System

Abstract

An object of a system according to an embodiment is to generate a travel plan based on a desire of a user and utilize resources of a tourist spot to the maximum.SOLUTION: A system includes a reception unit, a generation unit, and a proposal unit. The reception part inputs desired conditions of travel. The generation unit analyzes the condition input by the reception unit and generates a travel plan. The suggestion unit maximally utilizes the resources of the tourist spot based on the travel plan generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to efficiently generate travel plans based on the user's wishes and to fully utilize the resources of tourist destinations.

[0005] The system according to the embodiment aims to generate a travel plan based on the user's wishes and to make the most of the resources of tourist destinations. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a proposal unit. The reception unit inputs desired travel conditions. The generation unit analyzes the conditions input by the reception unit and generates a travel plan. The proposal unit makes maximum use of the resources of tourist destinations based on the travel plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate a travel plan based on the user's wishes and make the most of the resources of tourist destinations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel suggestion system according to an embodiment of the present invention is a system in which a user inputs desired travel conditions, and a generation AI analyzes the input to generate an optimal travel plan, making maximum use of tourist destination resources. In the travel suggestion system, a user inputs desired travel conditions, and a generation AI analyzes the input to generate an optimal travel plan. This plan includes tourist destination attractions, accommodations, restaurants, and transportation options. Furthermore, the generation AI incorporates local events, local specialties, and local cultural experiences into the proposal to maximize the use of tourist destination resources. For example, a travel suggestion system allows a user to input desired travel conditions, such as travel destination, budget, duration, and activities of interest. The travel suggestion system then analyzes the input conditions and generates an optimal travel plan using a generation AI. The input to the generation AI is the user's desired conditions themselves, and the generation AI generates the travel plan based on the input. For example, the generation AI receives a prompt such as, "Please create an optimal travel plan based on these conditions," and creates the travel plan. The travel suggestion system then suggests tourist destination attractions, accommodations, restaurants, and transportation options based on the generated travel plan. Furthermore, the travel suggestion system also incorporates local events, local specialties, and local cultural experiences into its suggestions. For example, it suggests local festivals and concerts, and experiences with local specialties and traditional crafts. This allows the travel suggestion system to easily create travel plans that meet the user's wishes, and tourist destinations can effectively utilize their resources. This allows the travel suggestion system to generate optimal travel plans based on the user's wishes, and to make maximum use of tourist destination resources. For example, the user can have a fulfilling travel experience, and tourist destinations can effectively utilize their resources.

[0029] A travel suggestion system according to an embodiment includes a reception unit, a generation unit, and a suggestion unit. The reception unit receives input of desired travel conditions from a user. The desired travel conditions include, but are not limited to, travel destinations, budgets, travel duration, and activities of interest. The reception unit receives, for example, input of a travel destination from a user. The reception unit can also receive input of a user's budget. The reception unit can also receive input of a user's travel duration. The reception unit can also receive input of activities of interest to a user. The generation unit uses a generation AI to analyze the conditions input by the reception unit and generate a travel plan. The generation AI generates a travel plan using, for example, a text generation AI (e.g., LLM). The generation unit can also use the generation AI to analyze past travel data and user preference data to generate an optimal travel plan. The generation unit can also use the generation AI to generate a travel plan based on the user's conditions. For example, the generation AI receives input of the user's desired conditions and outputs an optimal travel plan. The suggestion unit makes suggestions that make the most of the resources of tourist destinations based on the travel plan generated by the generation unit. The suggestion unit may suggest, for example, attractions of a tourist destination. The suggestion unit may also suggest accommodations. The suggestion unit may also suggest restaurants. The suggestion unit may also suggest transportation options. The suggestion unit may also incorporate local events, local specialties, and local cultural experiences into the suggestion. For example, the suggestion unit may suggest local festivals and concerts. The suggestion unit may also suggest experiences of local specialties and traditional crafts. This allows the travel suggestion system according to the embodiment to generate an optimal travel plan based on the user's wishes and make maximum use of the resources of the tourist destination. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit may generate a travel plan using a generation AI model that receives the user's desired conditions as input and outputs an optimal travel plan. Some or all of the above-described processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI.For example, the suggestion unit can make suggestions using an AI model that takes the generated travel plan as input and outputs suggestions that make the most of the tourist destination's resources.

[0030] The reception unit allows the user to input desired conditions for a travel destination, budget, duration, and activities of interest. Travel destinations include, but are not limited to, domestic travel, international travel, and specific cities or regions. For example, the reception unit allows the user to input a domestic travel destination. The reception unit also allows the user to input a destination for international travel. The reception unit also allows the user to input a specific city or region destination. The budget includes, but is not limited to, a total budget and a daily budget. For example, the reception unit allows the user to input a total budget. The reception unit also allows the user to input a daily budget. The duration includes, but is not limited to, the number of days of the trip and a specific period of time. For example, the reception unit allows the user to input the number of days of the trip. The reception unit also allows the user to input a specific period of time. Interested activities include, but are not limited to, sports, cultural experiences, nature sightseeing, and the like. For example, the reception unit allows the user to input that the user is interested in sports. The reception unit also allows the user to input that the user is interested in cultural experiences. The reception unit can also input that the user is interested in nature sightseeing. This allows the user to input detailed desired conditions, thereby generating a more accurate travel plan. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the desired conditions using an AI model that inputs the desired conditions and analyzes the desired conditions.

