System

Abstract

An object of a system according to an embodiment is to efficiently select a sightseeing spot at a travel destination and provide detailed information.SOLUTION: A system includes a ranking generation unit, an address providing unit, and a topic spot collection unit. The ranking generation part uses the generation AI to create ranking of the sightseeing spots. The address providing unit provides a local address or detailed information based on the ranking generated by the ranking generating unit. The hot spot collection unit collects a hot spot online.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to efficiently select tourist spots at travel destinations and provide detailed information, so there is room for improvement.

[0005] The system according to the embodiment aims to efficiently select tourist spots at travel destinations and provide detailed information. [Means for solving the problem]

[0006] The system according to the embodiment includes a ranking generation unit, an address providing unit, and a popular spot collection unit. The ranking generation unit creates a ranking of tourist spots using a generation AI. The address providing unit provides local addresses and detailed information based on the rankings generated by the ranking generation unit. The popular spot collection unit collects spots that are trending online. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently select tourist spots at travel destinations and provide detailed information. [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 support system according to an embodiment of the present invention is a system that uses a generation AI to automatically create rankings of tourist spots and provides local addresses and detailed information along with the rankings in order to reduce the time it takes to decide on a travel destination. This enables travelers to efficiently select a travel destination.

[0029] A travel support system according to an embodiment includes a ranking generation unit, an address provision unit, and a popular spot collection unit. The ranking generation unit uses a generation AI to create rankings of tourist spots. For example, the generation AI collects data from the Internet and analyzes information about travel destinations. The generation AI collects data from travel blogs, review sites, social media, and other sources to analyze which spots are popular. The generation AI receives inputs from travelers, including prompts containing instructions on what the travelers want the generation AI to do. The generation AI generates rankings of tourist spots based on the prompts. For example, by inputting a prompt such as "Ranking the popular tourist spots in Tokyo," the generation AI ranks Tokyo's tourist spots. The address provision unit provides local addresses and detailed information based on the rankings generated by the ranking generation unit. For example, if "Tokyo Tower" ranks highly, detailed information such as the address, nearest station, and business hours is also provided. The popular spot collection unit collects popular spots online. For example, it analyzes spots that many people post about on social media and spots that have received high ratings on travel blogs, and creates a list of them. This allows the travel support system to help travelers efficiently select travel destinations.

[0030] The ranking generation unit can generate rankings of tourist spots based on prompts containing instructions on what the traveler wants the generation AI to do. For example, the ranking generation unit generates rankings of tourist spots based on the traveler's prompts. For example, the generation AI receives the prompt "Rank the popular tourist spots in Tokyo" and ranks Tokyo's tourist spots. The generation AI can also analyze the traveler's preferences and past travel history to generate individually customized rankings. For example, for a traveler who has visited places with a preference for natural scenery in the past, the generation AI can generate a ranking that includes many spots with beautiful natural scenery. This allows rankings to be generated based on the traveler's instructions.

[0031] The address providing unit can provide detailed information such as the address, nearest station, and business hours of spots that are ranked highly. For example, if "Tokyo Tower" is ranked highly, detailed information such as its address, nearest station, and business hours will also be provided. The address providing unit can also update detailed information about spots in real time. For example, if business hours change or a new access method is added, the information will be reflected immediately. This allows travelers to check the specific location and how to access it.

[0032] The popular spot collection unit can analyze spots that many people have posted about on social media and spots that have received high ratings on travel blogs, and create a list of them. For example, the popular spot collection unit analyzes spots that many people have posted about on social media and creates a list of them. For example, it identifies popular spots on Instagram and generates a list of them. The popular spot collection unit also analyzes spots that have received high ratings on travel blogs and creates a list of them. For example, it identifies spots that have received high ratings on travel blogs and generates a list of them. Furthermore, the popular spot collection unit can update popular spots online in real time. For example, it can instantly add newly trending spots on social media to the list. This makes it easier for travelers to keep up with the latest trends.

[0033] The ranking generation unit can analyze the seasonal popularity of each spot and generate seasonal rankings. For example, the ranking generation unit uses a generation AI to collect seasonal data from travel blogs and social media and analyze the seasonal popularity of each tourist spot. For example, a spring ranking is generated taking into account that spots where cherry blossoms are in full bloom are popular in spring. The ranking generation unit also collects seasonal event information and generates tourist spot rankings based on that information. For example, spots where summer festivals are held are ranked in the summer rankings. Furthermore, the ranking generation unit analyzes seasonal weather data and generates rankings according to the seasons that are most popular for tourists. For example, spots with beautiful snow scenery in winter are ranked in the winter rankings. This allows the popular spots for each season to be identified.

