight information, for example, from an airplane ticket database. The collection unit can also collect transportation information from a railway or bus database. Furthermore, the collection unit can also collect accommodation information from a hotel reservation database. The collection unit can also collect tourist destination information from a tourist information database. For example, the collection unit acquires flight information using an airline's API. From the railway or bus database, timetable data is acquired using a transportation API. From the hotel reservation database, accommodation data is acquired using an API of a hotel reservation site. From the tourist information database, official website data and travel guide data of tourist destinations are acquired. The analysis unit analyzes the information collected by the collection unit and generates an optimal travel plan. The analysis unit, for example, analyzes the most efficient combination of transportation means, accommodations, and tourist destinations within the budget based on the collected information. The analysis unit generates an optimal travel plan, for example, taking into consideration cost performance and shortening travel time. The provision unit provides the travel plan generated by the analysis unit. The provision unit, for example, provides an interface that displays the proposed travel plan to the user. The provision unit can also modify the travel plan according to the user's request. For example, the provision unit provides an interface that allows the user to add specific tourist destinations or change accommodations. As a result, the travel plan proposal system according to the embodiment can propose an optimal travel plan based on the user's travel desire information.

[0030] The reception unit receives the user's desired travel information. The desired travel information includes, but is not limited to, a travel destination, a budget, a schedule, and a desired activity. The reception unit provides, for example, an interface for the user to input the desired travel date (period), the number of people, and the budget. Specifically, the reception unit provides a web form or a mobile app interface designed to allow the user to intuitively operate it. This allows the user to easily input the desired travel information. Furthermore, the reception unit can also enable the user to input the desired travel information in natural language using voice input or a chatbot. For example, when the user inputs a request such as "I want to go on a hot spring trip with my family during the consecutive holidays next month" by voice, the reception unit analyzes the content and automatically extracts information such as the travel destination, schedule, and number of people. The reception unit also has a function of complementing the input content by taking into account the user's past travel history and preferences. For example, the reception unit can make appropriate suggestions to the user based on information on travel destinations that the user has visited in the past and information on accommodations that the user prefers to use. This allows the reception unit to efficiently and accurately receive the user's desired travel information.

[0031] The collection unit collects related information from each database based on the information received by the reception unit. The collection unit, for example, collects flight information from an airplane ticket database. The collection unit can also collect information on transportation means from a railway and bus database. Furthermore, the collection unit can collect information on accommodation facilities from a hotel reservation database. The collection unit can also collect information on tourist destinations from a tourist information database. For example, the collection unit acquires flight information using an airline API. From the railway and bus databases, timetable data is acquired using an API of the transportation facility. From the hotel reservation database, accommodation data is acquired using an API of a hotel reservation site. From the tourist information database, data on the official website of the tourist destination and travel guide is acquired. Specifically, the collection unit can establish a link with each database and acquire the latest information in real time. For example, the collection unit acquires flight availability and fare information using an airline API, and collects operation schedules and fare information using a railway and bus API. Furthermore, the collection unit acquires accommodation availability and fare information using an API of a hotel reservation site, and collects business hours, admission fees, event information, etc. of tourist destinations using data of the official website of the tourist destination and travel guide. This enables the collection unit to quickly and accurately collect necessary information based on the user's desired travel information.

[0032] The analysis unit analyzes the information collected by the collection unit and generates an optimal travel plan. For example, the analysis unit analyzes the most efficient combination of transportation, accommodation, and tourist destinations within the budget based on the collected information. For example, the analysis unit generates an optimal travel plan taking into account cost performance and shortening of travel time. Specifically, the analysis unit uses AI to analyze the collected information and generates a travel plan that best suits the user's wishes. For example, the AI ​​selects the most cost-effective flight and accommodation within the user's budget and proposes the optimal transportation means to minimize travel time. In addition, the AI ​​creates an efficient schedule taking into account the user's desired activities and tourist destinations. Furthermore, the analysis unit can utilize past data and statistical information to analyze the user's preferences and trends and propose more personalized travel plans. For example, based on the evaluation of tourist destinations and accommodations that have been visited in the past, new recommended tourist destinations and accommodations are proposed to the user. In addition, the analysis unit can continuously optimize the travel plan based on information updated in real time. For example, the travel plan can be instantly revised in response to flight delays or cancellations, changes in the operation status of transportation, etc. This enables the analysis unit to provide the user with an optimal travel plan and support a comfortable and efficient trip.

[0033] The providing unit provides the travel plan generated by the analyzing unit. The providing unit, for example, provides an interface that displays the travel plan proposed to the user. The providing unit can also modify the travel plan according to the user's request. For example, the providing unit provides an interface for the user to add a specific tourist attraction or change the accommodation. Specifically, the providing unit provides an intuitive interface for the user to check the travel plan and modify it as necessary. For example, through a web browser or a mobile app, the user can check the details of the travel plan and view information on flights, accommodations, and tourist attractions. The providing unit also has a function for enabling the user to customize the travel plan. For example, the user can provide an option to add a specific tourist attraction or change the accommodation, and modify the travel plan according to the user's request. Furthermore, the providing unit can collect user feedback and make improvements to improve the accuracy and satisfaction of the travel plan. For example, the user provides feedback through the providing unit after the trip, and the travel plan suggestion algorithm is improved based on the content. The providing unit also has a support function for responding to changes and cancellations of the travel plan. For example, the providing unit makes it easy to carry out procedures for responding to flight changes and accommodation cancellations. This enables the providing unit to respond flexibly and quickly to the user and provide a highly satisfying travel plan.

[0034] The reception unit may include a designation unit that accepts information specifying a place that the user always wants to go to. The designation unit, for example, provides an interface for the user to designate a place that the user always wants to go to. For example, the designation unit provides an input field for the user to designate a particular tourist spot, restaurant, event, etc. The designation unit registers the place specified by the user in the system and takes it into consideration when generating a travel plan. In this way, by designating a place that the user always wants to go to, a more personalized travel plan can be proposed.

[0035] The reception unit may include an exclusion unit that receives information to exclude places that the user has previously visited. The exclusion unit, for example, provides an interface for the user to exclude places that the user has previously visited. For example, the exclusion unit provides a field for the user to input past travel history. The exclusion unit registers the places specified by the user in the system and takes them into consideration when generating a travel plan. In this way, a more personalized travel plan can be proposed by excluding places that the user has previously visited.

[0036] The providing unit may include a modifying unit that modifies the travel plan according to a user's request. The modifying unit, for example, provides an interface for the user to modify the proposed travel plan. For example, the modifying unit provides an input field for the user to add a specific tourist attraction or change accommodation. The modifying unit modifies the travel plan based on the user's request and re-proposes it. In this way, a more flexible travel plan can be provided by modifying the travel plan according to the user's request.

[0037] The collection unit can collect information from an airplane ticket database, a train or bus database, a hotel reservation database, and a tourist guide database. For example, the collection unit collects flight information from the airplane ticket database. The collection unit can also collect transportation information from the train or bus database. The collection unit can also collect accommodation information from the hotel reservation database. The collection unit can also collect tourist destination information from the tourist guide database. For example, the collection unit acquires flight information using an airline's API. From the train or bus database, timetable data is acquired using a transportation API. From the hotel reservation database, accommodation data is acquired using an API of a hotel reservation site. From the tourist guide database, official website and travel guide data of tourist destinations are acquired. In this way, a comprehensive travel plan can be proposed by collecting information from various databases.

[0038] The analysis unit can analyze the most efficient combination of transportation means, accommodation facilities, and tourist destinations within the budget based on the collected information. The analysis unit, for example, analyzes the most efficient combination of transportation means, accommodation facilities, and tourist destinations within the budget based on the collected information. The analysis unit generates an optimal travel plan, for example, taking into consideration cost performance and shortening of travel time. For example, in selecting a transportation means, the analysis unit compares flight prices and travel time to select the optimal flight. In selecting an accommodation facility, the analysis unit selects the optimal accommodation facility by considering the price, location, rating, etc. In selecting a combination of tourist destinations, the optimal combination is selected by considering the popularity and accessibility of the tourist destinations. This makes it possible to propose the most efficient travel plan within the budget.