[0031] The generation unit can analyze the input conditions using a generation AI and generate a travel plan. Examples of generation AI include, but are not limited to, machine learning models and neural networks. For example, the generation unit can analyze the input conditions and generate a travel plan using a machine learning model. The generation unit can also analyze the input conditions and generate a travel plan using a neural network. The generation unit can also analyze the input conditions and generate a travel plan using a generation AI to analyze the user's past travel data and preference data and generate an optimal travel plan. For example, the generation unit can generate a travel plan using a generation AI model that takes past travel data as input and outputs an optimal travel plan. The generation unit can also generate a travel plan using a generation AI model that takes user preference data as input and outputs an optimal travel plan. In this way, by using the generation AI, a travel plan optimal for the user's conditions can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a travel plan using a generation AI model that takes the user's desired conditions as input and outputs an optimal travel plan.

[0032] The suggestion unit can suggest tourist attractions, accommodations, restaurants, and means of transportation based on the generated travel plan. Tourist attractions include, for example, historical buildings, natural landscapes, cultural facilities, etc., but are not limited to these examples. The suggestion unit, for example, suggests historical buildings. The suggestion unit can also suggest natural landscapes. The suggestion unit can also suggest cultural facilities. Accommodations include, for example, hotels, inns, and guesthouses, but are not limited to these examples. The suggestion unit, for example, suggests hotels. The suggestion unit can also suggest inns. The suggestion unit can also suggest guesthouses. Restaurants include, for example, local cuisine, restaurants listed in the Michelin Guide, etc., but are not limited to these examples. The suggestion unit, for example, suggests restaurants serving local cuisine. The suggestion unit can also suggest restaurants listed in the Michelin Guide. Transportation modes include, for example, public transportation, rental cars, taxis, etc., but are not limited to these examples. The suggestion unit, for example, suggests public transportation. The suggestion unit can also suggest rental cars. The suggestion unit can also suggest taxis. This enhances the user's travel experience by suggesting tourist attractions, accommodations, and the like based on the travel plan. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the generated travel plan and make suggestions using an AI model that suggests tourist attractions, accommodations, and the like.

[0033] The suggestion unit may also incorporate local events, local specialties, and local cultural experiences into the suggestions. Local events include, but are not limited to, festivals, concerts, and sporting events, for example. The suggestion unit may, for example, propose festivals. The suggestion unit may also propose concerts. The suggestion unit may also propose sporting events. Local specialties include, but are not limited to, local ingredients and crafts. The suggestion unit may, for example, propose local ingredients. The suggestion unit may also propose crafts. Local cultural experiences include, but are not limited to, experiencing traditional crafts and participating in local festivals, for example. The suggestion unit may, for example, propose experiencing traditional crafts. The suggestion unit may also suggest participating in local festivals. In this way, by incorporating local events, local specialties, and local cultural experiences into the suggestions, it is possible to maximize the use of tourist destination resources. Some or all of the above-described processing by the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can take the generated travel plan as input and make suggestions using an AI model that suggests local events, local specialties, and local cultural experiences.

[0034] The generation unit can perform analysis using past travel data or user preference data. Past travel data includes, for example, past travel history and travel destination ratings, but is not limited to these examples. The generation unit, for example, analyzes past travel history. The generation unit can also analyze travel destination ratings. User preference data includes, for example, past preferences and survey results, but is not limited to these examples. The generation unit, for example, analyzes past preferences. The generation unit can also analyze survey results. In this way, by using past travel data and user preference data, a more accurate travel plan can be generated. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can generate a travel plan using a generation AI model that inputs past travel data and user preference data and outputs an optimal travel plan.

[0035] The reception unit can analyze the user's past travel history and suggest the optimal input method. For example, the reception unit automatically displays travel destinations that the user has frequently visited in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest travel destinations related to specific seasons or events from the user's past travel history. In this way, the optimal input method can be suggested to the user by analyzing the past travel history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can suggest an input method using an AI model that inputs the user's past travel history and suggests the optimal input method.

[0036] The reception unit can perform filtering based on the user's current living situation and areas of interest when inputting desired travel conditions. The reception unit can, for example, suggest appropriate travel destinations based on the user's current living situation (work, family, etc.). The reception unit can also filter related travel destinations based on the user's areas of interest (history, nature, art, etc.). The reception unit can also suggest reasonable travel plans based on the user's current health condition and physical strength. In this way, filtering based on the user's living situation and areas of interest can suggest more appropriate travel destinations. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's living situation and areas of interest and suggest travel destinations using an AI model that performs filtering.

[0037] The reception unit can select an input means according to the user's input method when inputting desired travel conditions. For example, when the user inputs desired travel conditions by voice, the reception unit supports the input using voice recognition technology. The reception unit can also provide an input completion function when the user inputs desired travel conditions by text. The reception unit can also suggest related travel destinations using image recognition technology when the user inputs desired travel conditions using an image. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select the input means using an AI model that uses the user's input method as input and selects the optimal input means.

[0038] When inputting desired travel conditions, the reception unit can prioritize inputting highly relevant conditions by taking into account the user's geographical location information. For example, the reception unit can prioritize suggesting travel destinations close to the user's current location. Furthermore, if the user is in a specific region, the reception unit can prioritize suggesting travel destinations related to that region. Furthermore, if the user is in a specific country or city, the reception unit can prioritize suggesting tourist attractions and activities related to that location. In this way, highly relevant travel destinations can be suggested by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input conditions using an AI model that inputs the user's geographical location information and prioritizes inputting highly relevant conditions.

[0039] When inputting desired travel conditions, the reception unit can analyze the user's online activity and input related conditions. The reception unit can, for example, suggest related travel destinations based on locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts to suggest travel destinations that the user may be interested in. The reception unit can also suggest related travel destinations based on the activities of the user's friends on social media. In this way, highly relevant travel destinations can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input conditions using an AI model that uses the user's online activity as input and inputs related conditions.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting desired travel conditions. The reception unit customizes the input interface, for example, based on feedback provided by the user in the past. The reception unit can also preferentially suggest a preferred input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and optimize the input procedure. In this way, the input method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the input method using an AI model that uses the user's past feedback as input and customizes the input method.