[0034] The ranking generation unit can add attribute information such as the traveler's age group and gender and generate different rankings for each attribute. For example, the ranking generation unit uses a generation AI to analyze data based on the traveler's age group and gender and generate different rankings for each attribute. For example, spots popular with young people and spots popular with the elderly are ranked separately. The ranking generation unit also generates rankings for families and couples based on the traveler's attribute information. For example, spots with many attractions for children are ranked for families. Furthermore, the ranking generation unit generates rankings for women and men based on gender. For example, cafes and shopping spots popular with women are included in the rankings. This makes it possible to provide rankings according to the traveler's attributes.

[0035] The ranking generation unit can generate rankings for avoiding crowds by taking into account the level of congestion and waiting times at spots. For example, the ranking generation unit uses a generation AI to collect real-time congestion data and prioritize ranking spots with low levels of congestion. For example, spots that are less crowded when visited on weekday mornings are included in the ranking. The ranking generation unit also analyzes waiting time data and reflects spots with short waiting times in the ranking. For example, even for popular attractions, the ranking is generated taking into account time periods with short waiting times. Furthermore, when generating rankings for avoiding crowds, the ranking generation unit prioritizes ranking off-season spots and little-known spots. For example, times and locations with fewer tourists are included in the ranking. This makes it possible to provide rankings for avoiding crowds.

[0036] The ranking generation unit can generate eco-friendly rankings by adding information about the environmental impact and sustainability of spots. For example, the ranking generation unit uses a generation AI to collect environmental impact data for each spot and prioritize ranking environmentally friendly spots. For example, it includes facilities that use renewable energy and spots that offer eco-tours in the rankings. The ranking generation unit also analyzes information about sustainability and reflects eco-friendly tourist spots in the rankings. For example, it ranks spots that engage in environmental conservation activities and nature reserves. Furthermore, when generating rankings that take environmental impact into consideration, the ranking generation unit prioritizes ranking spots that are easily accessible by public transportation. For example, it includes spots that are easily accessible by train or bus in the rankings. This makes it possible to provide eco-friendly rankings.

[0037] The address providing unit adds detailed information such as surrounding transportation information and whether parking is available to the local address, improving convenience for travelers. For example, the generation AI provides surrounding transportation information in addition to the local address of each spot. For example, it displays information on the nearest station and bus stop, as well as transportation options. The address providing unit also adds detailed information such as whether parking is available, parking fees, and parking location to the local address. For example, it provides information on parking availability and how to reserve. Furthermore, the address providing unit updates surrounding transportation information in real time, allowing travelers to select the optimal route. For example, it reflects traffic congestion information and operation status. This improves convenience for travelers.

[0038] The address provider can add information about nearby restaurants and accommodations to local addresses to support travelers' stays. For example, the generation AI can provide information about nearby restaurants in addition to the local addresses of each spot. For example, it can display restaurant menus, business hours, and customer reviews. The address provider can also add accommodation information to local addresses to make it easier for travelers to find their accommodations. For example, it can provide hotel rates, availability, and reservation methods. Furthermore, the address provider can update information about nearby restaurants and accommodations in real time to ensure travelers have the latest information. For example, it can reflect newly opened stores and special promotional information. This can support travelers' stays.

[0039] The address provider adds barrier-free information about spots to local addresses, making it possible to accommodate travelers with disabilities. For example, the generation AI provides barrier-free information in addition to the local address of each spot. For example, it displays whether there are wheelchair-accessible restrooms and elevators, and barrier-free routes. The address provider also adds information about facilities and services that are easy for travelers with disabilities to use to the local address. For example, it displays whether braille guides or audio guides are available, and the availability of assistance services. Furthermore, the address provider updates the barrier-free information in real time, allowing travelers with disabilities to access the latest information. For example, it reflects information on the renovation status of facilities and new barrier-free facilities. This makes it possible to accommodate travelers with disabilities.

[0040] When compiling popular online spots, the Hot Spot Collection Unit can analyze fluctuations in the popularity of spots over time and show changes in trends. For example, the Hot Spot Collection Unit uses a generation AI to collect data from social media and travel blogs and analyze changes in the popularity of spots over time. For example, it can show when a particular spot suddenly became popular and why. The Hot Spot Collection Unit also visually displays the changes in popularity in graphs and charts, allowing travelers to understand the changes in trends at a glance. For example, it can show the peak period of popularity and subsequent changes. Furthermore, the Hot Spot Collection Unit can predict changes in popularity based on time series data and identify spots that are likely to become popular in the future. For example, it can predict future trends based on past data. This makes it possible to understand changes in trends.

