system

Abstract

The system according to the embodiment aims to propose appropriate sightseeing plans and necessary preparations for people using wheelchairs or strollers to enjoy sightseeing without stress. [Solution] A system according to an embodiment includes an acquisition unit, a collection unit, and a suggestion unit. The acquisition unit acquires image data. The collection unit acquires location information data. The collection unit acquires posted data. The collection unit acquires information from official websites. The suggestion unit analyzes the data acquired by the acquisition unit and collection unit, and suggests sightseeing plans and necessary preparations suitable for the user.

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] With conventional technology, people who use wheelchairs or strollers have difficulty efficiently gathering the necessary information and creating appropriate sightseeing plans when planning a trip.

[0005] The system according to the embodiment aims to propose appropriate sightseeing plans and necessary preparations for people using wheelchairs or strollers to enjoy sightseeing without stress. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a collection unit, and a suggestion unit. The acquisition unit acquires image data. The collection unit acquires location information data. The collection unit acquires posted data. The collection unit acquires information from official websites. The suggestion unit analyzes the data acquired by the acquisition unit and the collection unit, and suggests sightseeing plans and necessary preparations suitable for the user. [Effects of the Invention]

[0007] The system according to the embodiment can propose appropriate sightseeing plans and necessary preparations to enable people using wheelchairs or strollers to enjoy sightseeing without stress. [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 tourism support system according to an embodiment of the present invention uses generative AI to enable groups of people using wheelchairs or strollers to enjoy stress-free sightseeing by taking into account constraints such as transportation, travel routes, accommodations, and restaurants. This tourism support system is equipped with a function that proposes and advises on recommended itineraries and necessary preparations in advance. Specifically, the system eliminates the need for information gathering and solves the problem all at once by importing the following data: First, map data and images from image sharing services are imported as image data. This allows visual information on tourist spots and travel routes to be obtained. Next, location information data is imported from travel routes taken by people in similar situations. This allows information on routes actually traveled using wheelchairs or strollers. Furthermore, information from review sites, social media, blogs, etc. is imported as posted data. This allows impressions and ratings from actual users to be obtained. Finally, information on accommodations and restaurants, along with user reviews, is imported as official websites. This allows detailed information on accommodations and restaurants to be obtained. The generative AI analyzes this data and proposes and advises on optimal tourism plans and necessary preparations for users. For example, it can suggest routes that are easy for wheelchair users and restaurants that are stroller-friendly. It can also provide advice on necessary preparations in advance (for example, wheelchair rentals and whether strollers can be brought in). This system allows people who use wheelchairs or strollers to enjoy sightseeing without stress. Users can avoid the hassle of collecting information from multiple sources and can receive suggestions all at once from the generative AI, making trip planning go more smoothly. This allows the tourism support system to suggest optimal sightseeing plans and necessary preparations to users.

[0029] The tourism support system according to the embodiment includes an acquisition unit, a collection unit, and a suggestion unit. The acquisition unit acquires image data. The image data includes, but is not limited to, formats such as JPEG, PNG, and GIF. The acquisition unit acquires, for example, map data and images from an image sharing service. The map data can be acquired from, for example, Yahoo! (registered trademark) Maps or Google (registered trademark) Maps. The images from the image sharing service can be acquired from, for example, Instagram (registered trademark) or Flickr (registered trademark). The collection unit acquires location information data. The location information data includes, for example, GPS data and Wi-Fi location information, but is not limited to these examples. The collection unit acquires, for example, travel routes of people in the same situation. Examples of people in the same situation include wheelchair users and stroller users. The collection unit acquires posted data. The posted data includes, for example, social media posts, blog articles, and reviews on review sites, but is not limited to these examples. The collection unit acquires posted data from, for example, review sites, social media, and blogs. The review site information can be obtained from, for example, TripAdvisor or Yelp. The social networking site information can be obtained from, for example, Facebook (registered trademark) or X (formerly Twitter (registered trademark)). The blog information can be obtained from, for example, personal blogs or corporate blogs. The collection unit acquires information from official websites. Official website information includes, for example, official websites of accommodations and restaurants, but is not limited to these examples. The collection unit acquires, for example, official websites of accommodations and restaurants and their user reviews. The accommodations can be acquired from, for example, hotels, private lodgings, and guesthouses. The restaurants can be acquired from, for example, restaurants, cafes, and food trucks. The suggestion unit analyzes the data acquired by the acquisition unit and the collection unit and suggests optimal sightseeing plans and necessary preparations to the user. The suggestion unit suggests, for example, routes that are easy for wheelchair users to travel and restaurants that are easy to use with strollers. A route that is easy for wheelchair users to travel is selected based on, for example, barrier-free roads and the presence or absence of elevators.Restaurants that are stroller-friendly are selected based on, for example, wide aisles and the presence or absence of baby chairs. The suggestion unit advises users on necessary preparations in advance. The necessary preparations in advance include, but are not limited to, a list of things to bring and reservation procedures. This allows the tourism support system according to the embodiment to suggest optimal sightseeing plans and necessary preparations to users.

[0030] The acquisition unit can acquire map data or images from an image sharing service. The acquisition unit acquires, for example, map data. Examples of map data include Google Maps and OpenStreetMap. The acquisition unit acquires, for example, images from an image sharing service. Examples of image sharing services include Instagram and Flickr. By acquiring map data or images from an image sharing service, visual information about tourist spots and travel routes can be provided. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit inputs map data or images from an image sharing service into a generation AI and causes the generation AI to analyze the visual information.

[0031] The collection unit can acquire the travel routes of people in the same circumstances. The collection unit, for example, acquires the travel routes of people in the same circumstances. People in the same circumstances refer to, for example, wheelchair users and stroller users. The collection unit acquires the travel routes using, for example, GPS tracking data. The collection unit can also acquire travel routes through manual input by a user. For example, the collection unit collects travel route data manually entered by a user. By acquiring the travel routes of people in the same circumstances, it is possible to provide information on routes actually traveled using wheelchairs or strollers. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS tracking data into a generation AI and cause the generation AI to analyze the travel routes.

