system

Abstract

The system according to the embodiment aims to analyze travel information of a user and propose an optimal travel schedule. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects travel information of a user. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a travel schedule based on the information analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to collect a large amount of information when planning a trip and create an optimal schedule.

[0005] The system according to the embodiment aims to analyze travel information of a user and propose an optimal travel schedule. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects travel information of a user. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a travel schedule based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's travel information and propose an optimal travel schedule. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel schedule suggestion system according to an embodiment of the present invention collects and analyzes a user's travel information to suggest an optimal travel schedule. The system makes suggestions based on access methods to destinations, budgets, best accommodations, recommended shops and restaurants along the way, and other information. Furthermore, the system suggests travel plans tailored to various needs, such as ticket reservations, luggage storage options, and information for families with children. For example, a user inputs their travel destination and departure point, along with their budget and desired accommodation options. This information is then input into a generation AI, which analyzes the information and generates an optimal schedule. For example, the system suggests optimal access methods to destinations and accommodations that fit the user's budget. The system also suggests recommended shops and restaurants along the way, such as restaurants serving local specialties or shops near tourist attractions. Furthermore, the generation AI considers ticket reservations, luggage storage options, and information for families with children when making suggestions. For example, the system considers child-friendly activities and stroller rentals. This content allows users to find the optimal travel schedule tailored to their needs. For example, for a family trip, the system suggests child-friendly activities and family-friendly accommodations. For business trips, the system will suggest efficient transportation methods and business-friendly accommodations. In this way, content that provides optimal schedules to enrich the trip will meet the diverse needs of users and make the trip more enjoyable. This allows the travel schedule suggestion system to efficiently collect, analyze, and suggest travel information for users.

[0029] A travel schedule proposal system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects travel information about a user. The user's travel information may include, but is not limited to, information about transportation, accommodations, and tourist attractions. The collection unit may collect the user's travel information using, for example, a questionnaire survey. The collection unit may also collect travel information from the Internet using web scraping technology. For example, the collection unit may automatically collect information from travel sites and review sites. The collection unit may also analyze the user's social media activities to collect related travel information. For example, the collection unit may collect information about tourist attractions and activities that the user has "liked" or shared on social media. The analysis unit analyzes the information collected by the collection unit. The analysis may be performed using, for example, data mining technology, but is not limited to, an example. For example, the analysis unit may statistically analyze the collected travel information to understand the user's preferences and trends. The analysis unit may also analyze the user's travel information using a machine learning algorithm. For example, the analysis unit may predict future travel destinations based on the user's past travel history. Furthermore, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is relaxed, the analysis unit displays the analysis results in a calm tone. The suggestion unit proposes a travel schedule based on the information analyzed by the analysis unit. The suggestion may be, for example, an automatic suggestion using an algorithm, but is not limited to such an example. For example, the suggestion unit proposes an optimal travel schedule based on the user's budget and desired accommodation conditions. The suggestion unit can also propose a travel schedule based on expert advice. For example, the suggestion unit proposes tourist spots and activities recommended by travel experts. This allows the travel schedule suggestion system according to the embodiment to efficiently collect, analyze, and suggest travel information for the user.

[0030] The collection unit can collect the user's budget and desired accommodation conditions. The collection unit, for example, collects budget information entered by the user. For example, the collection unit collects the budget range set by the user (e.g., under 10,000 yen, over 50,000 yen). The collection unit can also collect the user's desired accommodation conditions. For example, the collection unit collects the user's desired hotel rank (e.g., 5-star hotel, 3-star hotel) and room type (e.g., single room, suite). Furthermore, the collection unit can collect the user's desired accommodation facilities (e.g., pool, gym, spa). This allows the collection unit to collect travel information according to the user's budget and preferences. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's input budget information to a generation AI and cause the generation AI to analyze the budget information.

[0031] The suggestion unit can suggest recommended shops and restaurants along the way. For example, the suggestion unit can suggest recommended shops along the way based on the user's travel route. For example, the suggestion unit can suggest popular shops near tourist spots the user visits. The suggestion unit can also suggest recommended restaurants based on the user's preferences. For example, the suggestion unit can suggest restaurants based on the user's preferred cuisine (e.g., Japanese cuisine, Italian cuisine) or price range (e.g., fine dining, casual dining). The suggestion unit can also suggest recommended shops and restaurants based on the user's review ratings. For example, the suggestion unit can prioritize suggesting shops and restaurants that the user has given high ratings. This allows the suggestion unit to make suggestions to increase the enjoyment of the trip. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input information about the user's travel route into a generation AI and cause the generation AI to suggest recommended shops and restaurants along the way.

[0032] The suggestion unit can check the availability of ticket reservations and luggage storage. For example, the suggestion unit can check the reservation status of tickets desired by the user. For example, the suggestion unit can check ticket availability through an online reservation system. The suggestion unit can also check the availability of luggage storage services desired by the user. For example, the suggestion unit can check the availability of luggage storage services at station lockers or hotel front desks. Furthermore, the suggestion unit can suggest the optimal timing for ticket reservations and luggage storage based on the user's travel schedule. For example, the suggestion unit can suggest the timing for reserving tickets and storing luggage before the user arrives. This allows the suggestion unit to make suggestions that improve the convenience of travel. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's travel schedule information into the generation AI and cause the generation AI to check the availability of ticket reservations and luggage storage.