[0041] When compiling popular online spots, the Hot Spot Collection Unit can analyze the popularity of spots by region and show regional trends. For example, the Hot Spot Collection Unit uses a generation AI to collect data for each region and identify hot spots in each region. For example, it can analyze hot spots in Tokyo and Osaka separately and show regional trends. The Hot Spot Collection Unit also visually displays the popularity of each region in graphs and charts, allowing travelers to understand regional trends at a glance. For example, it can show peak periods of popularity for each region and subsequent fluctuations. Furthermore, the Hot Spot Collection Unit predicts fluctuations in popularity based on regional data and identifies spots that are likely to become popular in the future. For example, it can predict future regional trends based on past data. This makes it possible to understand regional trends.

[0042] When compiling online hot spots, the trending spot collection unit can analyze the popularity of spots for each different social media platform and show trends by platform. For example, the generation AI in the trending spot collection unit collects data from different social media platforms and identifies hot spots on each platform. For example, it analyzes hot spots on Twitter and Instagram separately and shows trends by platform. The trending spot collection unit also visually displays the popularity of each platform in graphs and charts, allowing travelers to understand trends by platform at a glance. For example, it shows peak periods of popularity for each platform and subsequent fluctuations. Furthermore, the trending spot collection unit predicts fluctuations in popularity based on data for each platform and identifies spots that are likely to become popular in the future. For example, it predicts future trends by platform based on past data. This allows travelers to understand trends by platform.

[0043] When compiling online hot spots, the Hot Spot Collection Unit analyzes the popularity of spots in different languages ​​to show international trends. For example, the Hot Spot Collection Unit uses a generation AI to collect data written in different languages ​​and identify hot spots in each language. For example, it analyzes hot spots separately in English and Japanese to show international trends. The Hot Spot Collection Unit also visually displays the popularity of each language in graphs and charts, allowing travelers to understand the trends in each language at a glance. For example, it shows the peak time of popularity for each language and subsequent fluctuations. Furthermore, the Hot Spot Collection Unit predicts fluctuations in popularity based on data for each language and identifies spots that are likely to become popular in the future. For example, it predicts future trends by language based on past data. This allows for understanding international trends.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The travel assistance system can further include a health monitoring unit that monitors the user's health condition and proposes a travel plan based on the user's health condition. For example, the health monitoring unit can measure the user's heart rate and blood pressure and propose relaxing spots and activities. The health monitoring unit can also analyze the user's number of steps and amount of exercise and propose a travel plan that includes moderate exercise. Furthermore, the health monitoring unit can consider the user's dietary restrictions and allergy information and propose appropriate restaurants and menus. This allows the system to propose a travel plan based on the user's health condition.

[0046] The travel support system can also include a history analysis unit that analyzes the user's past travel history and proposes new travel plans based on previously visited spots and experiences. For example, it can analyze ratings and impressions of previously visited spots and propose similar spots. The history analysis unit can also customize new travel plans based on activities and events that were well-received on past trips. Furthermore, the history analysis unit can analyze the user's past travel patterns and propose optimal travel times and routes. This makes it possible to provide travel plans based on the user's past travel history.

[0047] The travel assistance system may further include a hobby analysis unit that proposes travel plans based on the user's interests and hobbies. For example, if the user is interested in history, historical landmarks and museums may be proposed. If the user likes outdoor activities, the hobby analysis unit may also propose hiking and camping spots. Furthermore, the hobby analysis unit may propose events and workshops related to the user's hobbies. This allows the system to provide travel plans based on the user's interests and hobbies.

[0048] The travel assistance system may further include a budget management unit that proposes travel plans based on the user's budget. For example, it may propose accommodations and restaurants according to the user's budget. The budget management unit may also propose activities and events that can be enjoyed within the user's budget. Furthermore, the budget management unit may monitor the user's spending in real time and provide alerts to prevent the user from going over budget. This allows the system to provide travel plans based on the user's budget.

[0049] The travel assistance system can further include a safety management unit to ensure the user's safety. For example, it can provide information on the security situation at the travel destination and emergency contact information. The safety management unit can also track the user's current location in real time and issue an alert if the user approaches a dangerous area. Furthermore, the safety management unit can provide information for responding to emergencies such as natural disasters and traffic accidents. This can provide support to ensure the user's safety.