[0032] The collection unit can acquire posting data from review sites, SNS, and blogs. The collection unit, for example, acquires posting data from review sites. Review sites include, for example, TripAdvisor and Yelp. The collection unit, for example, acquires posting data from SNS. SNS includes, for example, Facebook and X (formerly Twitter). The collection unit, for example, acquires posting data from blogs. Blogs include, for example, personal blogs and corporate blogs. By acquiring posting data from review sites, SNS, blogs, etc., it is possible to provide impressions and evaluations from people who have actually used the services. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input posting data from review sites, SNS, and blogs into a generation AI and have the generation AI analyze the impressions and evaluations.

[0033] The collection unit can acquire the official website of an accommodation facility or a restaurant and its user reviews. The collection unit, for example, acquires the official website of an accommodation facility. Examples of accommodation facilities include hotels, guesthouses, and guesthouses. The collection unit, for example, acquires the official website of a restaurant. Examples of restaurants include restaurants, cafes, and food trucks. The collection unit, for example, acquires user reviews of accommodation facilities and restaurants. The user reviews include, for example, star ratings and comments. In this way, by acquiring the official website of an accommodation facility or a restaurant and its user reviews, detailed information about the accommodation facility or the restaurant can be provided. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the official website of an accommodation facility or a restaurant and its user reviews into a generation AI, and have the generation AI analyze the detailed information.

[0034] The suggestion unit analyzes the data acquired by the acquisition unit and the collection unit and can suggest routes that are easy for wheelchairs to navigate or restaurants that are easy to access with a stroller. The suggestion unit, for example, suggests routes that are easy for wheelchairs to navigate. Examples of routes that are easy for wheelchairs to navigate include barrier-free roads and the presence or absence of elevators. The suggestion unit, for example, suggests restaurants that are easy to access with a stroller. Examples of restaurants that are easy to access with a stroller include wide aisles and the presence or absence of baby chairs. This allows the user to be provided with an optimal sightseeing plan by suggesting routes that are easy for wheelchairs to navigate or restaurants that are easy to access with a stroller. Some or all of the above-mentioned processing by the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input the data acquired by the acquisition unit and the collection unit into a generation AI and cause the generation AI to propose an optimal sightseeing plan.

[0035] The suggestion unit can provide advice on necessary preparations in advance. For example, the suggestion unit provides advice on necessary preparations in advance. The necessary preparations in advance include, for example, a packing list and reservation procedures. By providing advice on necessary preparations in advance, the user can smoothly proceed with planning their trip. 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 input information on necessary preparations in advance into the generation AI and cause the generation AI to execute optimal advice.

[0036] When acquiring image data, the acquisition unit can analyze the user's past travel history and select an appropriate acquisition method. For example, the acquisition unit prioritizes acquiring image data of tourist spots visited by the user in the past. The acquisition unit can also acquire image data of landscapes or buildings that the user has previously liked, for example. Furthermore, the acquisition unit can acquire image data taken in a specific season or time period from the user's past travel history, for example. This allows the optimal image data acquisition method to be selected by analyzing the user's past travel history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past travel history data into the generation AI and cause the generation AI to select the optimal image data acquisition method.

[0037] The acquisition unit can filter image data based on the user's current interests and concerns when acquiring the image data. For example, if the user is interested in natural landscapes, the acquisition unit can prioritize acquiring image data of natural landscapes. Furthermore, for example, if the user is interested in historical buildings, the acquisition unit can also acquire image data of historical buildings. Furthermore, for example, if the user is interested in gourmet food, the acquisition unit can also acquire image data of restaurants and dishes. By filtering the image data based on the user's current interests and concerns, more relevant image data can be acquired. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's current interests and concerns data into the generation AI and cause the generation AI to filter the image data.

[0038] When acquiring image data, the acquisition unit can prioritize acquiring highly relevant images by taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring image data of tourist spots close to the user's current location. The acquisition unit can also prioritize acquiring image data of places the user plans to visit, for example. Furthermore, the acquisition unit can also prioritize acquiring image data near places the user has visited in the past, for example. In this way, highly relevant image data can be acquired preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information data to the generation AI and cause the generation AI to acquire highly relevant image data.

[0039] When acquiring image data, the acquisition unit can analyze the user's social media activity and acquire related images. For example, the acquisition unit can prioritize acquiring image data of locations shared by the user on SNS. The acquisition unit can also prioritize acquiring image data posted by accounts the user follows. Furthermore, the acquisition unit can prioritize acquiring image data that the user has "liked." This makes it possible to acquire related image data by analyzing the user's social media activity. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related image data.

[0040] When acquiring location information data, the collection unit can analyze the user's past movement history and select an appropriate acquisition method. For example, the collection unit prioritizes acquiring location information data of tourist spots visited by the user in the past. The collection unit can also acquire location information data of travel routes preferred by the user in the past. Furthermore, the collection unit can acquire location information data of routes traveled during specific seasons or time periods from the user's past movement history, for example. This allows the optimal method for acquiring location information data to be selected by analyzing the user's past movement history. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past movement history data into a generation AI and cause the generation AI to select an optimal method for acquiring location information data.

[0041] When acquiring location information data, the collection unit can filter the location information data based on the user's current living situation and areas of interest. For example, the collection unit prioritizes acquiring location information data of routes that are easy for the user to travel based on the user's current living situation. The collection unit can also prioritize acquiring location information data of tourist destinations that the user is interested in, for example. Furthermore, the collection unit can also acquire location information data of tourist destinations that are easy to access based on the user's current living situation. This allows more relevant location information data to be acquired by filtering the location information data based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current living situation and areas of interest data to the generation AI and cause the generation AI to filter the location information data.

[0042] When acquiring location information data, the collection unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes acquiring location information data of tourist spots close to the user's current location. The collection unit can also prioritize acquiring location information data of places the user plans to visit, for example. Furthermore, the collection unit can also prioritize acquiring location information data near places the user has visited in the past, for example. This makes it possible to prioritize acquiring highly relevant location information data by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to a generation AI and cause the generation AI to acquire highly relevant location information data.

[0043] When acquiring location information data, the collection unit can analyze the user's social media activity and acquire related data. For example, the collection unit prioritizes acquiring location information data of locations shared by the user on social media. The collection unit can also acquire location information data posted by accounts the user follows. Furthermore, the collection unit can prioritize acquiring location information data of locations the user has "liked." This allows related location information data to be acquired by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to acquire related location information data.