[0033] The suggestion unit can provide information for families. For example, if a user is planning a family trip, the suggestion unit can suggest activities for children. For example, the suggestion unit can provide information on theme parks and zoos that children can enjoy. The suggestion unit can also suggest accommodations for families. For example, the suggestion unit can suggest hotels with room types and facilities for families (e.g., family rooms, kids' clubs). The suggestion unit can also provide family discount and benefit information. For example, the suggestion unit can suggest discount plans and benefits that families can use. This allows the suggestion unit to make suggestions that improve the convenience of family trips. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the user's family trip information into the generation AI and cause the generation AI to provide information for families.

[0034] The collection unit can analyze the user's past travel history and select a collection method. For example, the collection unit can suggest similar travel destinations based on places the user has visited in the past. For example, the collection unit can analyze information on tourist spots and accommodations the user has visited in the past and collect similar travel destinations. The collection unit can also identify preferred types of accommodations from the user's past travel history and narrow down the information to be collected. For example, the collection unit can collect information on similar accommodations based on the rank and room type of hotels the user has used in the past. Furthermore, the collection unit can collect optimal access methods based on the transportation methods the user has used in the past. For example, the collection unit can collect information on similar transportation methods based on information on airplanes and trains the user has used in the past. This allows the collection unit to collect optimal travel information based on the past travel history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past travel history data into a generation AI and have the generation AI select an optimal collection method.

[0035] When collecting travel information, the collection unit can filter the travel information based on the user's current living situation and areas of interest. For example, if the user is busy with their current living situation, the collection unit collects travel information that can be enjoyed in a short period of time. For example, the collection unit collects information on weekend trips and day trips that the user can enjoy in between work. Furthermore, if the user has a specific area of ​​interest (e.g., history or nature), the collection unit can prioritize collecting travel information related to that area. For example, the collection unit collects information on historical tourist spots and natural parks. Furthermore, if the user is interested in health, the collection unit can collect travel information that offers healthy activities and meals. For example, the collection unit collects information on yoga retreats and organic restaurants. This allows the collection unit to collect travel information tailored to the user's current situation and interests. 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 data on the user's living situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0036] When collecting travel information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, the collection unit prioritizes collecting information about tourist attractions and activities close to the user's current location. For example, the collection unit collects information about tourist attractions that the user can visit within walking distance of the user's current location. Furthermore, if the user is staying in a specific area, the collection unit can prioritize collecting information about events and local specialties in that area. For example, the collection unit collects information about festivals held in the area where the user is staying and information about shops introducing local specialties. Furthermore, if the user is traveling, the collection unit can prioritize collecting information about tourist attractions and restaurants along the user's travel route. For example, the collection unit collects information about tourist attractions and restaurants that the user can stop at while traveling by car. This allows the collection unit to collect highly relevant travel information based on the user's current location. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0037] When collecting travel information, the collection unit can collect related information based on the user's social media activities. For example, the collection unit collects information on tourist attractions and activities that the user has "liked" or shared on social media. For example, the collection unit prioritizes collecting information on tourist attractions and activities that the user has "liked." The collection unit can also collect information on travel destinations and accommodations introduced by influencers the user follows. For example, the collection unit collects information on tourist attractions and accommodations recommended by influencers the user follows. Furthermore, the collection unit can collect information on travel destinations and activities that the user is interested in from photos and comments posted on social media. For example, the collection unit analyzes photos and comments posted by the user to collect related travel information. This allows the collection unit to collect highly relevant travel information based on the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis according to the importance of the travel information. The analysis unit, for example, performs a detailed analysis of important travel information (e.g., ticket reservations, accommodations). For example, the analysis unit may analyze ticket reservation information in detail and suggest the optimal reservation method and timing. The analysis unit may also analyze accommodation information in detail and suggest accommodations that meet the user's preferences. Furthermore, the analysis unit can perform a simplified analysis of less important information (e.g., shops and restaurants along the way). For example, the analysis unit may suggest a concise summary of information about shops and restaurants along the way. This allows the analysis unit to perform a detailed analysis according to the importance of the travel information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input travel information importance data to the generation AI and cause the generation AI to adjust the level of detail based on the importance.

[0039] During analysis, the analysis unit can apply different analysis algorithms based on the category of travel information. For example, for accommodation information, the analysis unit applies an analysis algorithm based on the user's past accommodation history. For example, the analysis unit may suggest similar accommodations based on information about hotels where the user has stayed in the past. Furthermore, for restaurant information, the analysis unit can apply an analysis algorithm that takes into account the user's food preferences. For example, the analysis unit may suggest restaurants based on the type of cuisine and price range the user prefers. Furthermore, for activity information, the analysis unit can apply an analysis algorithm that reflects the user's interests. For example, the analysis unit may suggest information about activities and events that the user is interested in. This allows the analysis unit to perform optimal analysis according to the category of travel information. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input category data of the travel information into the generation AI and cause the generation AI to apply an analysis algorithm based on the category.