[0050] The travel assistance system may further include a language support unit that suggests travel plans based on the user's language. For example, the system may suggest guided tours in the user's native language or activities to learn the local language. The language support unit may also provide phrasebooks and translation tools to help the user communicate in the local language. The language support unit may also provide information about local culture and customs based on the user's language. This allows the system to provide travel plans based on the user's language.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The ranking generation unit uses the generation AI to create a ranking of tourist spots. The generation AI collects data from the internet and analyzes information about travel destinations. Specifically, it collects data from travel blogs, review sites, social media, etc., and analyzes which spots are popular. When a traveler inputs a prompt to the generation AI, such as "Ranking the popular tourist spots in Tokyo," the generation AI will rank Tokyo's tourist spots. Step 2: The address providing unit provides local addresses and detailed information based on the rankings generated by the ranking generating unit. For example, if "Tokyo Tower" is ranked high, detailed information such as the address, nearest station, and opening hours will also be provided. Step 3: The Trending Spot Collection Unit collects hot topics online, such as those that many people have posted about on social media or those that have received high ratings on travel blogs, and creates a list of them.

[0053] (Example 2) A travel support system according to an embodiment of the present invention is a system that uses a generation AI to automatically create rankings of tourist spots and provides local addresses and detailed information along with the rankings in order to reduce the time it takes to decide on a travel destination. This enables travelers to efficiently select a travel destination.

[0054] A travel support system according to an embodiment includes a ranking generation unit, an address provision unit, and a popular spot collection unit. The ranking generation unit uses a generation AI to create rankings of tourist spots. For example, the generation AI collects data from the Internet and analyzes information about travel destinations. The generation AI collects data from travel blogs, review sites, social media, and other sources to analyze which spots are popular. The generation AI receives inputs from travelers, including prompts containing instructions on what the travelers want the generation AI to do. The generation AI generates rankings of tourist spots based on the prompts. For example, by inputting a prompt such as "Ranking the popular tourist spots in Tokyo," the generation AI ranks Tokyo's tourist spots. The address provision unit provides local addresses and detailed information based on the rankings generated by the ranking generation unit. For example, if "Tokyo Tower" ranks highly, detailed information such as the address, nearest station, and business hours is also provided. The popular spot collection unit collects popular spots online. For example, it analyzes spots that many people post about on social media and spots that have received high ratings on travel blogs, and creates a list of them. This allows the travel support system to help travelers efficiently select travel destinations.

[0055] The ranking generation unit can generate rankings of tourist spots based on prompts containing instructions on what the traveler wants the generation AI to do. For example, the ranking generation unit generates rankings of tourist spots based on the traveler's prompts. For example, the generation AI receives the prompt "Rank the popular tourist spots in Tokyo" and ranks Tokyo's tourist spots. The generation AI can also analyze the traveler's preferences and past travel history to generate individually customized rankings. For example, for a traveler who has visited places with a preference for natural scenery in the past, the generation AI can generate a ranking that includes many spots with beautiful natural scenery. This allows rankings to be generated based on the traveler's instructions.

[0056] The address providing unit can provide detailed information such as the address, nearest station, and business hours of spots that are ranked highly. For example, if "Tokyo Tower" is ranked highly, detailed information such as its address, nearest station, and business hours will also be provided. The address providing unit can also update detailed information about spots in real time. For example, if business hours change or a new access method is added, the information will be reflected immediately. This allows travelers to check the specific location and how to access it.

[0057] The popular spot collection unit can analyze spots that many people have posted about on social media and spots that have received high ratings on travel blogs, and create a list of them. For example, the popular spot collection unit analyzes spots that many people have posted about on social media and creates a list of them. For example, it identifies popular spots on Instagram and generates a list of them. The popular spot collection unit also analyzes spots that have received high ratings on travel blogs and creates a list of them. For example, it identifies spots that have received high ratings on travel blogs and generates a list of them. Furthermore, the popular spot collection unit can update popular spots online in real time. For example, it can instantly add newly trending spots on social media to the list. This makes it easier for travelers to keep up with the latest trends.