[0044] The suggestion unit can adjust the level of detail of the proposal based on the importance of the sightseeing plan when making the proposal. For example, the suggestion unit can provide detailed information for sightseeing plans with high importance. The suggestion unit can also provide concise information for sightseeing plans with low importance. Furthermore, the suggestion unit can, for example, gradually adjust the level of detail of the proposal according to the importance. This makes it possible to provide optimal information for the user by adjusting the level of detail of the proposal based on the importance of the sightseeing plan. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input importance data of the sightseeing plan to the generation AI and have the generation AI adjust the level of detail of the proposal.

[0045] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the category of the tour plan. For example, the suggestion unit can apply a proposal algorithm specialized for natural landscapes to a nature tour plan. The suggestion unit can also apply a proposal algorithm specialized for historical buildings to a history tour plan. The suggestion unit can also apply a proposal algorithm specialized for restaurants and cuisine to a gourmet tour plan. This allows for more appropriate proposals by applying different proposal algorithms depending on the category of the tour plan. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input category data of the tour plan into the generation AI and cause the generation AI to apply different proposal algorithms.

[0046] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the tour plan. For example, the suggestion unit can prioritize the most recent tour plan. The suggestion unit can also provide detailed information for tour plans submitted earlier. Furthermore, the suggestion unit can gradually adjust the priority of the proposal, for example, depending on the time of submission. This allows the user to be provided with optimal information by determining the priority of the proposal based on the time of submission of the tour plan. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the time of submission of the tour plan into the generation AI and have the generation AI determine the priority of the proposals.

[0047] The suggestion unit can adjust the order of suggestions based on the relevance of the sightseeing plans when making suggestions. For example, the suggestion unit can prioritize suggestions for highly relevant sightseeing plans. The suggestion unit can also provide concise information for less relevant sightseeing plans. Furthermore, the suggestion unit can gradually adjust the order of suggestions based on the relevance, for example. This makes it possible to provide optimal information for the user by adjusting the order of suggestions based on the relevance of the sightseeing plans. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input relevance data of the sightseeing plans into the generation AI and have the generation AI adjust the order of suggestions.

[0048] When providing advance preparation advice, the suggestion unit can provide appropriate advice by referring to the user's past travel history. The suggestion unit provides advice based on, for example, services and facilities the user has used in the past. The suggestion unit can also provide advice by predicting necessary preparations based on, for example, the user's past travel history. Furthermore, the suggestion unit can also analyze, for example, the user's past travel history and suggest the most efficient preparation method. This makes it possible to provide optimal advance preparation advice by referring to the user's past travel history. 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 input the user's past travel history data into the generation AI and cause the generation AI to execute optimal advance preparation advice.

[0049] The suggestion unit can customize the content of the advice based on the user's current living situation when providing advance preparation advice. The suggestion unit, for example, customizes the necessary preparations based on the user's current living situation. The suggestion unit can also suggest an optimal preparation method according to the user's current living situation. The suggestion unit can also adjust the content of the advice, for example, taking into account the user's current living situation. This allows for more appropriate advice to be provided by customizing the content of the advice based on the user's current living situation. 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 input the user's current living situation data into the generation AI and cause the generation AI to customize the content of the advice.

[0050] When providing advance preparation advice, the suggestion unit can provide appropriate advice taking into account the user's geographical location information. The suggestion unit, for example, advises the user on the optimal preparation method based on their current location. The suggestion unit can also advise the user on the necessary preparations based on, for example, the places the user plans to visit. Furthermore, the suggestion unit can also suggest the optimal preparation method taking into account the user's geographical location information. This makes it possible to provide optimal advance preparation advice by taking into account the user's geographical location information. 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 can input the user's geographical location information data into the generation AI and cause the generation AI to execute optimal advance preparation advice.

[0051] When providing advance preparation advice, the suggestion unit can analyze the user's social media activity and provide appropriate advice. The suggestion unit can advise the optimal preparation method based on, for example, information shared by the user on social media. The suggestion unit can also suggest a preparation method by referring to, for example, information on accounts the user follows. Furthermore, the suggestion unit can also advise on necessary preparations based on, for example, posts the user has "liked." In this way, optimal advance preparation advice can be provided by analyzing the user's social media activity. 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 can input the user's social media activity data into a generation AI and cause the generation AI to execute optimal advance preparation advice.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The tourism support system may further include a health management unit that monitors the user's health condition. The health management unit acquires data such as the user's heart rate, blood pressure, and number of steps, and monitors the user's health condition in real time based on this data. For example, if the user's heart rate suddenly increases, the health management unit can send the user a notification urging them to take a break. Also, if the user's number of steps exceeds a certain threshold, the health management unit can suggest that the user take a break. Furthermore, the health management unit can adjust the sightseeing plan based on the user's health condition and suggest a reasonable schedule. This allows the user to enjoy sightseeing while maintaining their health.

[0054] The tourism support system may further include a learning unit that learns the user's preferences. The learning unit analyzes the user's past selections and behavioral data to learn the user's preferences. For example, it can predict the tourist spots and services that the user will like based on data on tourist spots visited by the user in the past and services used by the user. The learning unit can also suggest new tourist spots and services based on the user's preferences. Furthermore, the learning unit can collect user feedback and improve the accuracy of suggestions. This allows the user to enjoy a sightseeing plan that suits their preferences.

[0055] The tourism support system can further include a budget management unit that manages the user's travel budget. The budget management unit allows the user to input their travel budget and proposes the optimal sightseeing plan within that budget. For example, the budget management unit can create a sightseeing plan that fits within the budget, taking into account expenses such as accommodation, transportation, and food. The budget management unit can also issue an alert if the user is likely to exceed their budget. Furthermore, the budget management unit can record the user's spending history and support budget management. This allows the user to enjoy their trip without worrying about their budget.

[0056] The tourism support system may further include a language support unit that provides multilingual support based on the user's language setting. The language support unit provides tourism information based on the user's language setting. For example, the language support unit may display descriptions and guides of tourist attractions in the user's native language. The language support unit may also perform real-time translation when the user speaks a different language. Furthermore, the language support unit may provide simple phrases for learning the local language based on the user's language setting. This allows the user to enjoy sightseeing without experiencing language barriers.

