System

Abstract

An object of a system according to an embodiment is to propose an optimal route according to individual needs of a user.SOLUTION: A system according to an embodiment includes a generation AI, a geographical information database, and a route suggestion unit. The generation AI analyzes the user's needs. The geographic information database provides geographic information. The route suggestion unit suggests an optimal route based on the user's needs analyzed by the generation AI.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 the problem of being unable to propose optimal routes that meet the individual needs of users, and are lacking in convenience and environmental considerations.

[0005] The system according to the embodiment aims to propose an optimal route according to the individual needs of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a geographic information database, and a route suggestion unit. The generation AI analyzes user needs. The geographic information database provides geographic information. The route suggestion unit suggests an optimal route based on the user needs analyzed by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal route according to the individual needs of the user. [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 navigation system according to an embodiment of the present invention utilizes a generation AI and a geographic information database to provide an optimal route according to the individual needs of a user. This allows the navigation system to provide an optimal route according to the diverse needs of the user.

[0029] A navigation system according to an embodiment includes a generation AI, a geographic information database, and a route suggestion unit. The generation AI analyzes user input information and understands the user's needs and circumstances. For example, the generation AI receives a user prompt such as "I would like a route that puts less strain on my lower back" and proposes an optimal route based on the user's needs. The generation AI can also receive a tourist prompt such as "I would like a route that efficiently tours tourist attractions" and propose a route that efficiently tours tourist attractions. The generation AI can also receive a transportation company prompt such as "I would like an optimal route that takes vehicle characteristics into consideration" and propose an optimal route that takes vehicle characteristics into consideration. The geographic information database provides information including road gradients, traffic volume, and tourist attraction location information. For example, the geographic information database provides information for suggesting routes with gentle gradients and easy walking for elderly people. The geographic information database can also provide information for suggesting routes that efficiently tour tourist attractions for tourists. The geographic information database can also provide transportation companies with information for suggesting optimal routes based on vehicle characteristics. The route suggestion unit proposes an optimal route based on the user's needs analyzed by the generation AI. For example, the route suggestion unit suggests routes that put less strain on the lower back based on the needs of elderly people analyzed by the generation AI. The route suggestion unit can also suggest routes that efficiently tour tourist spots based on the needs of tourists analyzed by the generation AI. Furthermore, the route suggestion unit can also suggest optimal routes that take vehicle characteristics into account based on the needs of transportation companies analyzed by the generation AI. This allows the navigation system to provide optimal routes that meet the diverse needs of users.

[0030] The route suggestion unit can suggest routes that put less strain on the lower back according to the elderly person's needs. For example, the route suggestion unit collects the elderly person's past travel history, and the generation AI analyzes that data. For example, it learns the routes they regularly use and the places they visit, and predicts their future travel needs. The route suggestion unit also clusters the elderly person's travel patterns based on the travel history data and identifies user groups with similar patterns. This makes it possible to predict common needs for each group. Furthermore, the route suggestion unit analyzes the elderly person's travel history and learns their travel patterns during specific times of day and days of the week. For example, it can predict weekday commuting routes and weekend leisure routes and suggest optimal routes. This makes it possible to provide optimal routes that meet the elderly person's needs.

[0031] The route suggestion unit can propose an efficient route to tour tourist spots according to the tourist's needs. The route suggestion unit, for example, acquires health data in real time from the user's smartwatch or fitness tracker, and the generation AI analyzes the data. For example, the health condition is evaluated based on the number of steps and heart rate, and an appropriate route is proposed. The route suggestion unit also estimates the user's physical condition and fatigue level based on the health data and proposes a route that is less strenuous. For example, if the heart rate is high, a route with many rest areas can be selected. Furthermore, the route suggestion unit analyzes the user's health data and learns daily fluctuations in health condition. This allows the system to dynamically adjust the route according to the user's specific health condition and provide an optimal travel experience. This makes it possible to provide an optimal route according to the tourist's needs.

[0032] The route suggestion unit can propose an optimal route that takes into account vehicle characteristics according to the needs of the transportation company. The route suggestion unit, for example, analyzes the user's facial expressions and voice to estimate the user's emotional state in real time. For example, if the user is feeling stressed, it can suggest a quiet route or a route with lots of nature. The route suggestion unit also uses an emotion estimation function to calculate the user's emotion score and propose a route according to the user's stress level. For example, it can select a route that passes through places where the user can relax. Furthermore, the route suggestion unit learns the association between the user's past travel history and emotional state based on the user's emotion data. This makes it possible to dynamically adjust a route suitable for a specific emotional state and provide a travel experience that reduces stress. This makes it possible to provide an optimal route that meets the needs of the transportation company.