[0058] The ranking generation unit can analyze the seasonal popularity of each spot and generate seasonal rankings. For example, the ranking generation unit uses a generation AI to collect seasonal data from travel blogs and social media and analyze the seasonal popularity of each tourist spot. For example, a spring ranking is generated taking into account that spots where cherry blossoms are in full bloom are popular in spring. The ranking generation unit also collects seasonal event information and generates tourist spot rankings based on that information. For example, spots where summer festivals are held are ranked in the summer rankings. Furthermore, the ranking generation unit analyzes seasonal weather data and generates rankings according to the seasons that are most popular for tourists. For example, spots with beautiful snow scenery in winter are ranked in the winter rankings. This allows the popular spots for each season to be identified.

[0059] The ranking generation unit can add attribute information such as the traveler's age group and gender and generate different rankings for each attribute. For example, the ranking generation unit uses a generation AI to analyze data based on the traveler's age group and gender and generate different rankings for each attribute. For example, spots popular with young people and spots popular with the elderly are ranked separately. The ranking generation unit also generates rankings for families and couples based on the traveler's attribute information. For example, spots with many attractions for children are ranked for families. Furthermore, the ranking generation unit generates rankings for women and men based on gender. For example, cafes and shopping spots popular with women are included in the rankings. This makes it possible to provide rankings according to the traveler's attributes.

[0060] The ranking generation unit uses the emotion estimation function to generate rankings based on travelers' emotions and prioritize spots that evoke positive emotions. For example, the ranking generation unit uses a generation AI to analyze emotional data from travelers' reviews and social media posts, and prioritizes ranking spots that evoke a lot of positive emotions. For example, spots with impressive scenery and fun attractions are placed at the top. The ranking generation unit also uses the emotion estimation function to identify spots that evoke positive emotions when travelers visit, and reflects those spots in the rankings. For example, it ranks spots with relaxing hot springs and beautiful natural landscapes. Furthermore, the ranking generation unit includes spots with many events and activities that evoke positive emotions based on travelers' emotional data. For example, it places spots with fun festivals and activities at the top. This makes it possible to provide rankings based on travelers' emotions.

[0061] The ranking generation unit can generate rankings for avoiding crowds by taking into account the level of congestion and waiting times at spots. For example, the ranking generation unit uses a generation AI to collect real-time congestion data and prioritize ranking spots with low levels of congestion. For example, spots that are less crowded when visited on weekday mornings are included in the ranking. The ranking generation unit also analyzes waiting time data and reflects spots with short waiting times in the ranking. For example, even for popular attractions, the ranking is generated taking into account time periods with short waiting times. Furthermore, when generating rankings for avoiding crowds, the ranking generation unit prioritizes ranking off-season spots and little-known spots. For example, times and locations with fewer tourists are included in the ranking. This makes it possible to provide rankings for avoiding crowds.

[0062] The ranking generation unit can generate eco-friendly rankings by adding information about the environmental impact and sustainability of spots. For example, the ranking generation unit uses a generation AI to collect environmental impact data for each spot and prioritize ranking environmentally friendly spots. For example, it includes facilities that use renewable energy and spots that offer eco-tours in the rankings. The ranking generation unit also analyzes information about sustainability and reflects eco-friendly tourist spots in the rankings. For example, it ranks spots that engage in environmental conservation activities and nature reserves. Furthermore, when generating rankings that take environmental impact into consideration, the ranking generation unit prioritizes ranking spots that are easily accessible by public transportation. For example, it includes spots that are easily accessible by train or bus in the rankings. This makes it possible to provide eco-friendly rankings.

[0063] The ranking generation unit can use the emotion estimation function to monitor travelers' emotions in real time when they visit and update the rankings based on that data. For example, the ranking generation unit uses a generation AI to collect emotional data on travelers' visits in real time and update the rankings based on that data. For example, spots where travelers expressed positive emotions when they visited are placed at the top. The ranking generation unit also uses the emotion estimation function to monitor changes in travelers' emotions and reflect them in the rankings. For example, spots that travelers felt moved or happy when they visited are given priority in the rankings. Furthermore, the ranking generation unit builds a system that dynamically updates the rankings based on real-time emotional data. For example, the rankings are adjusted every time travelers' emotions change. This makes it possible to dynamically update the rankings based on travelers' emotions.

[0064] The address providing unit adds detailed information such as surrounding transportation information and whether parking is available to the local address, improving convenience for travelers. For example, the generation AI provides surrounding transportation information in addition to the local address of each spot. For example, it displays information on the nearest station and bus stop, as well as transportation options. The address providing unit also adds detailed information such as whether parking is available, parking fees, and parking location to the local address. For example, it provides information on parking availability and how to reserve. Furthermore, the address providing unit updates surrounding transportation information in real time, allowing travelers to select the optimal route. For example, it reflects traffic congestion information and operation status. This improves convenience for travelers.