[0057] The tourism support system can further include a transportation optimization unit that optimizes the user's transportation means. The transportation optimization unit considers the user's transportation means (e.g., car, train, bus, bicycle, etc.) and proposes an optimal transportation route. For example, if the user uses a car, the transportation optimization unit can propose a route taking into consideration the availability of parking spaces and traffic congestion information. Also, if the user uses a train, the transportation optimization unit can propose a route taking into consideration transfer information and operation status. Furthermore, the transportation optimization unit can also propose optimal tourist destinations depending on the user's transportation means. This allows the user to enjoy sightseeing efficiently.

[0058] The tourism support system may further include a meal suggestion unit that suggests restaurants based on the user's dietary preferences. The meal suggestion unit considers the user's dietary preferences (e.g., vegetarian, vegan, gluten-free, etc.) and suggests optimal restaurants. For example, if the user is vegetarian, the meal suggestion unit may preferentially suggest restaurants that offer vegetarian menus. Also, if the user desires gluten-free meals, the meal suggestion unit may suggest restaurants that offer gluten-free menus. Furthermore, the meal suggestion unit may help the user discover new restaurants based on the user's dietary preferences. This allows the user to enjoy restaurants that match their dietary preferences.

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

[0060] Step 1: The acquisition unit acquires image data. The image data may be in formats such as JPEG, PNG, GIF, etc., but is not limited to these examples. The acquisition unit acquires, for example, map data or images from an image sharing service. Map data can be acquired from, for example, Yahoo! Maps or Google Maps. Images from an image sharing service can be acquired from, for example, Instagram or Flickr. Step 2: The collection unit acquires location information data. The location information data includes, but is not limited to, GPS data and Wi-Fi location information. The collection unit acquires, for example, the travel routes of people in the same situation. People in the same situation refer to, for example, wheelchair users and stroller users. Step 3: The collection unit acquires posting data. Posting data includes, but is not limited to, for example, social media posts, blog articles, and reviews on review sites. The collection unit acquires, for example, posting data from review sites, social media, and blogs. Review sites can be acquired from, for example, TripAdvisor and Yelp. Social media can be acquired from, for example, Facebook and Twitter. Blogs can be acquired from, for example, personal blogs and corporate blogs. Step 4: The collection unit acquires information about official websites. Information about official websites includes, but is not limited to, official websites of accommodations and restaurants. The collection unit acquires, for example, official websites of accommodations and restaurants and their user reviews. Accommodations can be acquired from, for example, hotels, private lodgings, guesthouses, etc. Restaurants can be acquired from, for example, restaurants, cafes, food trucks, etc. Step 5: The suggestion unit analyzes the data acquired by the acquisition unit and the collection unit and suggests an optimal sightseeing plan and necessary preparations to the user. The suggestion unit suggests, for example, routes that are easy for wheelchair users to navigate and restaurants that are easy to access with strollers. Routes that are easy for wheelchair users to navigate are selected based on, for example, whether there are barrier-free roads or elevators. Restaurants that are easy to access with strollers are selected based on, for example, whether there are wide aisles or baby chairs. The suggestion unit advises on necessary preparations in advance. Examples of necessary preparations in advance include, but are not limited to, a list of things to bring and reservation procedures.

[0061] (Example 2) A tourism support system according to an embodiment of the present invention uses generative AI to enable groups of people using wheelchairs or strollers to enjoy stress-free sightseeing by taking into account constraints such as transportation, travel routes, accommodations, and restaurants. This tourism support system is equipped with a function that proposes and advises on recommended itineraries and necessary preparations in advance. Specifically, the system eliminates the need for information gathering and solves the problem all at once by importing the following data: First, map data and images from image sharing services are imported as image data. This allows visual information on tourist spots and travel routes to be obtained. Next, location information data is imported from travel routes taken by people in similar situations. This allows information on routes actually traveled using wheelchairs or strollers. Furthermore, information from review sites, social media, blogs, etc. is imported as posted data. This allows impressions and ratings from actual users to be obtained. Finally, information on accommodations and restaurants, along with user reviews, is imported as official websites. This allows detailed information on accommodations and restaurants to be obtained. The generative AI analyzes this data and proposes and advises on optimal tourism plans and necessary preparations for users. For example, it can suggest routes that are easy for wheelchair users and restaurants that are stroller-friendly. It can also provide advice on necessary preparations in advance (for example, wheelchair rentals and whether strollers can be brought in). This system allows people who use wheelchairs or strollers to enjoy sightseeing without stress. Users can avoid the hassle of collecting information from multiple sources and can receive suggestions all at once from the generative AI, making trip planning go more smoothly. This allows the tourism support system to suggest optimal sightseeing plans and necessary preparations to users.

[0062] The tourism support system according to the embodiment includes an acquisition unit, a collection unit, and a suggestion unit. The acquisition unit acquires image data. The image data includes, but is not limited to, formats such as JPEG, PNG, and GIF. The acquisition unit acquires, for example, map data and images from an image sharing service. The map data can be acquired from, for example, Google Maps or OpenStreetMap. The images from the image sharing service can be acquired from, for example, Instagram or Flickr. The collection unit acquires location information data. The location information data includes, for example, GPS data and Wi-Fi location information, but is not limited to these examples. The collection unit acquires, for example, travel routes of people in the same situation. Examples of people in the same situation include wheelchair users and stroller users. The collection unit acquires posted data. The posted data includes, for example, social media posts, blog articles, and reviews on review sites, but is not limited to these examples. The collection unit acquires, for example, posted data from review sites, social media, and blogs. The review site information can be obtained from, for example, TripAdvisor or Yelp. The social networking sites can be obtained from, for example, Facebook or X (formerly Twitter). The blog information can be obtained from, for example, personal blogs or corporate blogs. The collection unit acquires information from official websites. Official website information includes, for example, official websites of accommodations and restaurants, but is not limited to these examples. The collection unit acquires, for example, official websites of accommodations and restaurants and their user reviews. The accommodations can be acquired from, for example, hotels, private lodgings, and guesthouses. The restaurants can be acquired from, for example, restaurants, cafes, and food trucks. The suggestion unit analyzes the data acquired by the acquisition unit and the collection unit and suggests optimal sightseeing plans and necessary preparations to the user. The suggestion unit suggests, for example, routes that are easy for wheelchair users to navigate and restaurants that are easy for strollers to access. Routes that are easy for wheelchair users to navigate are selected based on, for example, barrier-free roads and the presence or absence of elevators. Restaurants that are easy for strollers to access are selected based on, for example, wide aisles and the presence or absence of high chairs.The suggestion unit advises the user on necessary preparations in advance. The necessary preparations in advance include, but are not limited to, a list of belongings to bring and reservation procedures. This allows the tourism support system according to the embodiment to suggest an optimal sightseeing plan and necessary preparations to the user.