[0040] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the travel information. For example, the analysis unit prioritizes analysis of the most recently submitted travel information. For example, the analysis unit prioritizes analysis of the travel information most recently submitted by the user. The analysis unit can also determine the analysis priority based on a deadline specified by the user. For example, the analysis unit determines the analysis priority based on the travel departure date or reservation deadline specified by the user. Furthermore, the analysis unit can prioritize analysis of information with an approaching travel departure date. For example, the analysis unit prioritizes analysis of information with an approaching travel departure date and quickly makes suggestions. This allows the analysis unit to perform analysis with a priority based on the time of submission. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the time of submission of travel information to the generation AI and cause the generation AI to determine the priority based on the time of submission.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the travel information. For example, the analysis unit prioritizes analysis of information that the user is highly interested in. For example, the analysis unit prioritizes analysis of information about tourist spots and activities that the user is particularly interested in. The analysis unit can also prioritize analysis of information related to travel destinations. For example, the analysis unit prioritizes analysis of information about tourist spots and accommodations that the user plans to visit. Furthermore, the analysis unit can prioritize analysis of highly relevant information based on the user's past travel history. For example, the analysis unit prioritizes analysis of information related to places the user has visited or services the user has used. This allows the analysis unit to perform analysis in an order based on relevance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of travel information to the generation AI and cause the generation AI to perform the analysis order based on relevance.

[0042] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the travel information. The suggestion unit, for example, makes detailed suggestions for important travel information (e.g., ticket reservations, accommodations). For example, the suggestion unit may suggest detailed information about ticket reservations and provide the optimal reservation method and timing. The suggestion unit may also suggest detailed information about accommodations and provide accommodations that meet the user's preferences. Furthermore, the suggestion unit can make simplified suggestions for less important information (e.g., shops and restaurants along the way). For example, the suggestion unit may suggest information about shops and restaurants along the way in a concise summary. This allows the suggestion unit to make detailed suggestions based on the importance of the travel information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input importance data of the travel information to the generation AI and cause the generation AI to adjust the level of detail based on the importance.

[0043] When making a suggestion, the suggestion unit can apply different suggestion algorithms based on the category of the travel information. For example, for accommodation information, the suggestion unit applies a suggestion algorithm based on the user's past accommodation history. For example, the suggestion unit can suggest similar accommodations based on information about hotels where the user has stayed in the past. The suggestion unit can also apply a suggestion algorithm that takes the user's food preferences into account for restaurant information. For example, the suggestion unit can suggest restaurants based on the type of cuisine and price range the user prefers. Furthermore, the suggestion unit can apply a suggestion algorithm that reflects the user's interests and concerns for activity information. For example, the suggestion unit can suggest information about activities and events that the user is interested in. This allows the suggestion unit to make optimal suggestions according to the category of the travel information. 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 category data of the travel information into the generation AI and cause the generation AI to apply a suggestion algorithm based on the category.

[0044] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the travel information. For example, the suggestion unit prioritizes the most recently submitted travel information. For example, the suggestion unit prioritizes the most recently submitted travel information. The suggestion unit can also determine the priority of the proposal based on a deadline specified by the user. For example, the suggestion unit prioritizes the proposal based on the travel departure date or reservation deadline specified by the user. Furthermore, the suggestion unit can prioritize the proposal of information that is close to the travel departure date. For example, the suggestion unit prioritizes the proposal of information that is close to the user's travel departure date and makes the proposal quickly. This allows the suggestion unit to prioritize the proposal based on the time of submission. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input data on the time of submission of the travel information to the generation AI and cause the generation AI to determine the priority based on the time of submission.

[0045] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the travel information. For example, the suggestion unit prioritizes suggesting information that is of high interest to the user. For example, the suggestion unit prioritizes suggesting information about tourist spots and activities that the user is particularly interested in. The suggestion unit can also prioritize suggesting information related to travel destinations. For example, the suggestion unit prioritizes suggesting information about tourist spots and accommodations that the user plans to visit. Furthermore, the suggestion unit can prioritize suggesting highly relevant information based on the user's past travel history. For example, the suggestion unit prioritizes suggesting information related to places the user has visited or services the user has used. This allows the suggestion unit to make suggestions in an order based on relevance. 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 relevance data of travel information to a generation AI and cause the generation AI to execute the order of suggestions based on relevance.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The analysis unit can take the user's health condition into consideration when analyzing the user's travel information. For example, if the user provides health checkup results or fitness tracker data, the analysis unit can suggest health management options at the travel destination based on that information. Specifically, if the user has high blood pressure, the analysis unit can suggest restaurants that serve low-salt meals or relaxing spa facilities. Also, if the user has allergies, the analysis unit can suggest restaurants that serve allergen-free meals or allergy-friendly accommodations. Furthermore, if the user is interested in fitness, the analysis unit can provide information on running courses and gyms at the travel destination. This allows the analysis unit to suggest an optimal travel schedule based on the user's health condition.

[0048] The suggestion unit can take the user's hobbies and interests into consideration when proposing a travel schedule for the user. For example, if the user is interested in art, information on art museums and galleries at the travel destination can be provided. If the user likes outdoor activities, information on hiking trails and campsites can be proposed. Furthermore, if the user enjoys music, information on concerts and live events held at the travel destination can be provided. In this way, the suggestion unit can propose a travel schedule that suits the user's hobbies and interests.

[0049] The collection unit can take into account the user's past reviews and ratings when collecting the user's travel information. For example, it can prioritize collection of information on accommodations and restaurants that the user has previously rated highly. It can also collect information to help users avoid places that the user has previously rated poorly. Furthermore, it can analyze reviews and comments posted by the user in the past to understand the user's preferences and tendencies. This allows the collection unit to collect optimal travel information based on the user's past ratings.