[0041] When generating a travel plan, the generation unit can adjust the level of detail of the plan based on the importance of the trip. For example, the generation unit generates a detailed plan for an important trip (such as a wedding anniversary). The generation unit can also generate a concise plan for a short trip. The generation unit can also generate a plan that focuses on activities in which the user is particularly interested. In this way, by adjusting the level of detail of the plan based on the importance of the trip, a travel plan that meets the user's needs can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a travel plan using an AI model that uses the importance of the trip as input and adjusts the level of detail of the plan.

[0042] When generating a travel plan, the generation unit can apply different generation algorithms depending on the travel category. For example, in the case of a family trip, the generation unit can generate a plan including family-friendly activities. In addition, in the case of a business trip, the generation unit can generate a plan including efficient means of transportation and accommodations. In addition, in the case of an adventure trip, the generation unit can generate a plan including adventurous activities. In this way, by applying different generation algorithms depending on the travel category, a more appropriate travel plan can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a travel plan using an AI model that inputs a travel category and applies different generation algorithms.

[0043] When generating a travel plan, the generation unit can improve the accuracy of the plan by referring to the user's past travel results. For example, the generation unit generates a similar plan based on a travel plan that the user was satisfied with in the past. The generation unit can also generate a plan that avoids travel plans that the user was dissatisfied with in the past. The generation unit can also analyze the user's past travel results and generate an optimal plan. In this way, the accuracy of the plan can be improved by referring to the user's past travel results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a travel plan using an AI model that uses the user's past travel results as input and improves the accuracy of the plan.

[0044] When generating a travel plan, the generation unit can determine the priority of plans based on the time of trip submission. For example, in the case of an upcoming trip, the generation unit can generate a plan with priority. In addition, in the case of a long-term trip, the generation unit can also generate a detailed plan. In addition, if a user is participating in a specific event, the generation unit can generate a plan tailored to that event. In this way, by determining the priority of plans based on the time of trip submission, it is possible to provide a travel plan that meets the user's needs. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a travel plan using an AI model that inputs the time of trip submission and determines the priority of plans.

[0045] When generating a travel plan, the generation unit can adjust the order of the plans based on the travel relevance. For example, if a user desires multiple travel destinations, the generation unit generates the plan in order of relevance. Furthermore, if a user desires a specific activity, the generation unit can prioritize suggesting travel destinations related to that activity. Furthermore, if a user desires to travel in a specific season, the generation unit can prioritize suggesting travel destinations related to that season. In this way, by adjusting the order of the plans based on the travel relevance, it is possible to provide an optimal travel plan for the user. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a travel plan using an AI model that inputs travel relevance and adjusts the order of the plans.

[0046] When generating a travel plan, the generation unit can adjust the use of technical terms in the plan according to the user's level of expertise. For example, if the user is a novice traveler, the generation unit can generate a plan that avoids technical terms. Furthermore, if the user is an experienced traveler, the generation unit can generate a plan that includes detailed technical terms. Furthermore, the generation unit can generate a plan that uses appropriate terminology according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, a travel plan that is easy for the user to understand can be provided. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a travel plan using an AI model that uses the user's level of expertise as input and adjusts the use of technical terms.

[0047] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the tourist destination. For example, in the case of an important tourist destination, the suggestion unit makes a proposal including detailed information. Furthermore, in the case of a tourist destination that can be visited in a short time, the suggestion unit can also make a proposal including concise information. Furthermore, the suggestion unit can make a proposal that focuses on tourist destinations in which the user is particularly interested. In this way, by adjusting the level of detail of the proposal based on the importance of the tourist destination, it is possible to provide the most suitable proposal for the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that inputs the importance of the tourist destination and adjusts the level of detail of the proposal.

[0048] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the tourist destination. For example, in the case of a historical tourist destination, the suggestion unit makes suggestions that include historical background. Furthermore, in the case of a natural tourist destination, the suggestion unit can make suggestions that include natural scenery and activities. Furthermore, in the case of an urban tourist destination, the suggestion unit can make suggestions that include city attractions and shopping spots. In this way, by applying different suggestion algorithms depending on the category of the tourist destination, it is possible to provide optimal suggestions for the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the category of the tourist destination and applies different suggestion algorithms.

[0049] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit makes similar suggestions based on suggestions that the user was satisfied with in the past. The suggestion unit can also make suggestions to avoid suggestions that the user was dissatisfied with in the past. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that uses the user's past suggestion results as input and improves the accuracy of the suggestion.

[0050] When making a proposal, the suggestion unit can determine the priority of the proposals based on the time of submission of the tourist destinations. For example, the suggestion unit can prioritize suggesting tourist destinations with upcoming events or festivals. The suggestion unit can also prioritize suggesting seasonal tourist destinations. The suggestion unit can also prioritize suggesting tourist destinations that the user plans to visit at a specific time. In this way, by determining the priority of the proposals based on the time of submission of the tourist destinations, it is possible to provide optimal suggestions for the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the time of submission of the tourist destinations and determines the priority of the proposals.