[0063] The acquisition unit can acquire map data or images from an image sharing service. The acquisition unit acquires, for example, map data. Examples of map data include Yahoo! Maps and Google. The acquisition unit acquires, for example, images from an image sharing service. Examples of image sharing services include Instagram and Flickr. By acquiring map data or images from an image sharing service, visual information about tourist spots and travel routes can be provided. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit inputs map data or images from an image sharing service into a generation AI and causes the generation AI to analyze the visual information.

[0064] The collection unit can acquire the travel routes of people in the same circumstances. The collection unit, for example, acquires the travel routes of people in the same circumstances. People in the same circumstances refer to, for example, wheelchair users and stroller users. The collection unit acquires the travel routes using, for example, GPS tracking data. The collection unit can also acquire travel routes through manual input by a user. For example, the collection unit collects travel route data manually entered by a user. By acquiring the travel routes of people in the same circumstances, it is possible to provide information on routes actually traveled using wheelchairs or strollers. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS tracking data into a generation AI and cause the generation AI to analyze the travel routes.

[0065] The collection unit can acquire posting data from review sites, SNS, and blogs. The collection unit, for example, acquires posting data from review sites. Review sites include, for example, TripAdvisor and Yelp. The collection unit, for example, acquires posting data from SNS. SNS includes, for example, Facebook and X (formerly Twitter). The collection unit, for example, acquires posting data from blogs. Blogs include, for example, personal blogs and corporate blogs. By acquiring posting data from review sites, SNS, blogs, etc., it is possible to provide impressions and evaluations from people who have actually used the services. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input posting data from review sites, SNS, and blogs into a generation AI and have the generation AI analyze the impressions and evaluations.

[0066] The collection unit can acquire the official website of an accommodation facility or a restaurant and its user reviews. The collection unit, for example, acquires the official website of an accommodation facility. Examples of accommodation facilities include hotels, guesthouses, and guesthouses. The collection unit, for example, acquires the official website of a restaurant. Examples of restaurants include restaurants, cafes, and food trucks. The collection unit, for example, acquires user reviews of accommodation facilities and restaurants. The user reviews include, for example, star ratings and comments. In this way, by acquiring the official website of an accommodation facility or a restaurant and its user reviews, detailed information about the accommodation facility or the restaurant can be provided. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the official website of an accommodation facility or a restaurant and its user reviews into a generation AI, and have the generation AI analyze the detailed information.

[0067] The suggestion unit analyzes the data acquired by the acquisition unit and the collection unit and can suggest routes that are easy for wheelchairs to navigate or restaurants that are easy to access with a stroller. The suggestion unit, for example, suggests routes that are easy for wheelchairs to navigate. Examples of routes that are easy for wheelchairs to navigate include barrier-free roads and the presence or absence of elevators. The suggestion unit, for example, suggests restaurants that are easy to access with a stroller. Examples of restaurants that are easy to access with a stroller include wide aisles and the presence or absence of baby chairs. This allows the user to be provided with an optimal sightseeing plan by suggesting routes that are easy for wheelchairs to navigate or restaurants that are easy to access with a stroller. Some or all of the above-mentioned processing by the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input the data acquired by the acquisition unit and the collection unit into a generation AI and cause the generation AI to propose an optimal sightseeing plan.

[0068] The suggestion unit can provide advice on necessary preparations in advance. For example, the suggestion unit provides advice on necessary preparations in advance. The necessary preparations in advance include, for example, a packing list and reservation procedures. By providing advice on necessary preparations in advance, the user can smoothly proceed with planning their trip. 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 input information on necessary preparations in advance into the generation AI and cause the generation AI to execute optimal advice.

[0069] The tourism support system further includes an acquisition unit that estimates the user's emotions and adjusts the timing of image data acquisition based on the estimated user emotions. For example, when the user is excited, the acquisition unit acquires the latest image data in real time. For example, when the user is relaxed, the acquisition unit acquires past image data and suggests a leisurely sightseeing plan. For example, when the user is stressed, the acquisition unit prioritizes acquiring image data that is useful for stress reduction. This allows the image data to be acquired at a more appropriate time by adjusting the timing of image data acquisition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI 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 acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of image data acquisition.

[0070] When acquiring image data, the acquisition unit can analyze the user's past travel history and select an appropriate acquisition method. For example, the acquisition unit prioritizes acquiring image data of tourist spots visited by the user in the past. The acquisition unit can also acquire image data of landscapes or buildings that the user has previously liked, for example. Furthermore, the acquisition unit can acquire image data taken in a specific season or time period from the user's past travel history, for example. This allows the optimal image data acquisition method to be selected by analyzing the user's past travel history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past travel history data into the generation AI and cause the generation AI to select the optimal image data acquisition method.

[0071] The acquisition unit can filter image data based on the user's current interests and concerns when acquiring the image data. For example, if the user is interested in natural landscapes, the acquisition unit can prioritize acquiring image data of natural landscapes. Furthermore, for example, if the user is interested in historical buildings, the acquisition unit can also acquire image data of historical buildings. Furthermore, for example, if the user is interested in gourmet food, the acquisition unit can also acquire image data of restaurants and dishes. By filtering the image data based on the user's current interests and concerns, more relevant image data can be acquired. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's current interests and concerns data into the generation AI and cause the generation AI to filter the image data.

[0072] The acquisition unit can estimate the user's emotions and determine the priority of image data to be acquired based on the estimated user's emotions. For example, when the user is excited, the acquisition unit can prioritize acquiring visually stimulating image data. Furthermore, when the user is relaxed, the acquisition unit can prioritize acquiring image data of calming scenery. Furthermore, when the user is stressed, the acquisition unit can prioritize acquiring image data that has a relaxing effect. This allows more appropriate image data to be acquired by determining the priority of image data based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of image data.

[0073] When acquiring image data, the acquisition unit can prioritize acquiring highly relevant images by taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring image data of tourist spots close to the user's current location. The acquisition unit can also prioritize acquiring image data of places the user plans to visit, for example. Furthermore, the acquisition unit can also prioritize acquiring image data near places the user has visited in the past, for example. In this way, highly relevant image data can be acquired preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information data to the generation AI and cause the generation AI to acquire highly relevant image data.