[0050] The suggestion unit can take the user's travel purpose into consideration when proposing a travel schedule for the user. For example, if the user is planning a trip for relaxation, information on relaxing spas and hot springs can be provided. If the user is planning an adventure-seeking trip, information on active activities and sports can be proposed. Furthermore, if the user is planning a cultural experience trip, information on events and facilities where users can experience local culture and traditions can be provided. This allows the suggestion unit to propose an optimal travel schedule according to the user's travel purpose.

[0051] The analysis unit can take the user's travel purpose into consideration when analyzing the user's travel information. For example, if the user is planning a trip for relaxation, the analysis unit can prioritize information on relaxing activities and accommodations. If the user is planning an adventure-seeking trip, the analysis unit can prioritize information on active activities and sports. Furthermore, if the user is planning a cultural experience trip, the analysis unit can prioritize information on events and facilities where users can experience local culture and traditions. This allows the analysis unit to perform optimal analysis according to the user's travel purpose.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The collection unit collects the user's travel information. The user's travel information includes information on transportation, accommodation, tourist attractions, etc. The collection unit collects the user's travel information using a questionnaire survey. Travel information on the Internet can also be collected using web scraping technology. For example, information can be automatically collected from travel sites and review sites. Furthermore, the collection unit can analyze the user's social media activity and collect related travel information. For example, information on tourist attractions and activities that the user has "liked" or shared on social media can be collected. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using data mining technology. For example, the collected travel information is statistically analyzed to understand the user's preferences and trends. The analysis unit can also analyze the user's travel information using machine learning algorithms. For example, it can predict future travel destinations based on the user's past travel history. Furthermore, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is relaxed, the analysis results can be displayed in a calm tone. Step 3: The suggestion unit proposes a travel itinerary based on the information analyzed by the analysis unit. Suggestions are made automatically by an algorithm. For example, it proposes an optimal travel itinerary based on the user's budget and desired accommodation conditions. It can also propose travel itineraries based on expert advice. For example, it proposes tourist spots and activities recommended by travel experts.

[0054] (Example 2) A travel schedule suggestion system according to an embodiment of the present invention collects and analyzes a user's travel information to suggest an optimal travel schedule. The system makes suggestions based on access methods to destinations, budgets, best accommodations, recommended shops and restaurants along the way, and other information. Furthermore, the system suggests travel plans tailored to various needs, such as ticket reservations, luggage storage options, and information for families with children. For example, a user inputs their travel destination and departure point, along with their budget and desired accommodation options. This information is then input into a generation AI, which analyzes the information and generates an optimal schedule. For example, the system suggests optimal access methods to destinations and accommodations that fit the user's budget. The system also suggests recommended shops and restaurants along the way, such as restaurants serving local specialties or shops near tourist attractions. Furthermore, the generation AI considers ticket reservations, luggage storage options, and information for families with children when making suggestions. For example, the system considers child-friendly activities and stroller rentals. This content allows users to find the optimal travel schedule tailored to their needs. For example, for a family trip, the system suggests child-friendly activities and family-friendly accommodations. For business trips, the system will suggest efficient transportation methods and business-friendly accommodations. In this way, content that provides optimal schedules to enrich the trip will meet the diverse needs of users and make the trip more enjoyable. This allows the travel schedule suggestion system to efficiently collect, analyze, and suggest travel information for users.

[0055] A travel schedule proposal system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects travel information about a user. The user's travel information may include, but is not limited to, information about transportation, accommodations, and tourist attractions. The collection unit may collect the user's travel information using, for example, a questionnaire survey. The collection unit may also collect travel information from the Internet using web scraping technology. For example, the collection unit may automatically collect information from travel sites and review sites. The collection unit may also analyze the user's social media activities to collect related travel information. For example, the collection unit may collect information about tourist attractions and activities that the user has "liked" or shared on social media. The analysis unit analyzes the information collected by the collection unit. The analysis may be performed using, for example, data mining technology, but is not limited to, an example. For example, the analysis unit may statistically analyze the collected travel information to understand the user's preferences and trends. The analysis unit may also analyze the user's travel information using a machine learning algorithm. For example, the analysis unit may predict future travel destinations based on the user's past travel history. Furthermore, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is relaxed, the analysis unit displays the analysis results in a calm tone. The suggestion unit proposes a travel schedule based on the information analyzed by the analysis unit. The suggestion may be, for example, an automatic suggestion using an algorithm, but is not limited to such an example. For example, the suggestion unit proposes an optimal travel schedule based on the user's budget and desired accommodation conditions. The suggestion unit can also propose a travel schedule based on expert advice. For example, the suggestion unit proposes tourist spots and activities recommended by travel experts. This allows the travel schedule suggestion system according to the embodiment to efficiently collect, analyze, and suggest travel information for the user.

[0056] The collection unit can collect the user's budget and desired accommodation conditions. The collection unit, for example, collects budget information entered by the user. For example, the collection unit collects the budget range set by the user (e.g., under 10,000 yen, over 50,000 yen). The collection unit can also collect the user's desired accommodation conditions. For example, the collection unit collects the user's desired hotel rank (e.g., 5-star hotel, 3-star hotel) and room type (e.g., single room, suite). Furthermore, the collection unit can collect the user's desired accommodation facilities (e.g., pool, gym, spa). This allows the collection unit to collect travel information according to the user's budget and preferences. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's input budget information to a generation AI and cause the generation AI to analyze the budget information.