[0065] The address providing unit uses the emotion estimation function to customize detailed local address information based on the emotions expressed by travelers when they visit, thereby providing a positive experience. For example, the address providing unit uses a generation AI to analyze emotional data expressed by travelers when they visit, and customizes detailed local address information based on that data. For example, it prioritizes displaying information that evokes positive emotions. The address providing unit also uses the emotion estimation function to provide customized information based on travelers' emotions. For example, it adds information about relaxing places and fun activities. Furthermore, the address providing unit collects travelers' emotional data in real time and dynamically updates local address information based on that data. For example, it adjusts the information whenever the travelers' emotions change. This allows travelers to have a positive experience.

[0066] The address provider can add information about nearby restaurants and accommodations to local addresses to support travelers' stays. For example, the generation AI can provide information about nearby restaurants in addition to the local addresses of each spot. For example, it can display restaurant menus, business hours, and customer reviews. The address provider can also add accommodation information to local addresses to make it easier for travelers to find their accommodations. For example, it can provide hotel rates, availability, and reservation methods. Furthermore, the address provider can update information about nearby restaurants and accommodations in real time to ensure travelers have the latest information. For example, it can reflect newly opened stores and special promotional information. This can support travelers' stays.

[0067] The address provider adds barrier-free information about spots to local addresses, making it possible to accommodate travelers with disabilities. For example, the generation AI provides barrier-free information in addition to the local address of each spot. For example, it displays whether there are wheelchair-accessible restrooms and elevators, and barrier-free routes. The address provider also adds information about facilities and services that are easy for travelers with disabilities to use to the local address. For example, it displays whether braille guides or audio guides are available, and the availability of assistance services. Furthermore, the address provider updates the barrier-free information in real time, allowing travelers with disabilities to access the latest information. For example, it reflects information on the renovation status of facilities and new barrier-free facilities. This makes it possible to accommodate travelers with disabilities.

[0068] The address providing unit uses the emotion estimation function to update detailed information about local addresses in real time based on the emotions of travelers when they visit, thereby providing optimal information. For example, the address providing unit uses a generation AI to collect emotional data about travelers in real time when they visit, and updates detailed information about local addresses based on that data. For example, it prioritizes displaying information that evokes positive emotions. The address providing unit also uses the emotion estimation function to provide customized information based on travelers' emotions. For example, it adds information about relaxing places and fun activities. Furthermore, the address providing unit collects emotional data about travelers in real time and dynamically updates local address information based on that data. For example, it adjusts the information whenever the travelers' emotions change. This allows it to provide optimal information to travelers in real time.

[0069] When compiling popular online spots, the Hot Spot Collection Unit can analyze fluctuations in the popularity of spots over time and show changes in trends. For example, the Hot Spot Collection Unit uses a generation AI to collect data from social media and travel blogs and analyze changes in the popularity of spots over time. For example, it can show when a particular spot suddenly became popular and why. The Hot Spot Collection Unit also visually displays the changes in popularity in graphs and charts, allowing travelers to understand the changes in trends at a glance. For example, it can show the peak period of popularity and subsequent changes. Furthermore, the Hot Spot Collection Unit can predict changes in popularity based on time series data and identify spots that are likely to become popular in the future. For example, it can predict future trends based on past data. This makes it possible to understand changes in trends.

[0070] When compiling popular online spots, the Hot Spot Collection Unit can analyze the popularity of spots by region and show regional trends. For example, the Hot Spot Collection Unit uses a generation AI to collect data for each region and identify hot spots in each region. For example, it can analyze hot spots in Tokyo and Osaka separately and show regional trends. The Hot Spot Collection Unit also visually displays the popularity of each region in graphs and charts, allowing travelers to understand regional trends at a glance. For example, it can show peak periods of popularity for each region and subsequent fluctuations. Furthermore, the Hot Spot Collection Unit predicts fluctuations in popularity based on regional data and identifies spots that are likely to become popular in the future. For example, it can predict future regional trends based on past data. This makes it possible to understand regional trends.

[0071] The popular spot collection unit can use the emotion estimation function to analyze users' emotions toward popular online spots and prioritize listing spots that elicit positive emotions. For example, the popular spot collection unit analyzes users' emotions based on data collected by the generation AI from social media and travel blogs, and prioritize listing spots that elicit a lot of positive emotions. For example, spots with impressive scenery and fun attractions are placed at the top. The popular spot collection unit also uses the emotion estimation function to analyze users' emotional data and identify spots that elicit positive emotions. For example, it lists spots with relaxing hot springs and beautiful natural landscapes. Furthermore, the popular spot collection unit lists spots with many events and activities that elicit positive emotions based on the user's emotional data. For example, it places spots with fun festivals and activities at the top. This allows spots that elicit positive emotions to be listed preferentially.