[0039] The reception unit can analyze the user's past travel history and suggest the optimal input method. The reception unit, for example, analyzes the user's past travel history and suggests the optimal input method. For example, travel desire information that the user frequently inputs in the past is automatically displayed as a candidate. Input methods (voice, text, etc.) that the user has used in the past are suggested preferentially. Travel desire information to be used in a specific time period is predicted and suggested from the user's past travel history. Thereby, input efficiency is improved by suggesting the optimal input method based on the user's past travel history.

[0040] The reception unit can filter input items based on the user's current living situation and areas of interest when inputting desired travel information. For example, the reception unit filters input items based on the user's current living situation and areas of interest when inputting desired travel information. For example, if the user is planning a family trip, travel plans for families are preferentially displayed. If the user is planning a business trip, travel plans for business are preferentially displayed. If the user has specific hobbies or interests, travel plans related to the hobbies and interests are preferentially displayed. This makes it possible to propose more appropriate travel plans by providing input items according to the user's living situation and areas of interest.

[0041] The reception unit can prioritize input of highly relevant information taking into consideration the user's geographical location information when inputting desired travel information. For example, the reception unit can prioritize input of highly relevant information taking into consideration the user's geographical location information when inputting desired travel information. For example, the user can prioritize input of a departure point close to the current location. The user can prioritize input of a means of transportation that is easily accessible from the current location. The user can prioritize input of tourist spots close to the current location. In this way, by inputting highly relevant information based on the user's geographical location information, a more appropriate travel plan can be proposed.

[0042] The reception unit can analyze the user's social media activity when inputting desired travel information and input related information. The reception unit can, for example, analyze the user's social media activity when inputting desired travel information and input related information. For example, the reception unit can prioritize inputting travel destinations shared by the user on social media. The reception unit can prioritize inputting tourist spots that the user follows on social media. The reception unit can prioritize inputting travel plans that the user has "liked" on social media. In this way, by inputting related information based on the user's social media activity, a more personalized travel plan can be proposed.

[0043] The collection unit can collect optimal information by referring to the user's past travel history at the time of collection. For example, the collection unit collects optimal information by referring to the user's past travel history at the time of collection. For example, the collection unit preferentially collects information on airlines used by the user in the past. The collection unit preferentially collects information on hotels where the user has stayed in the past. The collection unit preferentially collects information on tourist spots that the user has visited in the past. In this way, by collecting optimal information based on the user's past travel history, a more appropriate travel plan can be proposed.

[0044] The collection unit can customize the information to be collected based on the user's current living situation at the time of collection. The collection unit, for example, customizes the information to be collected based on the user's current living situation at the time of collection. For example, if the user is planning a family trip, family-oriented information is preferentially collected. If the user is planning a business trip, business-oriented information is preferentially collected. If the user has specific hobbies or interests, information related to the hobbies and interests is preferentially collected. In this way, by collecting information according to the user's living situation, more appropriate travel plans can be proposed.

[0045] The collection unit can collect optimal information taking into consideration the user's geographical location information at the time of collection. The collection unit, for example, collects optimal information taking into consideration the user's geographical location information at the time of collection. For example, the collection unit prioritizes collecting information on departure points close to the user's current location. The collection unit prioritizes collecting information on transportation methods that are easily accessible from the user's current location. The collection unit prioritizes collecting information on tourist spots close to the user's current location. In this way, by collecting optimal information based on the user's geographical location information, a more appropriate travel plan can be proposed.

[0046] The collection unit can analyze the user's social media activity and collect related information at the time of collection. For example, the collection unit analyzes the user's social media activity and collects related information at the time of collection. For example, the collection unit preferentially collects information on travel destinations shared by the user on social media. The collection unit preferentially collects information on tourist spots followed by the user on social media. The collection unit preferentially collects information on travel plans that the user has "liked" on social media. In this way, by collecting related information based on the user's social media activity, more personalized travel plans can be proposed.

[0047] The analysis unit can optimize the current travel plan by referring to past travel data during analysis. The analysis unit, for example, optimizes the current travel plan by referring to past travel data during analysis. For example, the analysis unit suggests the most suitable flight based on data on airlines used by the user in the past. The analysis unit suggests the most suitable accommodation based on data on hotels where the user has stayed in the past. The analysis unit suggests the most suitable tourist destination based on data on tourist destinations visited by the user in the past. In this way, a more suitable travel plan can be suggested by optimizing the current travel plan based on past travel data.

[0048] The analysis unit can apply different analysis algorithms for each travel category during analysis. For example, the analysis unit applies different analysis algorithms for each travel category during analysis. For example, in the case of a family trip, an analysis algorithm for families is applied. In the case of a business trip, an analysis algorithm for business is applied. In the case of a trip based on a hobby or interest, an analysis algorithm related to the hobby or interest is applied. In this way, by applying an analysis algorithm according to the travel category, a more appropriate travel plan can be proposed.

[0049] The analysis unit can determine the analysis priority based on the time of trip submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of trip submission during analysis. For example, if the trip submission is near, analysis is performed preferentially. If the trip submission is far away, detailed analysis is performed. The analysis schedule is adjusted based on the time of trip submission. In this way, by determining the analysis priority based on the time of trip submission, an efficient travel plan can be proposed.

[0050] The analysis unit can improve the accuracy of the analysis by referring to travel-related literature during analysis. The analysis unit, for example, improves the accuracy of the analysis by referring to travel-related literature during analysis. For example, an optimal travel plan is proposed based on travel-related literature. The analysis accuracy is improved by referring to travel-related literature. The analysis criteria are adjusted based on travel-related literature. In this way, the accuracy of the analysis is improved by referring to travel-related literature.

[0051] The providing unit can adjust the level of detail of the provision based on the importance of the travel plan at the time of provision. The providing unit, for example, adjusts the level of detail of the provision based on the importance of the travel plan at the time of provision. For example, for an important travel plan, detailed information is provided. For a travel plan with low importance, brief information is provided. The level of detail of the provision is adjusted based on the importance of the travel plan. This enables efficient information provision by adjusting the level of detail of the provision based on the importance of the travel plan.

[0052] The providing unit can apply different providing algorithms depending on the category of the travel plan at the time of providing. For example, the providing unit applies different providing algorithms depending on the category of the travel plan at the time of providing. For example, in the case of a family trip, a providing algorithm for families is applied. In the case of a business trip, a providing algorithm for business is applied. In the case of a trip based on a hobby or interest, a providing algorithm related to the hobby or interest is applied. In this way, by applying a providing algorithm according to the category of the travel plan, a more appropriate travel plan can be proposed.

[0053] The providing unit can determine the priority of provision based on the time of submission of the travel plan at the time of provision. The providing unit, for example, determines the priority of provision based on the time of submission of the travel plan at the time of provision. For example, if the submission date of the trip is approaching, the information is provided preferentially. If the submission date of the trip is far away, detailed information is provided. The provision schedule is adjusted based on the time of submission of the trip. In this way, efficient information provision is possible by determining the priority of provision based on the time of submission of the travel plan.

[0054] The providing unit can adjust the order of provision based on the relevance of the travel plan at the time of provision. The providing unit, for example, adjusts the order of provision based on the relevance of the travel plan at the time of provision. For example, information highly relevant to the travel plan is provided preferentially. Information less relevant to the travel plan is provided later. The order of provision is adjusted based on the relevance of the travel plan. In this way, efficient information provision is possible by adjusting the order of provision based on the relevance of the travel plan.

[0055] The designation unit can designate the optimum location by referring to the user's past travel history at the time of designation. The designation unit, for example, designates the optimum location by referring to the user's past travel history at the time of designation. For example, the designation unit designates the optimum location based on places the user has visited in the past. The designation unit designates places that are likely to be of interest to the user from the user's past travel history. The designation unit analyzes the user's past travel history and designates the most suitable location. In this way, a more appropriate travel plan can be proposed by designating the optimum location based on the user's past travel history.