[0074] When acquiring image data, the acquisition unit can analyze the user's social media activity and acquire related images. For example, the acquisition unit can prioritize acquiring image data of locations shared by the user on SNS. The acquisition unit can also prioritize acquiring image data posted by accounts the user follows. Furthermore, the acquisition unit can prioritize acquiring image data that the user has "liked." This makes it possible to acquire related image data by analyzing the user's social media activity. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related image data.

[0075] The collection unit can estimate the user's emotions and adjust the timing of acquiring location information data based on the estimated user emotions. For example, when the user is excited, the collection unit acquires the latest location information data in real time. Furthermore, when the user is relaxed, the collection unit can acquire past location information data and suggest a leisurely sightseeing plan. Furthermore, when the user is stressed, the collection unit can prioritize acquiring location information data that is useful for stress reduction. By adjusting the timing of acquiring location information data based on the user's emotions, location information data can be acquired at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of acquiring location information data.

[0076] When acquiring location information data, the collection unit can analyze the user's past movement history and select an appropriate acquisition method. For example, the collection unit prioritizes acquiring location information data of tourist spots visited by the user in the past. The collection unit can also acquire location information data of travel routes preferred by the user in the past. Furthermore, the collection unit can acquire location information data of routes traveled during specific seasons or time periods from the user's past movement history, for example. This allows the optimal method for acquiring location information data to be selected by analyzing the user's past movement history. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past movement history data into a generation AI and cause the generation AI to select an optimal method for acquiring location information data.

[0077] When acquiring location information data, the collection unit can filter the location information data based on the user's current living situation and areas of interest. For example, the collection unit prioritizes acquiring location information data of routes that are easy for the user to travel based on the user's current living situation. The collection unit can also prioritize acquiring location information data of tourist destinations that the user is interested in, for example. Furthermore, the collection unit can also acquire location information data of tourist destinations that are easy to access based on the user's current living situation. This allows more relevant location information data to be acquired by filtering the location information data based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current living situation and areas of interest data to the generation AI and cause the generation AI to filter the location information data.

[0078] The collection unit can estimate the user's emotions and determine the priority of location information data to be acquired based on the estimated user's emotions. For example, when the user is excited, the collection unit can prioritize acquiring visually stimulating location information data. Furthermore, for example, when the user is relaxed, the collection unit can prioritize acquiring location information data of calming scenery. Furthermore, for example, when the user is stressed, the collection unit can prioritize acquiring location information data that has a relaxing effect. Thus, by determining the priority of location information data based on the user's emotions, more appropriate location information data can be acquired. 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 collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the location information data.

[0079] When acquiring location information data, the collection unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes acquiring location information data of tourist spots close to the user's current location. The collection unit can also prioritize acquiring location information data of places the user plans to visit, for example. Furthermore, the collection unit can also prioritize acquiring location information data near places the user has visited in the past, for example. This makes it possible to prioritize acquiring highly relevant location information data by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to a generation AI and cause the generation AI to acquire highly relevant location information data.

[0080] When acquiring location information data, the collection unit can analyze the user's social media activity and acquire related data. For example, the collection unit prioritizes acquiring location information data of locations shared by the user on social media. The collection unit can also acquire location information data posted by accounts the user follows. Furthermore, the collection unit can prioritize acquiring location information data of locations the user has "liked." This allows related location information data to be acquired by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to acquire related location information data.

[0081] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can make simple, highly visible suggestions. Furthermore, for example, if the user is relaxed, the suggestion unit can make suggestions that include detailed information. Furthermore, for example, if the user is in a hurry, the suggestion unit can make suggestions that focus on the main points. This allows for more appropriate suggestions to be made by adjusting the way the suggestions are expressed based on the user's emotions. The estimation of emotions is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0082] The suggestion unit can adjust the level of detail of the proposal based on the importance of the sightseeing plan when making the proposal. For example, the suggestion unit can provide detailed information for sightseeing plans with high importance. The suggestion unit can also provide concise information for sightseeing plans with low importance. Furthermore, the suggestion unit can, for example, gradually adjust the level of detail of the proposal according to the importance. This makes it possible to provide optimal information for the user by adjusting the level of detail of the proposal based on the importance of the sightseeing plan. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input importance data of the sightseeing plan to the generation AI and have the generation AI adjust the level of detail of the proposal.

[0083] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the category of the tour plan. For example, the suggestion unit can apply a proposal algorithm specialized for natural landscapes to a nature tour plan. The suggestion unit can also apply a proposal algorithm specialized for historical buildings to a history tour plan. The suggestion unit can also apply a proposal algorithm specialized for restaurants and cuisine to a gourmet tour plan. This allows for more appropriate proposals by applying different proposal algorithms depending on the category of the tour plan. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input category data of the tour plan into the generation AI and cause the generation AI to apply different proposal algorithms.

[0084] 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. For example, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. For example, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for more appropriate suggestions 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, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0085] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the tour plan. For example, the suggestion unit can prioritize the most recent tour plan. The suggestion unit can also provide detailed information for tour plans submitted earlier. Furthermore, the suggestion unit can gradually adjust the priority of the proposal, for example, depending on the time of submission. This allows the user to be provided with optimal information by determining the priority of the proposal based on the time of submission of the tour plan. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the time of submission of the tour plan into the generation AI and have the generation AI determine the priority of the proposals.

[0086] The suggestion unit can adjust the order of suggestions based on the relevance of the sightseeing plans when making suggestions. For example, the suggestion unit can prioritize suggestions for highly relevant sightseeing plans. The suggestion unit can also provide concise information for less relevant sightseeing plans. Furthermore, the suggestion unit can gradually adjust the order of suggestions based on the relevance, for example. This makes it possible to provide optimal information for the user by adjusting the order of suggestions based on the relevance of the sightseeing plans. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input relevance data of the sightseeing plans into the generation AI and have the generation AI adjust the order of suggestions.