[0072] When compiling online hot spots, the trending spot collection unit can analyze the popularity of spots for each different social media platform and show trends by platform. For example, the generation AI in the trending spot collection unit collects data from different social media platforms and identifies hot spots on each platform. For example, it analyzes hot spots on Twitter and Instagram separately and shows trends by platform. The trending spot collection unit also visually displays the popularity of each platform in graphs and charts, allowing travelers to understand trends by platform at a glance. For example, it shows peak periods of popularity for each platform and subsequent fluctuations. Furthermore, the trending spot collection unit predicts fluctuations in popularity based on data for each platform and identifies spots that are likely to become popular in the future. For example, it predicts future trends by platform based on past data. This allows travelers to understand trends by platform.

[0073] When compiling online hot spots, the Hot Spot Collection Unit analyzes the popularity of spots in different languages ​​to show international trends. For example, the Hot Spot Collection Unit uses a generation AI to collect data written in different languages ​​and identify hot spots in each language. For example, it analyzes hot spots separately in English and Japanese to show international trends. The Hot Spot Collection Unit also visually displays the popularity of each language in graphs and charts, allowing travelers to understand the trends in each language at a glance. For example, it shows the peak time of popularity for each language and subsequent fluctuations. Furthermore, the Hot Spot Collection Unit predicts fluctuations in popularity based on data for each language and identifies spots that are likely to become popular in the future. For example, it predicts future trends by language based on past data. This allows for understanding international trends.

[0074] The hot spot collection unit can use the emotion estimation function to monitor users' emotions about hot spots online in real time and update the list based on that data. For example, the hot spot collection unit can monitor users' emotions in real time based on data collected by the generation AI from social media and travel blogs and update the list based on that data. For example, it can prioritize listing spots that contain a lot of positive emotions. The hot spot collection unit also uses the emotion estimation function to analyze users' emotion data in real time and dynamically update the list. For example, it can adjust the list every time the user's emotion changes. Furthermore, the hot spot collection unit can build a system that updates the hot spot list based on real-time emotion data. For example, it can adjust the list every time the traveler's emotion changes. This allows the list to be dynamically updated based on the user's emotion.

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

[0076] The travel assistance system can further include a health monitoring unit that monitors the user's health condition and proposes a travel plan based on the user's health condition. For example, the health monitoring unit can measure the user's heart rate and blood pressure and propose relaxing spots and activities. The health monitoring unit can also analyze the user's number of steps and amount of exercise and propose a travel plan that includes moderate exercise. Furthermore, the health monitoring unit can consider the user's dietary restrictions and allergy information and propose appropriate restaurants and menus. This allows the system to propose a travel plan based on the user's health condition.

[0077] The travel support system can also include a history analysis unit that analyzes the user's past travel history and proposes new travel plans based on previously visited spots and experiences. For example, it can analyze ratings and impressions of previously visited spots and propose similar spots. The history analysis unit can also customize new travel plans based on activities and events that were well-received on past trips. Furthermore, the history analysis unit can analyze the user's past travel patterns and propose optimal travel times and routes. This makes it possible to provide travel plans based on the user's past travel history.

[0078] The travel assistance system may further include an emotion customization unit that estimates the user's emotions and customizes the travel plan based on the estimated emotions. For example, if the user feels like relaxing, the emotion customization unit may suggest tranquil natural landscapes or hot springs. If the user is looking for excitement or adventure, the emotion customization unit may also suggest active activities or adventure spots. Furthermore, the emotion customization unit may collect the user's emotion data in real time and dynamically update the travel plan based on that data. This allows the system to provide a travel plan based on the user's emotions.

[0079] The travel assistance system may further include a hobby analysis unit that proposes travel plans based on the user's interests and hobbies. For example, if the user is interested in history, historical landmarks and museums may be proposed. If the user likes outdoor activities, the hobby analysis unit may also propose hiking and camping spots. Furthermore, the hobby analysis unit may propose events and workshops related to the user's hobbies. This allows the system to provide travel plans based on the user's interests and hobbies.