[0057] The suggestion unit can suggest recommended shops and restaurants along the way. For example, the suggestion unit can suggest recommended shops along the way based on the user's travel route. For example, the suggestion unit can suggest popular shops near tourist spots the user visits. The suggestion unit can also suggest recommended restaurants based on the user's preferences. For example, the suggestion unit can suggest restaurants based on the user's preferred cuisine (e.g., Japanese cuisine, Italian cuisine) or price range (e.g., fine dining, casual dining). The suggestion unit can also suggest recommended shops and restaurants based on the user's review ratings. For example, the suggestion unit can prioritize suggesting shops and restaurants that the user has given high ratings. This allows the suggestion unit to make suggestions to increase the enjoyment of the trip. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input information about the user's travel route into a generation AI and cause the generation AI to suggest recommended shops and restaurants along the way.

[0058] The suggestion unit can check the availability of ticket reservations and luggage storage. For example, the suggestion unit can check the reservation status of tickets desired by the user. For example, the suggestion unit can check ticket availability through an online reservation system. The suggestion unit can also check the availability of luggage storage services desired by the user. For example, the suggestion unit can check the availability of luggage storage services at station lockers or hotel front desks. Furthermore, the suggestion unit can suggest the optimal timing for ticket reservations and luggage storage based on the user's travel schedule. For example, the suggestion unit can suggest the timing for reserving tickets and storing luggage before the user arrives. This allows the suggestion unit to make suggestions that improve the convenience of travel. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's travel schedule information into the generation AI and cause the generation AI to check the availability of ticket reservations and luggage storage.

[0059] The suggestion unit can provide information for families. For example, if a user is planning a family trip, the suggestion unit can suggest activities for children. For example, the suggestion unit can provide information on theme parks and zoos that children can enjoy. The suggestion unit can also suggest accommodations for families. For example, the suggestion unit can suggest hotels with room types and facilities for families (e.g., family rooms, kids' clubs). The suggestion unit can also provide family discount and benefit information. For example, the suggestion unit can suggest discount plans and benefits that families can use. This allows the suggestion unit to make suggestions that improve the convenience of family trips. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the user's family trip information into the generation AI and cause the generation AI to provide information for families.

[0060] The collection unit can estimate the user's emotions and adjust the timing of collecting travel information based on the user's emotions. For example, if the user is feeling stressed, the collection unit collects travel information during times when the user is able to relax. For example, the collection unit collects travel information after the user returns home from work or on weekends. Furthermore, if the user is excited, the collection unit can quickly collect travel information and quickly make suggestions. For example, the collection unit collects information while the user is planning a trip. Furthermore, if the user is tired, the collection unit can collect travel information after the user has rested. For example, the collection unit collects information after the user has had a sufficient rest. This allows the collection unit to collect travel information at the optimal timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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 may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the collection timing based on the emotion.

[0061] The collection unit can analyze the user's past travel history and select a collection method. For example, the collection unit can suggest similar travel destinations based on places the user has visited in the past. For example, the collection unit can analyze information on tourist spots and accommodations the user has visited in the past and collect similar travel destinations. The collection unit can also identify preferred types of accommodations from the user's past travel history and narrow down the information to be collected. For example, the collection unit can collect information on similar accommodations based on the rank and room type of hotels the user has used in the past. Furthermore, the collection unit can collect optimal access methods based on the transportation methods the user has used in the past. For example, the collection unit can collect information on similar transportation methods based on information on airplanes and trains the user has used in the past. This allows the collection unit to collect optimal travel information based on the past travel history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past travel history data into a generation AI and have the generation AI select an optimal collection method.

[0062] When collecting travel information, the collection unit can filter the travel information based on the user's current living situation and areas of interest. For example, if the user is busy with their current living situation, the collection unit collects travel information that can be enjoyed in a short period of time. For example, the collection unit collects information on weekend trips and day trips that the user can enjoy in between work. Furthermore, if the user has a specific area of ​​interest (e.g., history or nature), the collection unit can prioritize collecting travel information related to that area. For example, the collection unit collects information on historical tourist spots and natural parks. Furthermore, if the user is interested in health, the collection unit can collect travel information that offers healthy activities and meals. For example, the collection unit collects information on yoga retreats and organic restaurants. This allows the collection unit to collect travel information tailored to the user's current situation and interests. 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 data on the user's living situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0063] The collection unit can estimate the user's emotions and prioritize the travel information to be collected based on the user's emotions. For example, if the user is relaxed, the collection unit prioritizes collecting information on relaxing activities and accommodations. For example, the collection unit collects information on spas and hot springs. Furthermore, if the user is excited, the collection unit can prioritize collecting information on active activities and events. For example, the collection unit collects information on adventure sports and festivals. Furthermore, if the user is tired, the collection unit can prioritize collecting information on accommodations and spas suitable for rest. For example, the collection unit collects information on resort hotels and relaxation facilities. This allows the collection unit to collect travel information in priority order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotional data into the generation AI and have the generation AI determine priorities based on emotions.