[0051] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of tourist destinations. For example, if a user desires multiple tourist destinations, the suggestion unit can make suggestions in order of relevance. Furthermore, if a user desires a specific activity, the suggestion unit can prioritize suggesting tourist destinations related to that activity. Furthermore, if a user desires to travel in a specific season, the suggestion unit can prioritize suggesting tourist destinations related to that season. In this way, by adjusting the order of suggestions based on the relevance of tourist destinations, it is possible to provide optimal suggestions for the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the relevance of tourist destinations and adjusts the order of suggestions.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. For example, if the user is a novice traveler, the suggestion unit can make a proposal that avoids technical terms. Furthermore, if the user is an experienced traveler, the suggestion unit can make a proposal that includes detailed technical terms. Furthermore, the suggestion unit can make a proposal using appropriate terms according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide a proposal that is easy for the user to understand. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that uses the user's level of expertise as input and adjusts the use of technical terms.

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

[0054] When the user inputs their desired travel conditions, the reception unit can provide a predictive input function based on the user's past travel history and preference data. For example, it can automatically display travel destinations and accommodations that the user has visited in the past as candidates. It can also prioritize suggestions of activities and restaurants that the user has previously preferred. Furthermore, it can predict and suggest travel destinations related to specific seasons or events based on the user's past travel history. This makes it possible to provide more personalized travel plans by utilizing the user's past data.

[0055] The suggestion unit can suggest reasonable travel plans based on the user's current health condition and physical strength. For example, if the user is not confident in their physical strength, it can suggest plans that are short and involve minimal travel. Also, if the user is in good health, it can suggest plans that include many active activities. Furthermore, if the user has specific health constraints, it can suggest travel plans that take those constraints into consideration. This makes it possible to provide the optimal travel plan according to the user's health condition.

[0056] When generating a travel plan, the generation unit can adjust the level of detail of the plan based on the importance of the trip. For example, a detailed plan can be generated for an important trip (such as a wedding anniversary). A concise plan can also be generated for a short trip. A plan that focuses on activities in which the user is particularly interested can also be generated. In this way, by adjusting the level of detail of the plan based on the importance of the trip, a travel plan that meets the user's needs can be provided.

[0057] The reception unit can analyze the user's online activity and input related conditions. For example, it can suggest related travel destinations based on the places where the user has checked in on social media. It can also analyze the content of the user's social media posts to suggest travel destinations that the user may be interested in. It can also suggest related travel destinations based on the activities of the user's friends on social media. In this way, it is possible to suggest highly relevant travel destinations by analyzing the user's social media activity.

[0058] When generating a travel plan, the generation unit can adjust the use of technical terms in the plan according to the user's level of expertise. For example, if the user is a novice traveler, the generation unit can generate a plan that avoids technical terms. On the other hand, if the user is an experienced traveler, the generation unit can generate a plan that includes detailed technical terms. The generation unit can also generate a plan that uses appropriate terms according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide a travel plan that is easy for the user to understand.

[0059] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of the tourist destination. For example, in the case of a historical tourist destination, suggestions including historical background can be made. In addition, in the case of a natural tourist destination, suggestions including natural scenery and activities can be made. In addition, in the case of an urban tourist destination, suggestions including city attractions and shopping spots can be made. In this way, by applying different suggestion algorithms depending on the category of the tourist destination, it is possible to provide the most suitable suggestions for the user.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives the user's desired travel conditions. These conditions include, for example, the travel destination, budget, duration, and activities of interest. By entering these conditions into the reception unit, the user communicates their wishes to the system. Step 2: The generation unit analyzes the conditions entered by the reception unit and generates a travel plan. The generation unit uses a generation AI, for example, a text generation AI (LLM), to generate the travel plan. It can also analyze past travel data and user preference data to generate an optimal travel plan. Step 3: The proposal unit makes proposals to maximize the use of the tourist destination's resources based on the travel plan generated by the generation unit. The proposal unit suggests tourist destination's attractions, accommodations, restaurants, transportation, local events and specialties, local cultural experiences, etc.

[0062] (Example 2) A travel suggestion system according to an embodiment of the present invention is a system in which a user inputs desired travel conditions, and a generation AI analyzes the input to generate an optimal travel plan, making maximum use of tourist destination resources. In the travel suggestion system, a user inputs desired travel conditions, and a generation AI analyzes the input to generate an optimal travel plan. This plan includes tourist destination attractions, accommodations, restaurants, and transportation options. Furthermore, the generation AI incorporates local events, local specialties, and local cultural experiences into the proposal to maximize the use of tourist destination resources. For example, a travel suggestion system allows a user to input desired travel conditions, such as travel destination, budget, duration, and activities of interest. The travel suggestion system then analyzes the input conditions and generates an optimal travel plan using a generation AI. The input to the generation AI is the user's desired conditions themselves, and the generation AI generates the travel plan based on the input. For example, the generation AI receives a prompt such as, "Please create an optimal travel plan based on these conditions," and creates the travel plan. The travel suggestion system then suggests tourist destination attractions, accommodations, restaurants, and transportation options based on the generated travel plan. Furthermore, the travel suggestion system also incorporates local events, local specialties, and local cultural experiences into its suggestions. For example, it suggests local festivals and concerts, and experiences with local specialties and traditional crafts. This allows the travel suggestion system to easily create travel plans that meet the user's wishes, and tourist destinations can effectively utilize their resources. This allows the travel suggestion system to generate optimal travel plans based on the user's wishes, and to make maximum use of tourist destination resources. For example, the user can have a fulfilling travel experience, and tourist destinations can effectively utilize their resources.