[0056] The designation unit can designate an optimal location in consideration of the user's geographical location information at the time of designation. The designation unit, for example, designates an optimal location in consideration of the user's geographical location information at the time of designation. For example, the user preferentially designates a location close to the current location. The user preferentially designates a location that is easily accessible from the current location. The user preferentially designates a tourist spot close to the current location. In this way, a more appropriate travel plan can be proposed by designating an optimal location based on the user's geographical location information.

[0057] The exclusion unit can exclude the most suitable place by referring to the user's past travel history at the time of exclusion. For example, the exclusion unit excludes the most suitable place by referring to the user's past travel history at the time of exclusion. For example, the exclusion unit excludes the most suitable place based on the places the user has visited in the past. Places that the user was not interested in are excluded from the user's past travel history. The user's past travel history is analyzed and the most suitable place is excluded. In this way, a more suitable travel plan can be proposed by excluding the most suitable place based on the user's past travel history.

[0058] The exclusion unit can exclude optimal locations in consideration of the user's geographical location information at the time of exclusion. The exclusion unit, for example, excludes optimal locations in consideration of the user's geographical location information at the time of exclusion. For example, locations far from the user's current location are preferentially excluded. Locations that are difficult for the user to access from the user's current location are preferentially excluded. Tourist spots far from the user's current location are preferentially excluded. In this way, a more appropriate travel plan can be proposed by excluding optimal locations based on the user's geographical location information.

[0059] The correction unit can make optimal corrections by referring to the user's past travel history when making corrections. The correction unit, for example, makes optimal corrections by referring to the user's past travel history when making corrections. For example, the correction unit makes optimal corrections based on data on airlines used by the user in the past. The correction unit makes optimal corrections based on data on hotels where the user has stayed in the past. The correction unit makes optimal corrections based on data on tourist spots visited by the user in the past. In this way, a more appropriate travel plan can be proposed by making optimal corrections based on the user's past travel history.

[0060] The correction unit can make optimal corrections in consideration of the user's geographical location information at the time of correction. The correction unit, for example, makes optimal corrections in consideration of the user's geographical location information at the time of correction. For example, the correction unit gives priority to places close to the user's current location. The correction unit gives priority to places that are easily accessible from the user's current location. The correction unit gives priority to tourist spots close to the user's current location. In this way, a more appropriate travel plan can be proposed by making optimal corrections based on the user's geographical location information.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The reception unit can not only receive the user's travel request information, but also have a function of automatically importing the user's past travel history. For example, data from travel sites and apps that the user has used in the past can be imported, and information such as past travel destinations, accommodations, and transportation can be automatically acquired. This saves the user the trouble of manually inputting past travel history, and allows the user to make travel plans more quickly. In addition, based on the past travel history, the user's preferred travel style and patterns can be analyzed, and more personalized suggestions can be made. For example, for a user who has preferred to visit beach resorts in the past, similar resorts can be suggested. Furthermore, it is also possible to automatically exclude places and activities that the user wants to avoid based on the past travel history. This makes it possible to efficiently suggest travel plans that match the user's preferences and needs.

[0063] The designation unit can not only designate places that the user definitely wants to go to, but also have a function of automatically suggesting places that the user may be interested in by analyzing the user's social media activities. For example, information on tourist spots, restaurants, events, etc. that the user has "liked" on social media can be collected and incorporated into the travel plan. This saves the user the trouble of having to research it themselves and allows them to make more interesting travel plans. It is also possible to suggest travel plans that match the trends by referring to places visited by influencers and friends that the user follows. Furthermore, it is also possible to automatically exclude places and activities that the user wants to avoid from the user's social media activities. This makes it possible to suggest more personalized travel plans based on the user's interests and concerns.

[0064] The exclusion unit can not only exclude places that the user has previously visited, but also have a function of suggesting places to be excluded based on the user's current health condition and physical condition. For example, if the user has an allergy, areas and restaurants with a high content of the allergen can be excluded. In addition, if the user is feeling unwell or has a specific health problem, it is also possible to suggest a travel plan according to the situation. For example, if the user has back pain, nearby tourist spots can be preferentially suggested to avoid long travels. Furthermore, specific activities and tourist spots can be excluded based on the user's health condition. This makes it possible to suggest a safe and comfortable travel plan that takes the user's health condition into consideration.

[0065] The modification unit can not only modify the travel plan according to the user's request, but also have a function of collecting the user's real-time feedback and dynamically modifying the travel plan based on the feedback. For example, the user can provide feedback through the app during the trip, and the next tourist attractions and activities can be adjusted based on the feedback. If the user likes a particular tourist attraction, other nearby tourist attractions can be added. Also, if the user is not satisfied with a particular activity, an alternative activity can be suggested. Furthermore, based on the user's real-time feedback, changes to accommodations and transportation can be suggested. This makes the user's travel experience more flexible and optimized in real time.

[0066] The collection unit can not only collect information from various databases, but also have a function of acquiring the user's current geographical location information in real time and proposing an optimal travel plan based on that information. For example, the collection unit can preferentially collect and propose information on tourist spots, restaurants, events, etc. close to the user's current location. This allows the user to minimize travel time and enjoy sightseeing efficiently. It is also possible to propose transportation that is easily accessible from the user's current location. Furthermore, it is also possible to propose an optimal travel plan that takes into account weather and traffic conditions based on the user's current location. This makes it possible to provide a more appropriate travel plan according to the user's current situation.

[0067] The process flow of the first embodiment will be briefly described below.

[0068] Step 1: The reception unit receives the user's desired travel information. The desired travel information includes the travel destination, budget, schedule, desired activities, etc. The reception unit provides an interface for the user to input the desired travel date (period), number of people, and budget. Step 2: The collection unit collects related information from each database based on the information received by the reception unit. The collection unit collects flight information from an airline ticket database, transportation information from a train and bus database, accommodation information from a hotel reservation database, and tourist destination information from a tourist guide database. For example, the collection unit obtains flight information using an airline's API, timetable data using a transportation API, accommodation data using a hotel reservation site's API, and tourist destination official website and travel guide data. Step 3: The analysis unit analyzes the information collected by the collection unit and generates an optimal travel plan. Based on the collected information, the analysis unit analyzes the most efficient combination of transportation, accommodation, and tourist destinations within the budget, and generates an optimal travel plan that takes into account cost performance and shortening travel time. Step 4: The providing unit provides the itinerary generated by the analysis unit. The providing unit provides an interface that displays the proposed itinerary to the user, and can also modify the itinerary according to the user's request. For example, the providing unit provides an interface that allows the user to add specific tourist attractions or change accommodations.

[0069] (Example 2) The travel plan proposal system according to the embodiment of the present invention is a system that integrates an airplane ticket database, a train and bus database, a hotel reservation database, and a tourist information database of each region, and proposes an optimal travel plan based on the date (period), number of people, and budget input by the user. The travel plan proposal system can propose a more personalized trip by allowing the user to specify a place that the user always wants to go to and excluding places that the user has been to before. For example, the travel plan proposal system allows the user to input the desired date (period), number of people, and budget for the trip. This information is input to a reception unit of the system. Next, the reception unit analyzes the input information and collects related information from each database. For example, flight information is collected from the airplane ticket database, transportation information is collected from the train and bus database, accommodation information is collected from the hotel reservation database, and tourist destination information is collected from the tourist information database. The collected information is analyzed by an analysis unit, and a travel plan that is most suitable for the user's wishes is generated. For example, a combination of the most efficient means of transportation, accommodation, and tourist destinations within the budget is proposed. Also, if the user specifies a place that the user always wants to go to, a travel plan that includes that place is generated. Conversely, if the user excludes a place that the user has been to before, a travel plan that does not include that place is generated. The generated travel plan is provided to the user by the providing unit. The user can review the proposed travel plan and make corrections or additions as necessary. For example, the user can add specific tourist attractions or change accommodations. This system allows the user to easily create an optimal travel plan, significantly reducing the time and effort required for planning a trip. In addition, the personalized suggestions increase user satisfaction. This allows the travel plan suggestion system to suggest an optimal travel plan based on the user's travel desire information.