[0087] The suggestion unit can estimate the user's emotions and adjust the advance preparation advice based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible advice. Furthermore, for example, if the user is relaxed, the suggestion unit can provide advice including detailed information. Furthermore, for example, if the user is in a hurry, the suggestion unit can provide advice that focuses on the main points. This allows for more appropriate advice to be provided by adjusting the advance preparation advice based on the user's emotions. The emotion estimation is achieved 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 suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the advance preparation advice.

[0088] When providing advance preparation advice, the suggestion unit can provide appropriate advice by referring to the user's past travel history. The suggestion unit provides advice based on, for example, services and facilities the user has used in the past. The suggestion unit can also provide advice by predicting necessary preparations based on, for example, the user's past travel history. Furthermore, the suggestion unit can also analyze, for example, the user's past travel history and suggest the most efficient preparation method. This makes it possible to provide optimal advance preparation advice by referring to the user's past travel history. 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 input the user's past travel history data into the generation AI and cause the generation AI to execute optimal advance preparation advice.

[0089] The suggestion unit can customize the content of the advice based on the user's current living situation when providing advance preparation advice. The suggestion unit, for example, customizes the necessary preparations based on the user's current living situation. The suggestion unit can also suggest an optimal preparation method according to the user's current living situation. The suggestion unit can also adjust the content of the advice, for example, taking into account the user's current living situation. This allows for more appropriate advice to be provided by customizing the content of the advice based on the user's current living situation. 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 input the user's current living situation data into the generation AI and cause the generation AI to customize the content of the advice.

[0090] The suggestion unit can estimate the user's emotions and determine the priority of advance preparations based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can prioritize important preparation items. Furthermore, for example, if the user is relaxed, the suggestion unit can provide step-by-step advice on detailed preparation items. Furthermore, for example, if the user is in a hurry, the suggestion unit can provide concise advice on the most important preparation items. This allows for more appropriate advice by determining the priority of advance preparations based on the user's emotions. The emotion estimation is achieved 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 suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of advance preparations.

[0091] When providing advance preparation advice, the suggestion unit can provide appropriate advice taking into account the user's geographical location information. The suggestion unit, for example, advises the user on the optimal preparation method based on their current location. The suggestion unit can also advise the user on the necessary preparations based on, for example, the places the user plans to visit. Furthermore, the suggestion unit can also suggest the optimal preparation method taking into account the user's geographical location information. This makes it possible to provide optimal advance preparation advice by taking into account the user's geographical location information. 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 can input the user's geographical location information data into the generation AI and cause the generation AI to execute optimal advance preparation advice.

[0092] When providing advance preparation advice, the suggestion unit can analyze the user's social media activity and provide appropriate advice. The suggestion unit can advise the optimal preparation method based on, for example, information shared by the user on social media. The suggestion unit can also suggest a preparation method by referring to, for example, information on accounts the user follows. Furthermore, the suggestion unit can also advise on necessary preparations based on, for example, posts the user has "liked." In this way, optimal advance preparation advice can be provided by analyzing the user's social media activity. 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 can input the user's social media activity data into a generation AI and cause the generation AI to execute optimal advance preparation advice. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, collection unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires image data using the camera 42 and communication I / F 44 of the smart device 14, and the image data is analyzed by the specific processing unit 290 of the data processing device 12. The collection unit acquires location information data and posted data via the communication I / F 44 of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal sightseeing plans and necessary preparations to the user. Furthermore, the acquisition unit has a function of estimating the user's emotions and adjusting the timing of image data acquisition based on the estimated emotions, and is realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, collection unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires image data using the camera 42 and communication I / F 44 of the smart glasses 214, and the image data is analyzed by the specific processing unit 290 of the data processing device 12. The collection unit acquires location information data and posted data via the communication I / F 44 of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal sightseeing plans and necessary preparations to the user. Furthermore, the acquisition unit has a function of estimating the user's emotions and adjusting the timing of image data acquisition based on the estimated emotions, and is realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, collection unit, and suggestion unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit acquires image data using the camera 42 and communication I / F 44 of the headset terminal 314, and the image data is analyzed by the specific processing unit 290 of the data processing device 12. The collection unit acquires location information data and posted data via the communication I / F 44 of the headset terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal sightseeing plans and necessary preparations to the user. Furthermore, the acquisition unit has a function of estimating the user's emotions and adjusting the timing of image data acquisition based on the estimated emotions, and is realized, for example, by the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, collection unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires image data using the camera 42 and communication I / F 44 of the robot 414, and the image data is analyzed by the specific processing unit 290 of the data processing device 12. The collection unit acquires location information data and posted data via the communication I / F 44 of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal sightseeing plans and necessary preparations to the user. Furthermore, the acquisition unit has a function of estimating the user's emotions and adjusting the timing of acquiring image data based on the estimated emotions, and is realized, for example, by the control unit 46A 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] The tourism support system may further include a health management unit that monitors the user's health condition. The health management unit acquires data such as the user's heart rate, blood pressure, and number of steps, and monitors the user's health condition in real time based on this data. For example, if the user's heart rate suddenly increases, the health management unit can send the user a notification urging them to take a break. Also, if the user's number of steps exceeds a certain threshold, the health management unit can suggest that the user take a break. Furthermore, the health management unit can adjust the sightseeing plan based on the user's health condition and suggest a reasonable schedule. This allows the user to enjoy sightseeing while maintaining their health.

[0095] The tourism support system may further include a learning unit that learns the user's preferences. The learning unit analyzes the user's past selections and behavioral data to learn the user's preferences. For example, it can predict the tourist spots and services that the user will like based on data on tourist spots visited by the user in the past and services used by the user. The learning unit can also suggest new tourist spots and services based on the user's preferences. Furthermore, the learning unit can collect user feedback and improve the accuracy of suggestions. This allows the user to enjoy a sightseeing plan that suits their preferences.

[0096] The tourism support system can further include a congestion prediction unit that estimates the user's emotions and considers the congestion situation at tourist spots based on the estimated user emotions. If the user is feeling stressed, the congestion prediction unit can preferentially suggest less crowded tourist spots. Also, if the user is excited, it can suggest popular tourist spots. Furthermore, the congestion prediction unit can acquire real-time congestion situation data and suggest the most suitable tourist spots to the user. This allows the user to choose tourist spots that match their emotions.