[0064] When collecting travel information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, the collection unit prioritizes collecting information about tourist attractions and activities close to the user's current location. For example, the collection unit collects information about tourist attractions that the user can visit within walking distance of the user's current location. Furthermore, if the user is staying in a specific area, the collection unit can prioritize collecting information about events and local specialties in that area. For example, the collection unit collects information about festivals held in the area where the user is staying and information about shops introducing local specialties. Furthermore, if the user is traveling, the collection unit can prioritize collecting information about tourist attractions and restaurants along the user's travel route. For example, the collection unit collects information about tourist attractions and restaurants that the user can stop at while traveling by car. This allows the collection unit to collect highly relevant travel information based on the user's current location. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0065] When collecting travel information, the collection unit can collect related information based on the user's social media activities. For example, the collection unit collects information on tourist attractions and activities that the user has "liked" or shared on social media. For example, the collection unit prioritizes collecting information on tourist attractions and activities that the user has "liked." The collection unit can also collect information on travel destinations and accommodations introduced by influencers the user follows. For example, the collection unit collects information on tourist attractions and accommodations recommended by influencers the user follows. Furthermore, the collection unit can collect information on travel destinations and activities that the user is interested in from photos and comments posted on social media. For example, the collection unit analyzes photos and comments posted by the user to collect related travel information. This allows the collection unit to collect highly relevant travel information based on the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0066] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the user's emotions. For example, when the user is relaxed, the analysis unit displays the analysis results in a calm tone. For example, when the user is relaxed, the analysis unit displays the analysis results in soft colors and gentle language. Furthermore, when the user is excited, the analysis unit can also display the analysis results with visually stimulating effects. For example, when the user is excited, the analysis unit displays the analysis results using vivid colors and dynamic effects. Furthermore, when the user is tired, the analysis unit can display the analysis results in a simple, highly visible format. For example, when the user is tired, the analysis unit displays the analysis results in a concise, easy-to-read format. This allows the analysis unit to provide the analysis results in an optimal presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the expression method based on the emotion.

[0067] During analysis, the analysis unit can adjust the level of detail of the analysis according to the importance of the travel information. The analysis unit, for example, performs a detailed analysis of important travel information (e.g., ticket reservations, accommodations). For example, the analysis unit may analyze ticket reservation information in detail and suggest the optimal reservation method and timing. The analysis unit may also analyze accommodation information in detail and suggest accommodations that meet the user's preferences. Furthermore, the analysis unit can perform a simplified analysis of less important information (e.g., shops and restaurants along the way). For example, the analysis unit may suggest a concise summary of information about shops and restaurants along the way. This allows the analysis unit to perform a detailed analysis according to the importance of the travel information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input travel information importance data to the generation AI and cause the generation AI to adjust the level of detail based on the importance.

[0068] During analysis, the analysis unit can apply different analysis algorithms based on the category of travel information. For example, for accommodation information, the analysis unit applies an analysis algorithm based on the user's past accommodation history. For example, the analysis unit may suggest similar accommodations based on information about hotels where the user has stayed in the past. Furthermore, for restaurant information, the analysis unit can apply an analysis algorithm that takes into account the user's food preferences. For example, the analysis unit may suggest restaurants based on the type of cuisine and price range the user prefers. Furthermore, for activity information, the analysis unit can apply an analysis algorithm that reflects the user's interests. For example, the analysis unit may suggest information about activities and events that the user is interested in. This allows the analysis unit to perform optimal analysis according to the category of travel information. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input category data of the travel information into the generation AI and cause the generation AI to apply an analysis algorithm based on the category.

[0069] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the user's emotions. For example, when the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, when the user is in a hurry, the analysis unit displays the analysis result in a concise and concise format. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, when the user is relaxed, the analysis unit displays a detailed and comprehensive analysis result. Furthermore, when the user is excited, the analysis unit can provide the analysis result with visually stimulating effects. For example, when the user is excited, the analysis unit displays the analysis result using dynamic effects and vivid colors. This allows the analysis unit to provide the analysis result at an optimal length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the analysis based on the emotion.

[0070] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the travel information. For example, the analysis unit prioritizes analysis of the most recently submitted travel information. For example, the analysis unit prioritizes analysis of the travel information most recently submitted by the user. The analysis unit can also determine the analysis priority based on a deadline specified by the user. For example, the analysis unit determines the analysis priority based on the travel departure date or reservation deadline specified by the user. Furthermore, the analysis unit can prioritize analysis of information with an approaching travel departure date. For example, the analysis unit prioritizes analysis of information with an approaching travel departure date and quickly makes suggestions. This allows the analysis unit to perform analysis with a priority based on the time of submission. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the time of submission of travel information to the generation AI and cause the generation AI to determine the priority based on the time of submission.

[0071] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the travel information. For example, the analysis unit prioritizes analysis of information that the user is highly interested in. For example, the analysis unit prioritizes analysis of information about tourist spots and activities that the user is particularly interested in. The analysis unit can also prioritize analysis of information related to travel destinations. For example, the analysis unit prioritizes analysis of information about tourist spots and accommodations that the user plans to visit. Furthermore, the analysis unit can prioritize analysis of highly relevant information based on the user's past travel history. For example, the analysis unit prioritizes analysis of information related to places the user has visited or services the user has used. This allows the analysis unit to perform analysis in an order based on relevance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of travel information to the generation AI and cause the generation AI to perform the analysis order based on relevance.

[0072] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the user's emotions. For example, when the user is relaxed, the suggestion unit makes suggestions in a calm tone. For example, when the user is relaxed, the suggestion unit makes suggestions using soft colors and gentle language. Furthermore, when the user is excited, the suggestion unit can add visually stimulating effects to the suggestion. For example, when the user is excited, the suggestion unit makes suggestions using vivid colors and dynamic effects. Furthermore, when the user is tired, the suggestion unit can make suggestions in a simple, highly visible format. For example, when the user is tired, the suggestion unit makes suggestions in a concise, easy-to-read format. This allows the suggestion unit to make suggestions in an optimal way according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned 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 emotional data into the generation AI and cause the generation AI to adjust the expression method based on the emotion.