[0033] Generative AI can analyze a user's past travel history, learn their travel patterns, and predict future needs. For example, generative AI collects a user's past travel history and analyzes that data. For example, it learns the routes they regularly use and the places they visit, and predicts their future travel needs. Generative AI can also cluster users' travel patterns based on travel history data and identify user groups with similar patterns. This makes it possible to predict common needs for each group. Furthermore, generative AI analyzes users' travel history and learns their travel patterns during specific times of the day and days of the week. For example, it can predict weekday commute routes and weekend leisure routes and suggest optimal routes. This makes it possible to predict future needs based on a user's past travel history.

[0034] The generative AI can acquire a user's health data in real time and suggest a route based on their health condition. For example, the generative AI acquires health data in real time from a user's smartwatch or fitness tracker and analyzes that data. For example, it evaluates the user's health condition based on the number of steps and heart rate and suggests an appropriate route. The generative AI can also estimate the user's physical condition and fatigue level based on the health data and suggest a less strenuous route. For example, if the heart rate is high, it can select a route with many rest areas. Furthermore, the generative AI analyzes the user's health data and learns daily fluctuations in their health condition. This allows it to dynamically adjust the route based on specific health conditions and provide an optimal travel experience. This makes it possible to provide the optimal route based on the user's health condition.

[0035] The generation AI can suggest routes that include stopover spots based on the user's hobbies and interests. For example, the generation AI can profile the user's hobbies and interests and suggest routes that include stopover spots based on that. For example, for a user who likes cafes, the generation AI can suggest a route that visits cafes. The generation AI can also analyze the user's social media posts and suggest routes based on their interests. For example, it can detect an interest in parks or museums from the posts and select a route that includes them. Furthermore, the generation AI can identify hobbies and interests based on the user's past travel history and search history and suggest routes that include stopover spots based on that. For example, it can analyze trends in places visited in the past and suggest routes that include similar spots. This makes it possible to provide the optimal route based on the user's hobbies and interests.

[0036] Generative AI can analyze a user's social media posts and suggest routes based on their interests. For example, generative AI can analyze a user's social media posts to identify their interests. For example, it can detect interest in specific places or activities from posted photos and comments and suggest routes based on those interests. Generative AI can also cluster users' topics of interest based on their social media posts and suggest routes that include related spots. For example, a user who posts frequently about food can be suggested a route that includes a tour of restaurants. Furthermore, generative AI can analyze a user's social media posting history and learn their interests from past posts. This allows it to dynamically adjust routes based on the user's latest interests and provide an optimal travel experience. This allows it to provide optimal routes based on the user's social media posts.

[0037] The geographic information database can incorporate real-time weather information and suggest routes that suit the weather. For example, the geographic information database integrates real-time weather information and suggests the optimal route depending on the weather. For example, in rainy weather, it selects a route that goes through a covered walkway or an underground passage. The geographic information database also builds a system that dynamically adjusts the user's travel route based on the weather information. For example, in strong winds, it can suggest a route that is less affected by the wind. Furthermore, the geographic information database analyzes real-time weather data and recalculates the route depending on changes in the weather. For example, in case of sudden rain, it can suggest a route that includes the nearest evacuation site. This makes it possible to provide the optimal route depending on the weather.

[0038] The geographic information database can add user ratings and reviews and prioritize highly rated routes. The geographic information database, for example, integrates user ratings and reviews and prioritizes highly rated routes. For example, it selects routes that include tourist spots that users have given high ratings. The geographic information database also analyzes user reviews and builds a system that quantifies the ratings of specific routes and spots. This makes it possible to automatically select highly rated routes. Furthermore, the geographic information database reflects user feedback and dynamically updates highly rated routes. For example, it can suggest routes that include newly highly rated spots. This makes it possible to provide optimal routes based on user ratings and reviews.

[0039] A geographic information database can integrate operation information of public transportation facilities and propose optimal routes using public transportation facilities. For example, a geographic information database can integrate operation information of public transportation facilities and propose optimal routes using public transportation facilities. For example, a route with fewer transfers can be selected based on bus and train timetables. The geographic information database can also acquire operation information of public transportation facilities in real time and build a system that proposes routes that accommodate delays and cancellations. For example, an alternative route can be proposed in the event of a delay. Furthermore, the geographic information database can integrate operation information of public transportation facilities and dynamically adjust the optimal route according to the user's travel needs. For example, a route during less crowded times can be proposed to avoid congestion. This makes it possible to provide optimal routes using public transportation facilities.