[0097] The tourism support system can further include a budget management unit that manages the user's travel budget. The budget management unit allows the user to input their travel budget and proposes the optimal sightseeing plan within that budget. For example, the budget management unit can create a sightseeing plan that fits within the budget, taking into account expenses such as accommodation, transportation, and food. The budget management unit can also issue an alert if the user is likely to exceed their budget. Furthermore, the budget management unit can record the user's spending history and support budget management. This allows the user to enjoy their trip without worrying about their budget.

[0098] The tourism support system may further include a safety information providing unit that estimates the user's emotions and provides safety information about tourist spots based on the estimated user emotions. If the user feels anxious, the safety information providing unit may preferentially suggest safe tourist spots. If the user feels relaxed, the safety information providing unit may also suggest adventurous tourist spots. Furthermore, the safety information providing unit may obtain real-time safety information and provide the user with the latest safety information. This allows the user to enjoy sightseeing with peace of mind.

[0099] The tourism support system may further include a language support unit that provides multilingual support based on the user's language setting. The language support unit provides tourism information based on the user's language setting. For example, the language support unit may display descriptions and guides of tourist attractions in the user's native language. The language support unit may also perform real-time translation when the user speaks a different language. Furthermore, the language support unit may provide simple phrases for learning the local language based on the user's language setting. This allows the user to enjoy sightseeing without experiencing language barriers.

[0100] The tourism support system may further include a weather information providing unit that estimates the user's emotions and provides weather information for tourist destinations based on the estimated user emotions. If the user is feeling anxious, the weather information providing unit may preferentially suggest tourist destinations with stable weather. If the user is excited, the weather information providing unit may also suggest attractive tourist destinations with changeable weather. Furthermore, the weather information providing unit may obtain real-time weather information and provide the user with the latest weather information. This allows the user to make sightseeing plans that take the weather into account.

[0101] The tourism support system can further include a transportation optimization unit that optimizes the user's transportation means. The transportation optimization unit considers the user's transportation means (e.g., car, train, bus, bicycle, etc.) and proposes an optimal transportation route. For example, if the user uses a car, the transportation optimization unit can propose a route taking into consideration the availability of parking spaces and traffic congestion information. Also, if the user uses a train, the transportation optimization unit can propose a route taking into consideration transfer information and operation status. Furthermore, the transportation optimization unit can also propose optimal tourist destinations depending on the user's transportation means. This allows the user to enjoy sightseeing efficiently.

[0102] The tourism support system may further include an event information providing unit that estimates the user's emotions and provides event information about tourist spots based on the estimated user emotions. If the user is excited, the event information providing unit may preferentially suggest lively events. If the user is relaxed, the event information providing unit may also suggest quiet events. Furthermore, the event information providing unit may acquire real-time event information and provide the user with the latest event information. This allows the user to enjoy events that match their emotions.

[0103] The tourism support system may further include a meal suggestion unit that suggests restaurants based on the user's dietary preferences. The meal suggestion unit considers the user's dietary preferences (e.g., vegetarian, vegan, gluten-free, etc.) and suggests optimal restaurants. For example, if the user is vegetarian, the meal suggestion unit may preferentially suggest restaurants that offer vegetarian menus. Also, if the user desires gluten-free meals, the meal suggestion unit may suggest restaurants that offer gluten-free menus. Furthermore, the meal suggestion unit may help the user discover new restaurants based on the user's dietary preferences. This allows the user to enjoy restaurants that match their dietary preferences.

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

[0105] Step 1: The acquisition unit acquires image data. The image data may be in formats such as JPEG, PNG, GIF, etc., but is not limited to these examples. The acquisition unit acquires, for example, map data or images from an image sharing service. Map data can be acquired from, for example, Yahoo! Maps or Google Maps. Images from an image sharing service can be acquired from, for example, Instagram or Flickr. Step 2: The collection unit acquires location information data. The location information data includes, but is not limited to, GPS data and Wi-Fi location information. The collection unit acquires, for example, the travel routes of people in the same situation. People in the same situation refer to, for example, wheelchair users and stroller users. Step 3: The collection unit acquires posting data. Posting data includes, but is not limited to, for example, social media posts, blog articles, and reviews on review sites. The collection unit acquires, for example, posting data from review sites, social media, and blogs. Review sites can be acquired from, for example, TripAdvisor and Yelp. Social media can be acquired from, for example, Facebook and Twitter. Blogs can be acquired from, for example, personal blogs and corporate blogs. Step 4: The collection unit acquires information about official websites. Information about official websites includes, but is not limited to, official websites of accommodations and restaurants. The collection unit acquires, for example, official websites of accommodations and restaurants and their user reviews. Accommodations can be acquired from, for example, hotels, private lodgings, guesthouses, etc. Restaurants can be acquired from, for example, restaurants, cafes, food trucks, etc. Step 5: The suggestion unit analyzes the data acquired by the acquisition unit and the collection unit and suggests an optimal sightseeing plan and necessary preparations to the user. The suggestion unit suggests, for example, routes that are easy for wheelchair users to navigate and restaurants that are easy to access with strollers. Routes that are easy for wheelchair users to navigate are selected based on, for example, whether there are barrier-free roads or elevators. Restaurants that are easy to access with strollers are selected based on, for example, whether there are wide aisles or baby chairs. The suggestion unit advises on necessary preparations in advance. Examples of necessary preparations in advance include, but are not limited to, a list of things to bring and reservation procedures.

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

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

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

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

[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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[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, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[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. an acquisition unit that acquires image data; a collection unit that acquires location information data, posting data, and official website information; a suggestion unit that analyzes the data acquired by the acquisition unit and the collection unit and suggests a sightseeing plan and necessary preparations suitable for the user. A system characterized by:

2. The acquisition unit Obtaining map data or images from image sharing services The system of claim 1 .

3. The collecting unit Obtaining the travel routes of people in the same situation 2. The system of claim 1.

4. The collecting unit Acquire posting data from review sites, social media, and blogs The system of claim 1 .

5. The collecting unit Obtaining official websites of accommodations or restaurants and their user reviews The system of claim 1 .

6. The proposal unit The data acquired by the acquisition unit and the collection unit is analyzed, and a route that is easy for a wheelchair to travel or a restaurant that is easy to use with a stroller is suggested. The system of claim 1 .

7. The proposal unit Advise on necessary preparations in advance 2. The system of claim 1.

8. The acquisition unit A method for estimating user emotion and adjusting timing of acquiring image data based on the estimated user emotion The system of claim 1 .

Images