[0073] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the travel information. The suggestion unit, for example, makes detailed suggestions for important travel information (e.g., ticket reservations, accommodations). For example, the suggestion unit may suggest detailed information about ticket reservations and provide the optimal reservation method and timing. The suggestion unit may also suggest detailed information about accommodations and provide accommodations that meet the user's preferences. Furthermore, the suggestion unit can make simplified suggestions for less important information (e.g., shops and restaurants along the way). For example, the suggestion unit may suggest information about shops and restaurants along the way in a concise summary. This allows the suggestion unit to make detailed suggestions based on the importance of the travel information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input importance data of the travel information to the generation AI and cause the generation AI to adjust the level of detail based on the importance.

[0074] When making a suggestion, the suggestion unit can apply different suggestion algorithms based on the category of the travel information. For example, for accommodation information, the suggestion unit applies a suggestion algorithm based on the user's past accommodation history. For example, the suggestion unit can suggest similar accommodations based on information about hotels where the user has stayed in the past. The suggestion unit can also apply a suggestion algorithm that takes the user's food preferences into account for restaurant information. For example, the suggestion unit can suggest restaurants based on the type of cuisine and price range the user prefers. Furthermore, the suggestion unit can apply a suggestion algorithm that reflects the user's interests and concerns for activity information. For example, the suggestion unit can suggest information about activities and events that the user is interested in. This allows the suggestion unit to make optimal suggestions according to the category of the travel information. 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 category data of the travel information into the generation AI and cause the generation AI to apply a suggestion algorithm based on the category.

[0075] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the user's emotions. For example, when the user is in a hurry, the suggestion unit can make short and to-the-point suggestions. For example, when the user is in a hurry, the suggestion unit can make suggestions in a concise and to-the-point format. The suggestion unit can also make detailed suggestions when the user is relaxed. For example, when the user is relaxed, the suggestion unit can make detailed and comprehensive suggestions. Furthermore, when the user is excited, the suggestion unit can make suggestions with visually stimulating effects. For example, when the user is excited, the suggestion unit can make suggestions using dynamic effects and vivid colors. This allows the suggestion unit to make suggestions with an optimal length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without 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 suggestion based on the emotion.

[0076] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the travel information. For example, the suggestion unit prioritizes the most recently submitted travel information. For example, the suggestion unit prioritizes the most recently submitted travel information. The suggestion unit can also determine the priority of the proposal based on a deadline specified by the user. For example, the suggestion unit prioritizes the proposal based on the travel departure date or reservation deadline specified by the user. Furthermore, the suggestion unit can prioritize the proposal of information that is close to the travel departure date. For example, the suggestion unit prioritizes the proposal of information that is close to the user's travel departure date and makes the proposal quickly. This allows the suggestion unit to prioritize the proposal based on the time of submission. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input data on the time of submission of the travel information to the generation AI and cause the generation AI to determine the priority based on the time of submission.

[0077] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the travel information. For example, the suggestion unit prioritizes suggesting information that is of high interest to the user. For example, the suggestion unit prioritizes suggesting information about tourist spots and activities that the user is particularly interested in. The suggestion unit can also prioritize suggesting information related to travel destinations. For example, the suggestion unit prioritizes suggesting information about tourist spots and accommodations that the user plans to visit. Furthermore, the suggestion unit can prioritize suggesting highly relevant information based on the user's past travel history. For example, the suggestion unit prioritizes suggesting information related to places the user has visited or services the user has used. This allows the suggestion unit to make suggestions in an order based on relevance. 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 relevance data of travel information to a generation AI and cause the generation AI to execute the order of suggestions based on relevance. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis 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 collection unit is realized by the control unit 46A of the smart device 14 and collects the user's travel information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal travel schedule based on the analysis results. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the suggestion unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects the user's travel information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal travel schedule based on the analysis results. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects the user's travel information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal travel schedule based on the analysis results. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the suggestion unit may be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis 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 collection unit is realized by the control unit 46A of the robot 414 and collects the user's travel information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal travel schedule based on the analysis results. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the suggestion unit may be realized, for example, by the control unit 46A of the robot 414.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The analysis unit can take the user's health condition into consideration when analyzing the user's travel information. For example, if the user provides health checkup results or fitness tracker data, the analysis unit can suggest health management options at the travel destination based on that information. Specifically, if the user has high blood pressure, the analysis unit can suggest restaurants that serve low-salt meals or relaxing spa facilities. Also, if the user has allergies, the analysis unit can suggest restaurants that serve allergen-free meals or allergy-friendly accommodations. Furthermore, if the user is interested in fitness, the analysis unit can provide information on running courses and gyms at the travel destination. This allows the analysis unit to suggest an optimal travel schedule based on the user's health condition.

[0080] The suggestion unit can take the user's hobbies and interests into consideration when proposing a travel schedule for the user. For example, if the user is interested in art, information on art museums and galleries at the travel destination can be provided. If the user likes outdoor activities, information on hiking trails and campsites can be proposed. Furthermore, if the user enjoys music, information on concerts and live events held at the travel destination can be provided. In this way, the suggestion unit can propose a travel schedule that suits the user's hobbies and interests.