[0063] A travel suggestion system according to an embodiment includes a reception unit, a generation unit, and a suggestion unit. The reception unit receives input of desired travel conditions from a user. The desired travel conditions include, but are not limited to, travel destinations, budgets, travel duration, and activities of interest. The reception unit receives, for example, input of a travel destination from a user. The reception unit can also receive input of a user's budget. The reception unit can also receive input of a user's travel duration. The reception unit can also receive input of activities of interest to a user. The generation unit uses a generation AI to analyze the conditions input by the reception unit and generate a travel plan. The generation AI generates a travel plan using, for example, a text generation AI (e.g., LLM). The generation unit can also use the generation AI to analyze past travel data and user preference data to generate an optimal travel plan. The generation unit can also use the generation AI to generate a travel plan based on the user's conditions. For example, the generation AI receives input of the user's desired conditions and outputs an optimal travel plan. The suggestion unit makes suggestions that make the most of the resources of tourist destinations based on the travel plan generated by the generation unit. The suggestion unit may suggest, for example, attractions of a tourist destination. The suggestion unit may also suggest accommodations. The suggestion unit may also suggest restaurants. The suggestion unit may also suggest transportation options. The suggestion unit may also incorporate local events, local specialties, and local cultural experiences into the suggestion. For example, the suggestion unit may suggest local festivals and concerts. The suggestion unit may also suggest experiences of local specialties and traditional crafts. This allows the travel suggestion system according to the embodiment to generate an optimal travel plan based on the user's wishes and make maximum use of the resources of the tourist destination. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit may generate a travel plan using a generation AI model that receives the user's desired conditions as input and outputs an optimal travel plan. Some or all of the above-described processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI.For example, the suggestion unit can make suggestions using an AI model that takes the generated travel plan as input and outputs suggestions that make the most of the tourist destination's resources.

[0064] The reception unit allows the user to input desired conditions for a travel destination, budget, duration, and activities of interest. Travel destinations include, but are not limited to, domestic travel, international travel, and specific cities or regions. For example, the reception unit allows the user to input a domestic travel destination. The reception unit also allows the user to input a destination for international travel. The reception unit also allows the user to input a specific city or region destination. The budget includes, but is not limited to, a total budget and a daily budget. For example, the reception unit allows the user to input a total budget. The reception unit also allows the user to input a daily budget. The duration includes, but is not limited to, the number of days of the trip and a specific period of time. For example, the reception unit allows the user to input the number of days of the trip. The reception unit also allows the user to input a specific period of time. Interested activities include, but are not limited to, sports, cultural experiences, nature sightseeing, and the like. For example, the reception unit allows the user to input that the user is interested in sports. The reception unit also allows the user to input that the user is interested in cultural experiences. The reception unit can also input that the user is interested in nature sightseeing. This allows the user to input detailed desired conditions, thereby generating a more accurate travel plan. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the desired conditions using an AI model that inputs the desired conditions and analyzes the desired conditions.

[0065] The generation unit can analyze the input conditions using a generation AI and generate a travel plan. Examples of generation AI include, but are not limited to, machine learning models and neural networks. For example, the generation unit can analyze the input conditions and generate a travel plan using a machine learning model. The generation unit can also analyze the input conditions and generate a travel plan using a neural network. The generation unit can also analyze the input conditions and generate a travel plan using a generation AI to analyze the user's past travel data and preference data and generate an optimal travel plan. For example, the generation unit can generate a travel plan using a generation AI model that takes past travel data as input and outputs an optimal travel plan. The generation unit can also generate a travel plan using a generation AI model that takes user preference data as input and outputs an optimal travel plan. In this way, by using the generation AI, a travel plan optimal for the user's conditions can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a travel plan using a generation AI model that takes the user's desired conditions as input and outputs an optimal travel plan.

[0066] The suggestion unit can suggest tourist attractions, accommodations, restaurants, and means of transportation based on the generated travel plan. Tourist attractions include, for example, historical buildings, natural landscapes, cultural facilities, etc., but are not limited to these examples. The suggestion unit, for example, suggests historical buildings. The suggestion unit can also suggest natural landscapes. The suggestion unit can also suggest cultural facilities. Accommodations include, for example, hotels, inns, and guesthouses, but are not limited to these examples. The suggestion unit, for example, suggests hotels. The suggestion unit can also suggest inns. The suggestion unit can also suggest guesthouses. Restaurants include, for example, local cuisine, restaurants listed in the Michelin Guide, etc., but are not limited to these examples. The suggestion unit, for example, suggests restaurants serving local cuisine. The suggestion unit can also suggest restaurants listed in the Michelin Guide. Transportation modes include, for example, public transportation, rental cars, taxis, etc., but are not limited to these examples. The suggestion unit, for example, suggests public transportation. The suggestion unit can also suggest rental cars. The suggestion unit can also suggest taxis. This enhances the user's travel experience by suggesting tourist attractions, accommodations, and the like based on the travel plan. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the generated travel plan and make suggestions using an AI model that suggests tourist attractions, accommodations, and the like.

[0067] The suggestion unit may also incorporate local events, local specialties, and local cultural experiences into the suggestions. Local events include, but are not limited to, festivals, concerts, and sporting events, for example. The suggestion unit may, for example, propose festivals. The suggestion unit may also propose concerts. The suggestion unit may also propose sporting events. Local specialties include, but are not limited to, local ingredients and crafts. The suggestion unit may, for example, propose local ingredients. The suggestion unit may also propose crafts. Local cultural experiences include, but are not limited to, experiencing traditional crafts and participating in local festivals, for example. The suggestion unit may, for example, propose experiencing traditional crafts. The suggestion unit may also suggest participating in local festivals. In this way, by incorporating local events, local specialties, and local cultural experiences into the suggestions, it is possible to maximize the use of tourist destination resources. Some or all of the above-described processing by the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can take the generated travel plan as input and make suggestions using an AI model that suggests local events, local specialties, and local cultural experiences.