[0080] The travel assistance system may further include an emotional support unit that estimates the user's emotions and provides support during the trip based on the estimated emotions. For example, if the user is feeling stressed, the emotional support unit may suggest activities or spots that will help the user relax. If the user is enjoying themselves, the emotional support unit may also suggest additional activities or events to further enhance their emotions. Furthermore, the emotional support unit may collect the user's emotional data in real time and dynamically provide support during the trip based on the data. This allows support during the trip to be provided based on the user's emotions.

[0081] The travel assistance system may further include a budget management unit that proposes travel plans based on the user's budget. For example, it may propose accommodations and restaurants according to the user's budget. The budget management unit may also propose activities and events that can be enjoyed within the user's budget. Furthermore, the budget management unit may monitor the user's spending in real time and provide alerts to prevent the user from going over budget. This allows the system to provide travel plans based on the user's budget.

[0082] The travel support system can further include an emotion communication unit that estimates the user's emotions and supports communication at the travel destination based on the estimated emotions. For example, if the user feels anxious, it can provide information about the local language and culture. If the user is enjoying themselves, the emotion communication unit can also suggest social media posts to share their emotions. Furthermore, the emotion communication unit can collect the user's emotion data in real time and dynamically provide communication support based on that data. This makes it possible to provide communication support based on the user's emotions.

[0083] The travel assistance system can further include a safety management unit to ensure the user's safety. For example, it can provide information on the security situation at the travel destination and emergency contact information. The safety management unit can also track the user's current location in real time and issue an alert if the user approaches a dangerous area. Furthermore, the safety management unit can provide information for responding to emergencies such as natural disasters and traffic accidents. This can provide support to ensure the user's safety.

[0084] The travel assistance system may further include an emotional experience unit that estimates the user's emotions and customizes the experience at the travel destination based on the estimated emotions. For example, if the user feels like relaxing, the emotional experience unit may suggest tranquil natural landscapes or hot springs. If the user is looking for excitement or adventure, the emotional experience unit may also suggest active activities or adventure spots. Furthermore, the emotional experience unit may collect the user's emotional data in real time and dynamically customize the experience based on that data. This allows the system to provide a travel experience based on the user's emotions.

[0085] The travel assistance system may further include a language support unit that suggests travel plans based on the user's language. For example, the system may suggest guided tours in the user's native language or activities to learn the local language. The language support unit may also provide phrasebooks and translation tools to help the user communicate in the local language. The language support unit may also provide information about local culture and customs based on the user's language. This allows the system to provide travel plans based on the user's language.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The ranking generation unit uses the generation AI to create a ranking of tourist spots. The generation AI collects data from the internet and analyzes information about travel destinations. Specifically, it collects data from travel blogs, review sites, social media, etc., and analyzes which spots are popular. When a traveler inputs a prompt to the generation AI, such as "Ranking the popular tourist spots in Tokyo," the generation AI will rank Tokyo's tourist spots. Step 2: The address providing unit provides local addresses and detailed information based on the rankings generated by the ranking generating unit. For example, if "Tokyo Tower" is ranked high, detailed information such as the address, nearest station, and opening hours will also be provided. Step 3: The Trending Spot Collection Unit collects hot topics online, such as those that many people have posted about on social media or those that have received high ratings on travel blogs, and creates a list of them.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 7, the 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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. [Explanation of symbols]

[0155] 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 ranking generation unit that uses generation AI to create rankings of tourist spots; an address providing unit that provides local addresses and detailed information based on the rankings generated by the ranking generating unit; A popular spot collection unit that collects spots that are trending online. A system characterized by:

2. The ranking generation unit Generate rankings of tourist attractions based on prompts containing instructions on what the traveler wants the AI ​​to do.

2. The system of claim 1.

3. The address providing unit Provide detailed information such as the address, nearest station, and business hours of the top-ranked spots 2. The system of claim 1.

4. The hot spot collection unit Analyze and list the spots that many people have posted about on the social media platforms and the spots that have received high ratings on travel blogs.

2. The system of claim 1.

5. The ranking generation unit Analyzing the popularity of each of the spots by season and generating the ranking by season.

2. The system of claim 1.

6. The ranking generation unit By adding attribute information such as the age group and gender of travelers, the rankings are generated according to each attribute.

2. The system of claim 1.

7. The ranking generation unit The ranking is generated based on the emotions of travelers, and the spots that evoke positive emotions are ranked preferentially.

2. The system of claim 1.

8. The ranking generation unit The ranking is generated to avoid congestion by taking into consideration the degree of congestion at the spot and the waiting time.

2. The system of claim 1.

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