[0040] The geographic information database can add local event information and suggest the optimal route for participating in an event. The geographic information database, for example, integrates local event information and suggests the optimal route for participating in an event. For example, it selects the shortest route based on access information to the event venue. The geographic information database also builds a system that dynamically adjusts a user's travel route based on the event information. For example, it can suggest the optimal departure time based on the start time of the event. Furthermore, the geographic information database integrates event information and suggests a route that includes events that match the user's interests. For example, it can select a route that includes events that the user is interested in, such as a music festival or a food fair. This makes it possible to provide the optimal route based on local event information.

[0041] The route suggestion unit can learn the elderly person's walking speed and rest frequency and propose an optimal route based on that. For example, the route suggestion unit monitors the elderly person's walking speed and rest frequency in real time and proposes an optimal route based on that data. For example, if the walking speed is slow, a route with many rest areas is selected. The route suggestion unit also analyzes the elderly person's past movement data and learns patterns of walking speed and rest frequency. This makes it possible to dynamically adjust routes suitable for each elderly person. Furthermore, the route suggestion unit predicts the walking speed and rest frequency based on the elderly person's health data and proposes an optimal route based on that. For example, if the heart rate is high, a route with many rest areas can be selected. This makes it possible to provide an optimal route based on the elderly person's walking speed and rest frequency.

[0042] The route suggestion unit can propose a less stressful route by taking into account the elderly person's health condition. For example, the route suggestion unit acquires the elderly person's health data (e.g., joint condition) in real time and proposes a less stressful route based on that data. For example, it selects a flat road that puts less strain on the joints. The route suggestion unit also analyzes the elderly person's health condition and builds a system that proposes a route that addresses specific health issues. For example, if the elderly person has knee pain, it can select a route that avoids stairs. Furthermore, the route suggestion unit dynamically adjusts the less stressful route based on the elderly person's health data. For example, if the condition of the elderly person's joints has deteriorated, it can propose a route with many rest areas. This makes it possible to provide a less stressful route based on the elderly person's health condition.

[0043] The geographic information database can integrate information on facilities for the elderly and suggest routes that include them. The geographic information database, for example, integrates information on facilities for the elderly and suggests routes that include rest areas and restrooms. For example, it selects routes with many rest areas. The geographic information database also builds a system that dynamically adjusts a user's travel route based on information on facilities for the elderly. For example, it can suggest an optimal route based on location information of restrooms. The geographic information database also integrates information on facilities for the elderly and dynamically adjusts an optimal route according to the user's needs. For example, it can suggest a route with many rest areas. This makes it possible to provide an optimal route based on information on facilities for the elderly.

[0044] The route suggestion unit can learn the length of stay of tourists and tourist spots of interest, and propose the optimal route based on that. For example, the route suggestion unit profiles the length of stay of tourists and tourist spots of interest, and proposes the optimal route based on that. For example, it selects a route that visits major tourist spots in a short amount of time. The route suggestion unit also analyzes the tourist's past movement data and learns patterns of stay length and spots of interest. This makes it possible to dynamically adjust routes suitable for individual tourists. Furthermore, the route suggestion unit proposes routes that optimize stay length and tourist spots based on the tourist's interests. For example, it can select an efficient route taking into account the congestion status of tourist spots. This makes it possible to provide the optimal route based on the tourist's stay length and interests.

[0045] The route suggestion unit can acquire congestion status at tourist spots in real time and propose a route that avoids congestion. For example, the route suggestion unit acquires congestion status at tourist spots in real time and proposes a route that avoids congestion. For example, the route suggestion unit selects a route that visits tourist spots while avoiding times when congestion is expected. The route suggestion unit also builds a system that dynamically adjusts a user's travel route based on congestion status data. For example, it can propose a route that visits tourist spots during times when congestion is less likely. Furthermore, the route suggestion unit analyzes real-time congestion data and recalculates a route to avoid congestion. For example, it can propose an alternative route if congestion occurs. This makes it possible to provide an optimal route based on the congestion status of tourist spots.

[0046] The geographic information database can integrate guided tour information for tourists and suggest routes for participating in a guided tour. The geographic information database, for example, integrates guided tour information for tourists and suggests the optimal route for participating in a guided tour. For example, the shortest route is selected based on access information to the guided tour meeting point. The geographic information database also builds a system that dynamically adjusts a user's travel route based on the guided tour information. For example, it can suggest the optimal departure time to match the start time of the guided tour. Furthermore, the geographic information database integrates guided tour information and suggests routes that include guided tours that match the user's interests. For example, it can select a route that includes a guided tour that visits historical tourist spots. This makes it possible to provide the optimal route based on the guided tour information for tourists.