[0068] The generation unit can perform analysis using past travel data or user preference data. Past travel data includes, for example, past travel history and travel destination ratings, but is not limited to these examples. The generation unit, for example, analyzes past travel history. The generation unit can also analyze travel destination ratings. User preference data includes, for example, past preferences and survey results, but is not limited to these examples. The generation unit, for example, analyzes past preferences. The generation unit can also analyze survey results. In this way, by using past travel data and user preference data, a more accurate travel plan can be generated. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can generate a travel plan using a generation AI model that inputs past travel data and user preference data and outputs an optimal travel plan.

[0069] The reception unit can estimate the user's emotions and adjust the input method for desired travel conditions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input desired travel conditions. This allows for a more comfortable input experience by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can adjust the input method using an AI model that receives the user's emotion data as input and adjusts the input method based on the emotion.

[0070] The reception unit can analyze the user's past travel history and suggest the optimal input method. For example, the reception unit automatically displays travel destinations that the user has frequently visited in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest travel destinations related to specific seasons or events from the user's past travel history. In this way, the optimal input method can be suggested to the user by analyzing the past travel history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can suggest an input method using an AI model that inputs the user's past travel history and suggests the optimal input method.

[0071] The reception unit can perform filtering based on the user's current living situation and areas of interest when inputting desired travel conditions. The reception unit can, for example, suggest appropriate travel destinations based on the user's current living situation (work, family, etc.). The reception unit can also filter related travel destinations based on the user's areas of interest (history, nature, art, etc.). The reception unit can also suggest reasonable travel plans based on the user's current health condition and physical strength. In this way, filtering based on the user's living situation and areas of interest can suggest more appropriate travel destinations. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's living situation and areas of interest and suggest travel destinations using an AI model that performs filtering.

[0072] The reception unit can select an input means according to the user's input method when inputting desired travel conditions. For example, when the user inputs desired travel conditions by voice, the reception unit supports the input using voice recognition technology. The reception unit can also provide an input completion function when the user inputs desired travel conditions by text. The reception unit can also suggest related travel destinations using image recognition technology when the user inputs desired travel conditions using an image. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select the input means using an AI model that uses the user's input method as input and selects the optimal input means.

[0073] The reception unit can estimate the user's emotions and determine the priority of the desired conditions entered based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize suggesting relaxing travel destinations. Furthermore, if the user is excited, the reception unit can prioritize suggesting travel destinations that include active activities. Furthermore, if the user is tired, the reception unit can prioritize suggesting travel destinations that emphasize rest. This allows for determining the priority of the desired conditions based on the user's emotions, thereby proposing a more appropriate travel plan. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can determine the priority using an AI model that receives the user's emotion data and prioritizes the desired conditions based on emotions.

[0074] When inputting desired travel conditions, the reception unit can prioritize inputting highly relevant conditions by taking into account the user's geographical location information. For example, the reception unit can prioritize suggesting travel destinations close to the user's current location. Furthermore, if the user is in a specific region, the reception unit can prioritize suggesting travel destinations related to that region. Furthermore, if the user is in a specific country or city, the reception unit can prioritize suggesting tourist attractions and activities related to that location. In this way, highly relevant travel destinations can be suggested by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input conditions using an AI model that inputs the user's geographical location information and prioritizes inputting highly relevant conditions.

[0075] When inputting desired travel conditions, the reception unit can analyze the user's online activity and input related conditions. The reception unit can, for example, suggest related travel destinations based on locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts to suggest travel destinations that the user may be interested in. The reception unit can also suggest related travel destinations based on the activities of the user's friends on social media. In this way, highly relevant travel destinations can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input conditions using an AI model that uses the user's online activity as input and inputs related conditions.

[0076] The reception unit can customize the input method by reflecting the user's past feedback when inputting desired travel conditions. The reception unit customizes the input interface, for example, based on feedback provided by the user in the past. The reception unit can also preferentially suggest a preferred input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and optimize the input procedure. In this way, the input method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the input method using an AI model that uses the user's past feedback as input and customizes the input method.

[0077] The generation unit can estimate the user's emotions and adjust the itinerary generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an itinerary that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate an itinerary that emphasizes the shortest route. If the user is excited, the generation unit can generate an itinerary that adds visually stimulating effects. This allows for adjusting the itinerary generation method based on the user's emotions to provide a more appropriate itinerary. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can adjust the generation method using an AI model that inputs the user's emotion data and adjusts the itinerary generation method based on the emotion.

[0078] When generating a travel plan, the generation unit can adjust the level of detail of the plan based on the importance of the trip. For example, the generation unit generates a detailed plan for an important trip (such as a wedding anniversary). The generation unit can also generate a concise plan for a short trip. The generation unit can also generate a plan that focuses on activities in which the user is particularly interested. In this way, by adjusting the level of detail of the plan based on the importance of the trip, a travel plan that meets the user's needs can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a travel plan using an AI model that uses the importance of the trip as input and adjusts the level of detail of the plan.

[0079] When generating a travel plan, the generation unit can apply different generation algorithms depending on the travel category. For example, in the case of a family trip, the generation unit can generate a plan including family-friendly activities. In addition, in the case of a business trip, the generation unit can generate a plan including efficient means of transportation and accommodations. In addition, in the case of an adventure trip, the generation unit can generate a plan including adventurous activities. In this way, by applying different generation algorithms depending on the travel category, a more appropriate travel plan can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a travel plan using an AI model that inputs a travel category and applies different generation algorithms.

[0080] When generating a travel plan, the generation unit can improve the accuracy of the plan by referring to the user's past travel results. For example, the generation unit generates a similar plan based on a travel plan that the user was satisfied with in the past. The generation unit can also generate a plan that avoids travel plans that the user was dissatisfied with in the past. The generation unit can also analyze the user's past travel results and generate an optimal plan. In this way, the accuracy of the plan can be improved by referring to the user's past travel results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a travel plan using an AI model that uses the user's past travel results as input and improves the accuracy of the plan.