[0081] The collection unit can take into account the user's past reviews and ratings when collecting the user's travel information. For example, it can prioritize collection of information on accommodations and restaurants that the user has previously rated highly. It can also collect information to help users avoid places that the user has previously rated poorly. Furthermore, it can analyze reviews and comments posted by the user in the past to understand the user's preferences and tendencies. This allows the collection unit to collect optimal travel information based on the user's past ratings.

[0082] The suggestion unit can take the user's travel purpose into consideration when proposing a travel schedule for the user. For example, if the user is planning a trip for relaxation, information on relaxing spas and hot springs can be provided. If the user is planning an adventure-seeking trip, information on active activities and sports can be proposed. Furthermore, if the user is planning a cultural experience trip, information on events and facilities where users can experience local culture and traditions can be provided. This allows the suggestion unit to propose an optimal travel schedule according to the user's travel purpose.

[0083] The collection unit can take the user's current mood and emotions into consideration when collecting the user's travel information. For example, if the user is feeling stressed, it can prioritize collecting information about relaxing travel destinations and activities. Also, if the user is excited, it can prioritize collecting information about active activities and events. Furthermore, if the user is tired, it can prioritize collecting information about accommodations and spas suitable for rest. This allows the collection unit to collect optimal travel information according to the user's emotions.

[0084] When proposing a travel itinerary to the user, the suggestion unit can estimate the user's emotions and adjust the content of the suggestion based on the emotions. For example, if the user is relaxed, the suggestion unit can make the suggestion in a calm tone. If the user is excited, the suggestion unit can make the suggestion with a visually stimulating effect. Furthermore, if the user is tired, the suggestion unit can make the suggestion in a simple, highly visible format. This allows the suggestion unit to make optimal suggestions according to the user's emotions.

[0085] When analyzing the user's travel information, the analysis unit can estimate the user's emotions and determine the analysis priority based on the emotions. For example, if the user is relaxed, the analysis unit can prioritize information on relaxing activities and accommodations. If the user is excited, the analysis unit can prioritize information on active activities and events. Furthermore, if the user is tired, the analysis unit can prioritize information on accommodations and spas suitable for rest. This allows the analysis unit to perform optimal analysis according to the user's emotions.

[0086] When collecting the user's travel information, the collection unit can estimate the user's emotions and determine the priority of the information to be collected based on the emotions. For example, if the user is relaxed, information on relaxing activities and accommodations can be collected with priority. Also, if the user is excited, information on active activities and events can be collected with priority. Furthermore, if the user is tired, information on accommodations and spas suitable for rest can be collected with priority. This allows the collection unit to collect optimal travel information according to the user's emotions.

[0087] When proposing a travel itinerary for the user, the suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the emotions. For example, if the user is relaxed, information on relaxing activities and accommodations can be preferentially suggested. Also, if the user is excited, information on active activities and events can be preferentially suggested. Furthermore, if the user is tired, information on accommodations and spas suitable for rest can be preferentially suggested. In this way, the suggestion unit can make optimal suggestions according to the user's emotions.

[0088] The analysis unit can take the user's travel purpose into consideration when analyzing the user's travel information. For example, if the user is planning a trip for relaxation, the analysis unit can prioritize information on relaxing activities and accommodations. If the user is planning an adventure-seeking trip, the analysis unit can prioritize information on active activities and sports. Furthermore, if the user is planning a cultural experience trip, the analysis unit can prioritize information on events and facilities where users can experience local culture and traditions. This allows the analysis unit to perform optimal analysis according to the user's travel purpose.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The collection unit collects the user's travel information. The user's travel information includes information on transportation, accommodation, tourist attractions, etc. The collection unit collects the user's travel information using a questionnaire survey. Travel information on the Internet can also be collected using web scraping technology. For example, information can be automatically collected from travel sites and review sites. Furthermore, the collection unit can analyze the user's social media activity and collect related travel information. For example, information on tourist attractions and activities that the user has "liked" or shared on social media can be collected. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using data mining technology. For example, the collected travel information is statistically analyzed to understand the user's preferences and trends. The analysis unit can also analyze the user's travel information using machine learning algorithms. For example, it can predict future travel destinations based on the user's past travel history. Furthermore, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is relaxed, the analysis results can be displayed in a calm tone. Step 3: The suggestion unit proposes a travel itinerary based on the information analyzed by the analysis unit. Suggestions are made automatically by an algorithm. For example, it proposes an optimal travel itinerary based on the user's budget and desired accommodation conditions. It can also propose travel itineraries based on expert advice. For example, it proposes tourist spots and activities recommended by travel experts.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] 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.

[0162] [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user travel information; an analysis unit that analyzes the information collected by the collection unit; a suggestion unit that proposes a travel schedule based on the information analyzed by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect the user's budget and desired accommodation requirements 2. The system of claim 1.

3. The proposal unit Suggest recommended shops and restaurants along the way 2. The system of claim 1.

4. The proposal unit Check ticket reservations and luggage storage availability 2. The system of claim 1.

5. The proposal unit Providing information for families 2. The system of claim 1.

6. The collecting unit Estimate user emotions and adjust travel information collection timing based on user emotions 2. The system of claim 1.

7. The collecting unit Analyze the user's past travel history and select the collection method 2. The system of claim 1.

8. The collecting unit When collecting travel information, filter it based on the user's current living situation and areas of interest.

2. The system of claim 1.

9. The collecting unit Estimate user emotions and prioritize travel information collection based on user emotions 2. The system of claim 1.

10. The collecting unit When collecting travel information, prioritize collection of relevant information based on the user's geographic location.

2. The system of claim 1.

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