[0070] The travel plan proposal system according to the embodiment includes a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives a user's desired travel information. The desired travel information includes, for example, a travel destination, a budget, a schedule, a desired activity, and the like, but is not limited to such examples. The reception unit provides an interface for the user to input, for example, a desired travel date (period), the number of people, and a budget. The collection unit collects related information from each database based on the information received by the reception unit. The collection unit collects flight information, for example, from an airplane ticket database. The collection unit can also collect transportation information from a railway or bus database. Furthermore, the collection unit can also collect accommodation information from a hotel reservation database. The collection unit can also collect tourist destination information from a tourist information database. For example, the collection unit acquires flight information using an airline's API. From the railway or bus database, timetable data is acquired using a transportation API. From the hotel reservation database, accommodation data is acquired using an API of a hotel reservation site. From the tourist information database, official website data and travel guide data of tourist destinations are acquired. The analysis unit analyzes the information collected by the collection unit and generates an optimal travel plan. The analysis unit, for example, analyzes the most efficient combination of transportation means, accommodations, and tourist destinations within the budget based on the collected information. The analysis unit generates an optimal travel plan, for example, taking into consideration cost performance and shortening travel time. The provision unit provides the travel plan generated by the analysis unit. The provision unit, for example, provides an interface that displays the proposed travel plan to the user. The provision unit can also modify the travel plan according to the user's request. For example, the provision unit provides an interface that allows the user to add specific tourist destinations or change accommodations. As a result, the travel plan proposal system according to the embodiment can propose an optimal travel plan based on the user's travel desire information.

[0071] The reception unit receives the user's desired travel information. The desired travel information includes, but is not limited to, a travel destination, a budget, a schedule, and a desired activity. The reception unit provides, for example, an interface for the user to input the desired travel date (period), the number of people, and the budget. Specifically, the reception unit provides a web form or a mobile app interface designed to allow the user to intuitively operate it. This allows the user to easily input the desired travel information. Furthermore, the reception unit can also enable the user to input the desired travel information in natural language using voice input or a chatbot. For example, when the user inputs a request such as "I want to go on a hot spring trip with my family during the consecutive holidays next month" by voice, the reception unit analyzes the content and automatically extracts information such as the travel destination, schedule, and number of people. The reception unit also has a function of complementing the input content by taking into account the user's past travel history and preferences. For example, the reception unit can make appropriate suggestions to the user based on information on travel destinations that the user has visited in the past and information on accommodations that the user prefers to use. This allows the reception unit to efficiently and accurately receive the user's desired travel information.

[0072] The collection unit collects related information from each database based on the information received by the reception unit. The collection unit, for example, collects flight information from an airplane ticket database. The collection unit can also collect information on transportation means from a railway and bus database. Furthermore, the collection unit can collect information on accommodation facilities from a hotel reservation database. The collection unit can also collect information on tourist destinations from a tourist information database. For example, the collection unit acquires flight information using an airline API. From the railway and bus databases, timetable data is acquired using an API of the transportation facility. From the hotel reservation database, accommodation data is acquired using an API of a hotel reservation site. From the tourist information database, data on the official website of the tourist destination and travel guide is acquired. Specifically, the collection unit can establish a link with each database and acquire the latest information in real time. For example, the collection unit acquires flight availability and fare information using an airline API, and collects operation schedules and fare information using a railway and bus API. Furthermore, the collection unit acquires accommodation availability and fare information using an API of a hotel reservation site, and collects business hours, admission fees, event information, etc. of tourist destinations using data of the official website of the tourist destination and travel guide. This enables the collection unit to quickly and accurately collect necessary information based on the user's desired travel information.

[0073] The analysis unit analyzes the information collected by the collection unit and generates an optimal travel plan. For example, the analysis unit analyzes the most efficient combination of transportation, accommodation, and tourist destinations within the budget based on the collected information. For example, the analysis unit generates an optimal travel plan taking into account cost performance and shortening of travel time. Specifically, the analysis unit uses AI to analyze the collected information and generates a travel plan that best suits the user's wishes. For example, the AI ​​selects the most cost-effective flight and accommodation within the user's budget and proposes the optimal transportation means to minimize travel time. In addition, the AI ​​creates an efficient schedule taking into account the user's desired activities and tourist destinations. Furthermore, the analysis unit can utilize past data and statistical information to analyze the user's preferences and trends and propose more personalized travel plans. For example, based on the evaluation of tourist destinations and accommodations that have been visited in the past, new recommended tourist destinations and accommodations are proposed to the user. In addition, the analysis unit can continuously optimize the travel plan based on information updated in real time. For example, the travel plan can be instantly revised in response to flight delays or cancellations, changes in the operation status of transportation, etc. This enables the analysis unit to provide the user with an optimal travel plan and support a comfortable and efficient trip.

[0074] The providing unit provides the travel plan generated by the analyzing unit. The providing unit, for example, provides an interface that displays the travel plan proposed to the user. The providing unit can also modify the travel plan according to the user's request. For example, the providing unit provides an interface for the user to add a specific tourist attraction or change the accommodation. Specifically, the providing unit provides an intuitive interface for the user to check the travel plan and modify it as necessary. For example, through a web browser or a mobile app, the user can check the details of the travel plan and view information on flights, accommodations, and tourist attractions. The providing unit also has a function for enabling the user to customize the travel plan. For example, the user can provide an option to add a specific tourist attraction or change the accommodation, and modify the travel plan according to the user's request. Furthermore, the providing unit can collect user feedback and make improvements to improve the accuracy and satisfaction of the travel plan. For example, the user provides feedback through the providing unit after the trip, and the travel plan suggestion algorithm is improved based on the content. The providing unit also has a support function for responding to changes and cancellations of the travel plan. For example, the providing unit makes it easy to carry out procedures for responding to flight changes and accommodation cancellations. This enables the providing unit to respond flexibly and quickly to the user and provide a highly satisfying travel plan.

[0075] The reception unit may include a designation unit that accepts information specifying a place that the user always wants to go to. The designation unit, for example, provides an interface for the user to designate a place that the user always wants to go to. For example, the designation unit provides an input field for the user to designate a particular tourist spot, restaurant, event, etc. The designation unit registers the place specified by the user in the system and takes it into consideration when generating a travel plan. In this way, by designating a place that the user always wants to go to, a more personalized travel plan can be proposed.

[0076] The reception unit may include an exclusion unit that receives information to exclude places that the user has previously visited. The exclusion unit, for example, provides an interface for the user to exclude places that the user has previously visited. For example, the exclusion unit provides a field for the user to input past travel history. The exclusion unit registers the places specified by the user in the system and takes them into consideration when generating a travel plan. In this way, a more personalized travel plan can be proposed by excluding places that the user has previously visited.

[0077] The providing unit may include a modifying unit that modifies the travel plan according to a user's request. The modifying unit, for example, provides an interface for the user to modify the proposed travel plan. For example, the modifying unit provides an input field for the user to add a specific tourist attraction or change accommodation. The modifying unit modifies the travel plan based on the user's request and re-proposes it. In this way, a more flexible travel plan can be provided by modifying the travel plan according to the user's request.

[0078] The collection unit can collect information from an airplane ticket database, a train or bus database, a hotel reservation database, and a tourist guide database. For example, the collection unit collects flight information from the airplane ticket database. The collection unit can also collect transportation information from the train or bus database. The collection unit can also collect accommodation information from the hotel reservation database. The collection unit can also collect tourist destination information from the tourist guide database. For example, the collection unit acquires flight information using an airline's API. From the train or bus database, timetable data is acquired using a transportation API. From the hotel reservation database, accommodation data is acquired using an API of a hotel reservation site. From the tourist guide database, official website and travel guide data of tourist destinations are acquired. In this way, a comprehensive travel plan can be proposed by collecting information from various databases.