[0081] The generation unit can estimate the user's emotions and adjust the duration of the travel plan based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short travel plan. Furthermore, if the user is relaxed, the generation unit can generate a long travel plan. Furthermore, if the user is excited, the generation unit can generate a travel plan that includes many activities. This allows the duration of the travel plan to be adjusted based on the user's emotions, thereby providing a more appropriate travel plan. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can generate a travel plan using an AI model that inputs the user's emotion data and adjusts the duration of the travel plan based on the emotion.

[0082] When generating a travel plan, the generation unit can determine the priority of plans based on the time of trip submission. For example, in the case of an upcoming trip, the generation unit can generate a plan with priority. In addition, in the case of a long-term trip, the generation unit can also generate a detailed plan. In addition, if a user is participating in a specific event, the generation unit can generate a plan tailored to that event. In this way, by determining the priority of plans based on the time of trip submission, it is possible to provide a travel plan that meets the user's needs. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a travel plan using an AI model that inputs the time of trip submission and determines the priority of plans.

[0083] When generating a travel plan, the generation unit can adjust the order of the plans based on the travel relevance. For example, if a user desires multiple travel destinations, the generation unit generates the plan in order of relevance. Furthermore, if a user desires a specific activity, the generation unit can prioritize suggesting travel destinations related to that activity. Furthermore, if a user desires to travel in a specific season, the generation unit can prioritize suggesting travel destinations related to that season. In this way, by adjusting the order of the plans based on the travel relevance, it is possible to provide an optimal travel plan for the user. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a travel plan using an AI model that inputs travel relevance and adjusts the order of the plans.

[0084] When generating a travel plan, the generation unit can adjust the use of technical terms in the plan according to the user's level of expertise. For example, if the user is a novice traveler, the generation unit can generate a plan that avoids technical terms. Furthermore, if the user is an experienced traveler, the generation unit can generate a plan that includes detailed technical terms. Furthermore, the generation unit can generate a plan that uses appropriate terminology according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, a travel plan that is easy for the user to understand can be provided. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a travel plan using an AI model that uses the user's level of expertise as input and adjusts the use of technical terms.

[0085] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide a simple, highly visible suggestion method. Furthermore, if the user is relaxed, the suggestion unit can provide a suggestion method that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide a suggestion method that focuses on the main points. This allows for more appropriate suggestions to be provided by adjusting the way suggestions are presented based on the user's emotions. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can adjust the way suggestions are presented based on the user's emotions using an AI model that receives user emotion data as input and adjusts the way suggestions are presented based on the emotions.

[0086] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the tourist destination. For example, in the case of an important tourist destination, the suggestion unit makes a proposal including detailed information. Furthermore, in the case of a tourist destination that can be visited in a short time, the suggestion unit can also make a proposal including concise information. Furthermore, the suggestion unit can make a proposal that focuses on tourist destinations in which the user is particularly interested. In this way, by adjusting the level of detail of the proposal based on the importance of the tourist destination, it is possible to provide the most suitable proposal for the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that inputs the importance of the tourist destination and adjusts the level of detail of the proposal.

[0087] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the tourist destination. For example, in the case of a historical tourist destination, the suggestion unit makes suggestions that include historical background. Furthermore, in the case of a natural tourist destination, the suggestion unit can make suggestions that include natural scenery and activities. Furthermore, in the case of an urban tourist destination, the suggestion unit can make suggestions that include city attractions and shopping spots. In this way, by applying different suggestion algorithms depending on the category of the tourist destination, it is possible to provide optimal suggestions for the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the category of the tourist destination and applies different suggestion algorithms.

[0088] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit makes similar suggestions based on suggestions that the user was satisfied with in the past. The suggestion unit can also make suggestions to avoid suggestions that the user was dissatisfied with in the past. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that uses the user's past suggestion results as input and improves the accuracy of the suggestion.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for more appropriate suggestions to be provided by adjusting the length of the suggestions based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can make suggestions using an AI model that receives user emotion data as input and adjusts the length of the suggestions based on the emotion.

[0090] When making a proposal, the suggestion unit can determine the priority of the proposals based on the time of submission of the tourist destinations. For example, the suggestion unit can prioritize suggesting tourist destinations with upcoming events or festivals. The suggestion unit can also prioritize suggesting seasonal tourist destinations. The suggestion unit can also prioritize suggesting tourist destinations that the user plans to visit at a specific time. In this way, by determining the priority of the proposals based on the time of submission of the tourist destinations, it is possible to provide optimal suggestions for the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the time of submission of the tourist destinations and determines the priority of the proposals.

[0091] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of tourist destinations. For example, if a user desires multiple tourist destinations, the suggestion unit can make suggestions in order of relevance. Furthermore, if a user desires a specific activity, the suggestion unit can prioritize suggesting tourist destinations related to that activity. Furthermore, if a user desires to travel in a specific season, the suggestion unit can prioritize suggesting tourist destinations related to that season. In this way, by adjusting the order of suggestions based on the relevance of tourist destinations, it is possible to provide optimal suggestions for the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the relevance of tourist destinations and adjusts the order of suggestions.