[0079] The analysis unit can analyze the most efficient combination of transportation means, accommodation facilities, and tourist destinations within the budget based on the collected information. The analysis unit, for example, analyzes the most efficient combination of transportation means, accommodation facilities, and tourist destinations within the budget based on the collected information. The analysis unit generates an optimal travel plan, for example, taking into consideration cost performance and shortening of travel time. For example, in selecting a transportation means, the analysis unit compares flight prices and travel time to select the optimal flight. In selecting an accommodation facility, the analysis unit selects the optimal accommodation facility by considering the price, location, rating, etc. In selecting a combination of tourist destinations, the optimal combination is selected by considering the popularity and accessibility of the tourist destinations. This makes it possible to propose the most efficient travel plan within the budget.

[0080] The reception unit can estimate the user's emotions and customize the input interface for travel desire information based on the estimated user's emotions. The reception unit, for example, estimates the user's emotions and customizes the input interface for travel desire information based on the estimated user's emotions. For example, when the user is stressed, a simple interface is provided and the input procedure is minimized. When the user is relaxed, detailed input options are provided and a customizable input method is suggested. When the user is in a hurry, voice input is prioritized so that the travel desire information can be input quickly. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, an input interface according to the user's emotions is provided, thereby improving the convenience of the user.

[0081] The reception unit can analyze the user's past travel history and suggest the optimal input method. The reception unit, for example, analyzes the user's past travel history and suggests the optimal input method. For example, travel desire information that the user frequently inputs in the past is automatically displayed as a candidate. Input methods (voice, text, etc.) that the user has used in the past are suggested preferentially. Travel desire information to be used in a specific time period is predicted and suggested from the user's past travel history. Thereby, input efficiency is improved by suggesting the optimal input method based on the user's past travel history.

[0082] The reception unit can filter input items based on the user's current living situation and areas of interest when inputting desired travel information. For example, the reception unit filters input items based on the user's current living situation and areas of interest when inputting desired travel information. For example, if the user is planning a family trip, travel plans for families are preferentially displayed. If the user is planning a business trip, travel plans for business are preferentially displayed. If the user has specific hobbies or interests, travel plans related to the hobbies and interests are preferentially displayed. This makes it possible to propose more appropriate travel plans by providing input items according to the user's living situation and areas of interest.

[0083] The reception unit can estimate the user's emotions and determine the priority of the travel desire information to be input based on the estimated user's emotions. The reception unit, for example, estimates the user's emotions and determines the priority of the travel desire information to be input based on the estimated user's emotions. For example, when the user is stressed, important information is input preferentially. When the user is relaxed, detailed information is input. When the user is in a hurry, minimum information is input preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, an efficient travel plan can be proposed by inputting information in a priority order according to the user's emotions.

[0084] The reception unit can prioritize input of highly relevant information taking into consideration the user's geographical location information when inputting desired travel information. For example, the reception unit can prioritize input of highly relevant information taking into consideration the user's geographical location information when inputting desired travel information. For example, the user can prioritize input of a departure point close to the current location. The user can prioritize input of a means of transportation that is easily accessible from the current location. The user can prioritize input of tourist spots close to the current location. In this way, by inputting highly relevant information based on the user's geographical location information, a more appropriate travel plan can be proposed.

[0085] The reception unit can analyze the user's social media activity when inputting desired travel information and input related information. The reception unit can, for example, analyze the user's social media activity when inputting desired travel information and input related information. For example, the reception unit can prioritize inputting travel destinations shared by the user on social media. The reception unit can prioritize inputting tourist spots that the user follows on social media. The reception unit can prioritize inputting travel plans that the user has "liked" on social media. In this way, by inputting related information based on the user's social media activity, a more personalized travel plan can be proposed.

[0086] The collection unit can estimate the user's emotion and determine the priority of information to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of information to be collected based on the estimated user's emotion. For example, when the user is stressed, important information is collected preferentially. When the user is relaxed, detailed information is collected. When the user is in a hurry, minimum information is collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, an efficient travel plan can be proposed by collecting information in a priority order according to the user's emotion.

[0087] The collection unit can collect optimal information by referring to the user's past travel history at the time of collection. For example, the collection unit collects optimal information by referring to the user's past travel history at the time of collection. For example, the collection unit preferentially collects information on airlines used by the user in the past. The collection unit preferentially collects information on hotels where the user has stayed in the past. The collection unit preferentially collects information on tourist spots that the user has visited in the past. In this way, by collecting optimal information based on the user's past travel history, a more appropriate travel plan can be proposed.

[0088] The collection unit can customize the information to be collected based on the user's current living situation at the time of collection. The collection unit, for example, customizes the information to be collected based on the user's current living situation at the time of collection. For example, if the user is planning a family trip, family-oriented information is preferentially collected. If the user is planning a business trip, business-oriented information is preferentially collected. If the user has specific hobbies or interests, information related to the hobbies and interests is preferentially collected. In this way, by collecting information according to the user's living situation, more appropriate travel plans can be proposed.

[0089] The collection unit can estimate the user's emotion and adjust the display method of the collected information based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the display method of the collected information based on the estimated user's emotion. For example, when the user is stressed, a simple display method is provided. When the user is relaxed, a detailed display method is provided. When the user is in a hurry, a display method that focuses on the main points is provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This improves user convenience by providing information in a display method according to the user's emotion.

[0090] The collection unit can collect optimal information taking into consideration the user's geographical location information at the time of collection. The collection unit, for example, collects optimal information taking into consideration the user's geographical location information at the time of collection. For example, the collection unit prioritizes collecting information on departure points close to the user's current location. The collection unit prioritizes collecting information on transportation methods that are easily accessible from the user's current location. The collection unit prioritizes collecting information on tourist spots close to the user's current location. In this way, by collecting optimal information based on the user's geographical location information, a more appropriate travel plan can be proposed.

[0091] The collection unit can analyze the user's social media activity and collect related information at the time of collection. For example, the collection unit analyzes the user's social media activity and collects related information at the time of collection. For example, the collection unit preferentially collects information on travel destinations shared by the user on social media. The collection unit preferentially collects information on tourist spots followed by the user on social media. The collection unit preferentially collects information on travel plans that the user has "liked" on social media. In this way, by collecting related information based on the user's social media activity, more personalized travel plans can be proposed.

[0092] The analysis unit can estimate the user's emotion and adjust the analysis criteria based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis criteria based on the estimated user's emotion. For example, if the user is stressed, a simple analysis criterion is applied. If the user is relaxed, a detailed analysis criterion is applied. If the user is in a hurry, a quick analysis criterion is applied. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, an efficient travel plan can be proposed by applying an analysis criterion according to the user's emotion.

[0093] The analysis unit can optimize the current travel plan by referring to past travel data during analysis. The analysis unit, for example, optimizes the current travel plan by referring to past travel data during analysis. For example, the analysis unit suggests the most suitable flight based on data on airlines used by the user in the past. The analysis unit suggests the most suitable accommodation based on data on hotels where the user has stayed in the past. The analysis unit suggests the most suitable tourist destination based on data on tourist destinations visited by the user in the past. In this way, a more suitable travel plan can be suggested by optimizing the current travel plan based on past travel data.

[0094] The analysis unit can apply different analysis algorithms for each travel category during analysis. For example, the analysis unit applies different analysis algorithms for each travel category during analysis. For example, in the case of a family trip, an analysis algorithm for families is applied. In the case of a business trip, an analysis algorithm for business is applied. In the case of a trip based on a hobby or interest, an analysis algorithm related to the hobby or interest is applied. In this way, by applying an analysis algorithm according to the travel category, a more appropriate travel plan can be proposed.

[0095] 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, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, when the user is stressed, a simple display method is provided. When the user is relaxed, a detailed display method is provided. When the user is in a hurry, a display method that focuses on the main points is provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This improves user convenience by providing the analysis result in a display method according to the user's emotion.

[0096] The analysis unit can determine the analysis priority based on the time of trip submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of trip submission during analysis. For example, if the trip submission is near, analysis is performed preferentially. If the trip submission is far away, detailed analysis is performed. The analysis schedule is adjusted based on the time of trip submission. In this way, by determining the analysis priority based on the time of trip submission, an efficient travel plan can be proposed.

[0097] The analysis unit can improve the accuracy of the analysis by referring to travel-related literature during analysis. The analysis unit, for example, improves the accuracy of the analysis by referring to travel-related literature during analysis. For example, an optimal travel plan is proposed based on travel-related literature. The analysis accuracy is improved by referring to travel-related literature. The analysis criteria are adjusted based on travel-related literature. In this way, the accuracy of the analysis is improved by referring to travel-related literature.

[0098] The providing unit can estimate the user's emotion and adjust the expression method of the travel plan to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the expression method of the travel plan to be provided based on the estimated user's emotion. For example, when the user is stressed, a simple expression method is provided. When the user is relaxed, a detailed expression method is provided. When the user is in a hurry, an expression method that focuses on the main points is provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the travel plan is provided using an expression method according to the user's emotion, thereby improving the convenience of the user.

[0099] The providing unit can adjust the level of detail of the provision based on the importance of the travel plan at the time of provision. The providing unit, for example, adjusts the level of detail of the provision based on the importance of the travel plan at the time of provision. For example, for an important travel plan, detailed information is provided. For a travel plan with low importance, brief information is provided. The level of detail of the provision is adjusted based on the importance of the travel plan. This enables efficient information provision by adjusting the level of detail of the provision based on the importance of the travel plan.

[0100] The providing unit can apply different providing algorithms depending on the category of the travel plan at the time of providing. For example, the providing unit applies different providing algorithms depending on the category of the travel plan at the time of providing. For example, in the case of a family trip, a providing algorithm for families is applied. In the case of a business trip, a providing algorithm for business is applied. In the case of a trip based on a hobby or interest, a providing algorithm related to the hobby or interest is applied. In this way, by applying a providing algorithm according to the category of the travel plan, a more appropriate travel plan can be proposed.

[0101] The providing unit can estimate the user's emotion and adjust the length of the travel plan to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the length of the travel plan to be provided based on the estimated user's emotion. For example, when the user is stressed, a short and to the point travel plan is provided. When the user is relaxed, a detailed travel plan is provided. When the user is in a hurry, a concise travel plan is provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This improves the user's convenience by providing a travel plan with a length according to the user's emotion.

[0102] The providing unit can determine the priority of provision based on the time of submission of the travel plan at the time of provision. The providing unit, for example, determines the priority of provision based on the time of submission of the travel plan at the time of provision. For example, if the submission date of the trip is approaching, the information is provided preferentially. If the submission date of the trip is far away, detailed information is provided. The provision schedule is adjusted based on the time of submission of the trip. In this way, efficient information provision is possible by determining the priority of provision based on the time of submission of the travel plan.

[0103] The providing unit can adjust the order of provision based on the relevance of the travel plan at the time of provision. The providing unit, for example, adjusts the order of provision based on the relevance of the travel plan at the time of provision. For example, information highly relevant to the travel plan is provided preferentially. Information less relevant to the travel plan is provided later. The order of provision is adjusted based on the relevance of the travel plan. In this way, efficient information provision is possible by adjusting the order of provision based on the relevance of the travel plan.

[0104] The designation unit can estimate the user's emotion and determine the priority of the locations to be designated based on the estimated user's emotion. The designation unit, for example, estimates the user's emotion and determines the priority of the locations to be designated based on the estimated user's emotion. For example, when the user is stressed, a location where the user can relax is designated preferentially. When the user is relaxed, an interesting location is designated preferentially. When the user is in a hurry, a location where the user can move efficiently is designated preferentially. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a more appropriate travel plan can be proposed by designating locations in a priority order according to the user's emotion.

[0105] The designation unit can designate the optimum location by referring to the user's past travel history at the time of designation. The designation unit, for example, designates the optimum location by referring to the user's past travel history at the time of designation. For example, the designation unit designates the optimum location based on places the user has visited in the past. The designation unit designates places that are likely to be of interest to the user from the user's past travel history. The designation unit analyzes the user's past travel history and designates the most suitable location. In this way, a more appropriate travel plan can be proposed by designating the optimum location based on the user's past travel history.

[0106] The designation unit can estimate the user's emotion and adjust the display method of the designated location based on the estimated user's emotion. The designation unit, for example, estimates the user's emotion and adjusts the display method of the designated location based on the estimated user's emotion. For example, when the user is stressed, a simple display method is provided. When the user is relaxed, a detailed display method is provided. When the user is in a hurry, a display method that focuses on the main points is provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the location is designated using a display method according to the user's emotion, thereby improving the user's convenience.

[0107] The designation unit can designate an optimal location in consideration of the user's geographical location information at the time of designation. The designation unit, for example, designates an optimal location in consideration of the user's geographical location information at the time of designation. For example, the user preferentially designates a location close to the current location. The user preferentially designates a location that is easily accessible from the current location. The user preferentially designates a tourist spot close to the current location. In this way, a more appropriate travel plan can be proposed by designating an optimal location based on the user's geographical location information.

[0108] The exclusion unit can estimate the user's emotion and determine the priority order of the places to be excluded based on the estimated user's emotion. The exclusion unit, for example, estimates the user's emotion and determines the priority order of the places to be excluded based on the estimated user's emotion. For example, when the user is stressed, crowded places are preferentially excluded. When the user is relaxed, places of no interest are preferentially excluded. When the user is in a hurry, places that are a long way around are preferentially excluded. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, it is possible to propose a more appropriate travel plan by excluding places in a priority order according to the user's emotion.

[0109] The exclusion unit can exclude the most suitable place by referring to the user's past travel history at the time of exclusion. For example, the exclusion unit excludes the most suitable place by referring to the user's past travel history at the time of exclusion. For example, the exclusion unit excludes the most suitable place based on the places the user has visited in the past. Places that the user was not interested in are excluded from the user's past travel history. The user's past travel history is analyzed and the most suitable place is excluded. In this way, a more suitable travel plan can be proposed by excluding the most suitable place based on the user's past travel history.

[0110] The exclusion unit can estimate the user's emotion and adjust the display method of the excluded location based on the estimated user's emotion. The exclusion unit, for example, estimates the user's emotion and adjusts the display method of the excluded location based on the estimated user's emotion. For example, when the user is stressed, a simple display method is provided. When the user is relaxed, a detailed display method is provided. When the user is in a hurry, a display method that focuses on the main points is provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the convenience of the user is improved by excluding the location using a display method according to the user's emotion.

[0111] The exclusion unit can exclude optimal locations in consideration of the user's geographical location information at the time of exclusion. The exclusion unit, for example, excludes optimal locations in consideration of the user's geographical location information at the time of exclusion. For example, locations far from the user's current location are preferentially excluded. Locations that are difficult for the user to access from the user's current location are preferentially excluded. Tourist spots far from the user's current location are preferentially excluded. In this way, a more appropriate travel plan can be proposed by excluding optimal locations based on the user's geographical location information.

[0112] The correction unit can estimate the user's emotion and determine the priority of the travel plan to be corrected based on the estimated user's emotion. The correction unit, for example, estimates the user's emotion and determines the priority of the travel plan to be corrected based on the estimated user's emotion. For example, if the user is stressed, important corrections are made with priority. If the user is relaxed, detailed corrections are made. If the user is in a hurry, quick corrections are made. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a more appropriate travel plan can be proposed by correcting the travel plan in a priority order according to the user's emotion.