[0092] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. For example, if the user is a novice traveler, the suggestion unit can make a proposal that avoids technical terms. Furthermore, if the user is an experienced traveler, the suggestion unit can make a proposal that includes detailed technical terms. Furthermore, the suggestion unit can make a proposal using appropriate terms according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide a proposal that is easy for the user to understand. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that uses the user's level of expertise as input and adjusts the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and proposal 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 can input the user's desired travel conditions using the reception device 38 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a travel plan using a generation AI. The proposal unit can present the generated travel plan to the user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and proposal 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 can input the user's desired travel conditions using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a travel plan using a generation AI. The proposal unit can present the generated travel plan to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and proposal 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 can input the user's desired travel conditions using the microphone 238 of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a travel plan using a generation AI. The proposal unit can present the generated travel plan to the user using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and proposal unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the user's desired travel conditions using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a travel plan using a generation AI. The proposal unit can present the generated travel plan to the user using the speaker 240 of the robot 414.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] When the user inputs their desired travel conditions, the reception unit can provide a predictive input function based on the user's past travel history and preference data. For example, it can automatically display travel destinations and accommodations that the user has visited in the past as candidates. It can also prioritize suggestions of activities and restaurants that the user has previously preferred. Furthermore, it can predict and suggest travel destinations related to specific seasons or events based on the user's past travel history. This makes it possible to provide more personalized travel plans by utilizing the user's past data.

[0095] The generation unit can estimate the user's emotions and adjust the method for generating the itinerary based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an itinerary that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate an itinerary that emphasizes the shortest route. If the user is excited, the generation unit can generate an itinerary that adds visually stimulating effects. In this way, by adjusting the method for generating the itinerary based on the user's emotions, it is possible to provide a more appropriate itinerary.

[0096] The suggestion unit can suggest reasonable travel plans based on the user's current health condition and physical strength. For example, if the user is not confident in their physical strength, it can suggest plans that are short and involve minimal travel. Also, if the user is in good health, it can suggest plans that include many active activities. Furthermore, if the user has specific health constraints, it can suggest travel plans that take those constraints into consideration. This makes it possible to provide the optimal travel plan according to the user's health condition.

[0097] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible suggestion method can be provided. If the user is relaxed, a suggestion method including detailed information can be provided. If the user is in a hurry, a suggestion method that focuses on the main points can be provided. In this way, by adjusting the way suggestions are expressed based on the user's emotions, more appropriate suggestions can be provided.

[0098] When generating a travel plan, the generation unit can adjust the level of detail of the plan based on the importance of the trip. For example, a detailed plan can be generated for an important trip (such as a wedding anniversary). A concise plan can also be generated for a short trip. A plan that focuses on activities in which the user is particularly interested can also be generated. In this way, by adjusting the level of detail of the plan based on the importance of the trip, a travel plan that meets the user's needs can be provided.

[0099] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, if the user is in a hurry, a short and to-the-point suggestion can be made. If the user is relaxed, a longer suggestion with detailed explanations can be made. If the user is excited, a suggestion with visually stimulating effects can be made. In this way, by adjusting the length of the suggestion based on the user's emotions, more appropriate suggestions can be provided.

[0100] The reception unit can analyze the user's online activity and input related conditions. For example, it can suggest related travel destinations based on the places where the user has checked in on social media. It can also analyze the content of the user's social media posts to suggest travel destinations that the user may be interested in. It can also suggest related travel destinations based on the activities of the user's friends on social media. In this way, it is possible to suggest highly relevant travel destinations by analyzing the user's social media activity.

[0101] When generating a travel plan, the generation unit can adjust the use of technical terms in the plan according to the user's level of expertise. For example, if the user is a novice traveler, the generation unit can generate a plan that avoids technical terms. On the other hand, if the user is an experienced traveler, the generation unit can generate a plan that includes detailed technical terms. The generation unit can also generate a plan that uses appropriate terms according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide a travel plan that is easy for the user to understand.

[0102] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of the tourist destination. For example, in the case of a historical tourist destination, suggestions including historical background can be made. In addition, in the case of a natural tourist destination, suggestions including natural scenery and activities can be made. In addition, in the case of an urban tourist destination, suggestions including city attractions and shopping spots can be made. In this way, by applying different suggestion algorithms depending on the category of the tourist destination, it is possible to provide the most suitable suggestions for the user.

[0103] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, tourist spots where the user can relax can be preferentially suggested. Also, if the user is excited, tourist spots that include active activities can be preferentially suggested. Also, if the user is tired, tourist spots that emphasize rest can be preferentially suggested. In this way, by determining the priority of suggestions based on the user's emotions, more appropriate suggestions can be provided.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit receives the user's desired travel conditions. These conditions include, for example, the travel destination, budget, duration, and activities of interest. By entering these conditions into the reception unit, the user communicates their wishes to the system. Step 2: The generation unit analyzes the conditions entered by the reception unit and generates a travel plan. The generation unit uses a generation AI, for example, a text generation AI (LLM), to generate the travel plan. It can also analyze past travel data and user preference data to generate an optimal travel plan. Step 3: The proposal unit makes proposals to maximize the use of the tourist destination's resources based on the travel plan generated by the generation unit. The proposal unit suggests tourist destination's attractions, accommodations, restaurants, transportation, local events and specialties, local cultural experiences, etc.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] 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.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] 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.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] 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.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] 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.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0169] 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.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 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 section where travel requirements are entered; a generation unit that analyzes the conditions input by the reception unit and generates a travel plan; a proposal unit that makes the most of the resources of tourist destinations based on the travel plan generated by the generation unit. A system characterized by:

2. The reception unit Enter your travel destination, budget, duration, and desired activities 2. The system of claim 1.

3. The generation unit The generation AI analyzes the input conditions and generates a travel plan.

2. The system of claim 1.

4. The proposal unit Based on the generated itinerary, we suggest tourist attractions, accommodations, restaurants, and transportation options.

2. The system of claim 1.

5. The proposal unit Incorporate local events, local specialties, and local cultural experiences into your proposals.

2. The system of claim 1.

6. The generation unit Analyze using past travel data or user preference data 2. The system of claim 1.

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

2. The system of claim 1.

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

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