[0113] The correction unit can make optimal corrections by referring to the user's past travel history when making corrections. The correction unit, for example, makes optimal corrections by referring to the user's past travel history when making corrections. For example, the correction unit makes optimal corrections based on data on airlines used by the user in the past. The correction unit makes optimal corrections based on data on hotels where the user has stayed in the past. The correction unit makes optimal corrections based on data on tourist spots visited by the user in the past. In this way, a more appropriate travel plan can be proposed by making optimal corrections based on the user's past travel history.

[0114] The correction unit can estimate the user's emotion and adjust the display method of the travel plan to be corrected based on the estimated user's emotion. The correction unit, for example, estimates the user's emotion and adjusts the display method of the travel plan to be corrected based on the estimated user's emotion. For example, when the user is stressed, a simple display method is provided. When the user is relaxed, a detailed display method is provided. When the user is in a hurry, a display method that focuses on the main points is provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the convenience of the user is improved by correcting the travel plan using a display method according to the user's emotion.

[0115] The correction unit can make optimal corrections in consideration of the user's geographical location information at the time of correction. The correction unit, for example, makes optimal corrections in consideration of the user's geographical location information at the time of correction. For example, the correction unit gives priority to places close to the user's current location. The correction unit gives priority to places that are easily accessible from the user's current location. The correction unit gives priority to tourist spots close to the user's current location. In this way, a more appropriate travel plan can be proposed by making optimal corrections based on the user's geographical location information.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The reception unit can not only receive the user's travel request information, but also have a function of automatically importing the user's past travel history. For example, data from travel sites and apps that the user has used in the past can be imported, and information such as past travel destinations, accommodations, and transportation can be automatically acquired. This saves the user the trouble of manually inputting past travel history, and allows the user to make travel plans more quickly. In addition, based on the past travel history, the user's preferred travel style and patterns can be analyzed, and more personalized suggestions can be made. For example, for a user who has preferred to visit beach resorts in the past, similar resorts can be suggested. Furthermore, it is also possible to automatically exclude places and activities that the user wants to avoid based on the past travel history. This makes it possible to efficiently suggest travel plans that match the user's preferences and needs.

[0118] The designation unit can not only designate places that the user definitely wants to go to, but also have a function of automatically suggesting places that the user may be interested in by analyzing the user's social media activities. For example, information on tourist spots, restaurants, events, etc. that the user has "liked" on social media can be collected and incorporated into the travel plan. This saves the user the trouble of having to research it themselves and allows them to make more interesting travel plans. It is also possible to suggest travel plans that match the trends by referring to places visited by influencers and friends that the user follows. Furthermore, it is also possible to automatically exclude places and activities that the user wants to avoid from the user's social media activities. This makes it possible to suggest more personalized travel plans based on the user's interests and concerns.

[0119] The exclusion unit can not only exclude places that the user has previously visited, but also have a function of suggesting places to be excluded based on the user's current health condition and physical condition. For example, if the user has an allergy, areas and restaurants with a high content of the allergen can be excluded. In addition, if the user is feeling unwell or has a specific health problem, it is also possible to suggest a travel plan according to the situation. For example, if the user has back pain, nearby tourist spots can be preferentially suggested to avoid long travels. Furthermore, specific activities and tourist spots can be excluded based on the user's health condition. This makes it possible to suggest a safe and comfortable travel plan that takes the user's health condition into consideration.

[0120] The modification unit can not only modify the travel plan according to the user's request, but also have a function of collecting the user's real-time feedback and dynamically modifying the travel plan based on the feedback. For example, the user can provide feedback through the app during the trip, and the next tourist attractions and activities can be adjusted based on the feedback. If the user likes a particular tourist attraction, other nearby tourist attractions can be added. Also, if the user is not satisfied with a particular activity, an alternative activity can be suggested. Furthermore, based on the user's real-time feedback, changes to accommodations and transportation can be suggested. This makes the user's travel experience more flexible and optimized in real time.

[0121] The collection unit can not only collect information from various databases, but also have a function of acquiring the user's current geographical location information in real time and proposing an optimal travel plan based on that information. For example, the collection unit can preferentially collect and propose information on tourist spots, restaurants, events, etc. close to the user's current location. This allows the user to minimize travel time and enjoy sightseeing efficiently. It is also possible to propose transportation that is easily accessible from the user's current location. Furthermore, it is also possible to propose an optimal travel plan that takes into account weather and traffic conditions based on the user's current location. This makes it possible to provide a more appropriate travel plan according to the user's current situation.

[0122] The analysis unit can not only generate an optimal travel plan based on the collected information, but also estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is stressed, a simple and relaxing travel plan can be preferentially suggested. If the user is relaxed, a detailed and comprehensive travel plan can be suggested. Also, if the user is in a hurry, it is possible to suggest a travel plan that allows quick travel. Emotions can be estimated, for example, using the user's input contents or facial expression recognition technology. This makes it possible to provide an optimal travel plan according to the user's emotions, thereby improving user satisfaction.

[0123] The reception unit not only receives the user's travel request information, but also estimates the user's emotions and customizes the input interface based on the estimated emotions. For example, when the user is stressed, a simple and intuitive interface can be provided to minimize the input procedure. When the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Also, when the user is in a hurry, it is possible to prioritize voice input and enable the user to quickly input the travel request information. The estimation of emotions can be performed, for example, using the user's input contents or facial expression recognition technology. As a result, an input interface according to the user's emotions can be provided, thereby improving the user's convenience.

[0124] The collection unit can not only collect information from various databases, but also estimate the user's emotions and determine the priority of the information to be collected based on the estimated emotions. For example, if the user is stressed, important information can be collected with priority, and if the user is relaxed, detailed information can be collected with priority. Also, if the user is in a hurry, it is possible to collect the minimum amount of information with priority. The emotions can be estimated, for example, using the contents of the user's input or facial expression recognition technology. As a result, efficient travel plans can be proposed by collecting information according to the user's emotions.

[0125] The analysis unit can not only generate an optimal travel plan based on the collected information, but also estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, a simple and intuitive display method can be provided, and if the user is relaxed, a detailed and comprehensive display method can be provided. Also, if the user is in a hurry, it is possible to provide a display method that focuses on the main points. The emotion can be estimated, for example, using the user's input content or facial expression recognition technology. This makes it possible to improve user convenience by providing the analysis results in a display method according to the user's emotions.

[0126] The providing unit can not only provide the travel plan generated by the analyzing unit, but also estimate the user's emotions and adjust the expression method of the travel plan to be provided based on the estimated emotions. For example, when the user is stressed, a simple and intuitive expression method can be provided, and when the user is relaxed, a detailed and comprehensive expression method can be provided. Also, when the user is in a hurry, it is possible to provide an expression method that focuses on the main points. The emotion can be estimated, for example, by using the contents input by the user or facial expression recognition technology. As a result, the travel plan can be provided in an expression method according to the user's emotions, thereby improving the convenience of the user.

[0127] The process flow of the second embodiment will be briefly described below.

[0128] Step 1: The reception unit receives the user's desired travel information. The desired travel information includes the travel destination, budget, schedule, desired activities, etc. The reception unit provides an interface for the user to input the desired travel date (period), number of people, and budget. Step 2: The collection unit collects related information from each database based on the information received by the reception unit. The collection unit collects flight information from an airline ticket database, transportation information from a train and bus database, accommodation information from a hotel reservation database, and tourist destination information from a tourist guide database. For example, the collection unit obtains flight information using an airline's API, timetable data using a transportation API, accommodation data using a hotel reservation site's API, and tourist destination official website and travel guide data. Step 3: The analysis unit analyzes the information collected by the collection unit and generates an optimal travel plan. Based on the collected information, the analysis unit analyzes the most efficient combination of transportation, accommodation, and tourist destinations within the budget, and generates an optimal travel plan that takes into account cost performance and shortening travel time. Step 4: The providing unit provides the itinerary generated by the analysis unit. The providing unit provides an interface that displays the proposed itinerary to the user, and can also modify the itinerary according to the user's request. For example, the providing unit provides an interface that allows the user to add specific tourist attractions or change accommodations.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0130] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0131] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and provides an interface for the user to input the desired date (period) of the trip, the number of people, and the budget. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects related information from each database. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to generate an optimal travel plan. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides an interface that displays the travel plan proposed to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The 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.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0140] Fig. 4 shows an example of 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.

[0141] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and provision 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 control unit 46A of the smart glasses 214, and provides an interface for the user to input the desired date (period) of the trip, the number of people, and the budget. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects related information from each database. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to generate an optimal travel plan. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides an interface that displays the proposed travel plan to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The 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.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0156] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0160] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] 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 a voice indicating a user input for 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.

[0162] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and provides an interface for the user to input the desired date (period) of the trip, the number of people, and the budget. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects related information from each database. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to generate an optimal travel plan. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides an interface that displays the travel plan proposed to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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, the RAM 30, and the 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.

[0168] 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.

[0169] 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 the voice according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0171] 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 performed in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0173] 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.

[0174] 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.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0176] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0177] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0178] 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 a voice indicating a user input for 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.

[0179] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0180] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0181] Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and provides an interface for the user to input the desired date (period) of the trip, the number of people, and the budget. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects related information from each database. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to generate an optimal travel plan. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides an interface that displays the travel plan proposed to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0183] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0185] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0186] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0187] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0189] 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.

[0190] 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 Universal Serial Bus (USB) 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.

[0191] In addition, 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 upon request from the data processing device 12.

[0192] 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.

[0193] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0194] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0195] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0196] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0198] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0200] (Appendix 1) A reception unit that receives a user's desired travel information; a collection unit that collects related information from each database based on the information received by the reception unit; an analysis unit that analyzes the information collected by the collection unit and generates an efficient travel plan; a providing unit that provides a travel plan generated by the analysis unit; Equipped A system characterized by: (Appendix 2) The reception unit is A designation unit is provided for accepting information designating a place where the user definitely wants to go. 2. The system of claim 1, (Appendix 3) The reception unit is An exclusion unit is provided that accepts information to exclude places that the user has previously visited. 2. The system of claim 1, (Appendix 4) The providing unit is A revision unit is provided to revise the travel plan according to the user's request. 2. The system of claim 1, (Appendix 5) The collecting unit includes: Collect information from airline ticket databases, train or bus databases, hotel reservation databases, tourist information databases 2. The system of claim 1, (Appendix 6) The analysis unit is Using the collected information, analyze the most efficient combination of transportation, accommodation, and sightseeing within your budget 2. The system of claim 1, (Appendix 7) The reception unit is To estimate a user's emotion and customize an input interface for travel desire information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The reception unit is Analyzes the user's past travel history and suggests the best way to input information 2. The system of claim 1, (Appendix 9) The reception unit is As users enter their travel preferences, filter the options based on their current living situation and interests 2. The system of claim 1, (Appendix 10) The reception unit is A user's emotions are estimated, and the priority of travel desire information to be input is determined based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The reception unit is When entering travel information, the system takes into account the user's geographic location and prioritizes the most relevant information. 2. The system of claim 1, (Appendix 12) The reception unit is When users enter their travel preferences, the app analyzes their social media activity and prompts them to enter relevant information. 2. The system of claim 1, (Appendix 13) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 14) The collecting unit includes: At the time of collection, the user's past travel history is referenced to collect the most appropriate information. 2. The system of claim 1, (Appendix 15) The collecting unit includes: Customize the information we collect based on your current life situation at the time of collection 2. The system of claim 1, (Appendix 16) The collecting unit includes: Inferring user emotions and adjusting the way information is displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The collecting unit includes: At the time of collection, the geographic location of the user is taken into consideration to collect the most appropriate information. 2. The system of claim 1, (Appendix 18) The collecting unit includes: At the time of collection, we analyze your social media activity to gather relevant information. 2. The system of claim 1, (Appendix 19) The analysis unit is Inferring user emotions and adjusting analysis criteria based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The analysis unit is During analysis, look at past trip data to optimize current trip plans 2. The system of claim 1, (Appendix 21) The analysis unit is During analysis, different analysis algorithms are applied to different categories of trips 2. The system of claim 1, (Appendix 22) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 23) The analysis unit is During analysis, prioritize analysis based on when the trip was submitted 2. The system of claim 1, (Appendix 24) The analysis unit is During analysis, refer to travel-related literature to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 25) The providing unit is To estimate a user's emotion and adjust a representation of a travel plan based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The providing unit is At the time of delivery, adjust the level of detail of the delivery based on the importance of the travel plan 2. The system of claim 1, (Appendix 27) The providing unit is At the time of serving, different serving algorithms are applied depending on the category of the travel plan 2. The system of claim 1, (Appendix 28) The providing unit is To estimate a user's emotion and adjust the length of a travel plan to be provided based on the estimated user's emotion. 2. The system of claim 1, (Appendix 29) The providing unit is When offers are made, they will be prioritized based on when travel plans are submitted. 2. The system of claim 1, (Appendix 30) The providing unit is At the time of serving, adjust the order of serving based on the relevance of the travel plan 2. The system of claim 1, (Appendix 31) The designation unit is Estimate the user's emotions and prioritize locations based on the estimated user's emotions. 3. The system of claim 2, (Appendix 32) The designation unit is When specified, the best location is selected by looking at the user's past travel history. 3. The system of claim 2, (Appendix 33) The designation unit is Infer a user's emotion and adjust the display method of the specified location based on the estimated user's emotion. 3. The system of claim 2, (Appendix 34) The designation unit is When specified, the optimal location is selected taking into account the user's geographic location. 3. The system of claim 2, (Appendix 35) The exclusion unit is Estimate user sentiment and prioritize locations to be excluded based on the estimated user sentiment. 4. The system of claim 3. (Appendix 36) The exclusion unit is When excluding places, look at the user's past travel history to exclude the best places. 4. The system of claim 3. (Appendix 37) The exclusion unit is Infer user sentiment and adjust the way in which excluded locations are displayed based on the estimated user sentiment 4. The system of claim 3. (Appendix 38) The exclusion unit is During exclusion, consider the user's geographic location to exclude optimal locations 4. The system of claim 3. (Appendix 39) The correction unit is To estimate a user's emotion and prioritize a travel plan to be revised based on the estimated user's emotion. 5. The system of claim 4. (Appendix 40) The correction unit is When making corrections, the system refers to the user's past travel history to make optimal corrections. 5. The system of claim 4. (Appendix 41) The correction unit is Inferring a user's emotions and adjusting the display method of a travel plan that is modified based on the estimated user's emotions 5. The system of claim 4. (Appendix 42) The correction unit is During revisions, the geographic location of the user is taken into account to make optimal revisions. 5. The system of claim 4. [Explanation of symbols]

[0201] 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 a user's desired travel information; a collection unit that collects related information from each database based on the information received by the reception unit; an analysis unit that analyzes the information collected by the collection unit and generates an efficient travel plan; a providing unit that provides a travel plan generated by the analysis unit; Equipped A system characterized by:

2. The reception unit is A designation unit is provided for accepting information designating a place where the user definitely wants to go.

2. The system of claim 1 .

3. The reception unit is An exclusion unit is provided that accepts information to exclude places that the user has previously visited.

2. The system of claim 1 .

4. The providing unit is A revision unit is provided to revise the travel plan according to the user's request.

2. The system of claim 1 .

5. The collecting unit includes: Collect information from airline ticket databases, train or bus databases, hotel reservation databases, tourist information databases 2. The system of claim 1 .

6. The analysis unit is Using the collected information, analyze the most efficient combination of transportation, accommodation, and sightseeing within your budget 2. The system of claim 1 .

7. The reception unit is To estimate a user's emotion and customize an input interface for travel desire information based on the estimated user's emotion.

2. The system of claim 1 .

8. The reception unit is Analyzes the user's past travel history and suggests the best way to input information 2. The system of claim 1 .

9. The reception unit is As users enter their travel preferences, filter the options based on their current living situation and interests 2. The system of claim 1 .

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