[0047] The route suggestion unit can suggest a route that includes restaurants based on the tourist's food preferences. For example, the route suggestion unit profiles the tourist's food preferences and suggests a route that includes restaurants based on the profile. For example, for a vegetarian user, a route that includes vegetarian restaurants is selected. The route suggestion unit also analyzes the tourist's past dining history and learns the tourist's food preference patterns. This makes it possible to dynamically adjust a route that includes restaurants that are suitable for each tourist. Furthermore, the route suggestion unit suggests a route that optimizes restaurants based on the tourist's food preferences. For example, it can select a route that includes highly rated restaurants near tourist attractions. This makes it possible to provide an optimal route based on the tourist's food preferences.

[0048] The generation AI can analyze the carrier's past delivery data and learn and propose efficient delivery routes. For example, the generation AI collects the carrier's past delivery data and analyzes that data. For example, it learns patterns of delivery destinations and delivery times and proposes efficient delivery routes. The generation AI also clusters the carrier's delivery patterns based on the delivery data and identifies delivery routes with similar patterns. This makes it possible to propose efficient routes. Furthermore, the generation AI analyzes the carrier's past delivery data and learns delivery patterns for specific time periods and days of the week. This makes it possible to dynamically adjust efficient delivery routes. This makes it possible to provide efficient delivery routes based on the carrier's past delivery data.

[0049] The generation AI can propose fuel-efficient routes by taking into account fuel efficiency data of the transportation company's vehicles. For example, the generation AI obtains fuel efficiency data of the transportation company's vehicles in real time and proposes fuel-efficient routes based on that data. For example, it selects routes that allow travel at fuel-efficient speeds. The generation AI also builds a system that proposes routes that optimize the vehicle's fuel efficiency based on fuel efficiency data. For example, it can select routes with fewer sudden accelerations and decelerations. Furthermore, the generation AI analyzes the fuel efficiency data of the transportation company's vehicles and dynamically adjusts fuel-efficient routes. For example, if fuel efficiency deteriorates, it can recalculate a more fuel-efficient route. This makes it possible to provide fuel-efficient routes based on the fuel efficiency data of the transportation company's vehicles.

[0050] The geographic information database can integrate information on loading and unloading locations for carriers and propose routes that include the locations. The geographic information database, for example, integrates information on loading and unloading locations for carriers and proposes routes that include the locations. For example, it selects a route with many loading and unloading locations. The geographic information database also builds a system that dynamically adjusts a user's travel route based on the information on loading and unloading locations. For example, it can propose an optimal route based on location information of the loading and unloading locations. The geographic information database also integrates information on loading and unloading locations and dynamically adjusts an optimal route according to the user's needs. For example, it can propose a route with many loading and unloading locations. This makes it possible to provide an optimal route based on information on loading and unloading locations for carriers.

[0051] The geographic information database incorporates information on rest stops and service areas of the carrier and can suggest routes that take rest stops into consideration. The geographic information database, for example, integrates information on rest stops and service areas of the carrier and suggests routes that take rest stops into consideration. For example, it selects a route with many rest stops. The geographic information database also builds a system that dynamically adjusts a user's travel route based on information on rest stops and service areas. For example, it can suggest an optimal route based on location information of rest stops. Furthermore, the geographic information database integrates information on rest stops and service areas and dynamically adjusts an optimal route according to the user's needs. For example, it can suggest a route with many rest stops. This makes it possible to provide an optimal route based on information on rest stops and service areas of the carrier.

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

[0053] The navigation system may further include a voice recognition unit that analyzes the user's voice commands. For example, the voice recognition unit receives a user's voice command, such as "I want to go to the nearest cafe," and analyzes the content of the command. The voice recognition unit can also search for specific locations and routes based on the user's voice command. Furthermore, the voice recognition unit can analyze the user's voice command in real time and send instructions to the navigation system. This allows the user to operate the navigation system hands-free.

[0054] The navigation system may further include an eye-tracking unit that tracks the user's gaze. For example, the eye-tracking unit may measure the time the user spends looking at a particular point on a map and determine that the point is of interest. The eye-tracking unit may also identify points of interest based on the user's gaze data and suggest routes that include those points. The eye-tracking unit may also analyze the user's gaze data in real time and send feedback to the navigation system. This may allow the navigation system to provide an optimal route based on the user's gaze.

[0055] The navigation system may further include a walking speed detection unit that monitors the user's walking speed. The walking speed detection unit may acquire walking speed data from, for example, the user's smartwatch or fitness tracker and analyze the data. The walking speed detection unit may also suggest an appropriate route based on the user's walking speed. The walking speed detection unit may also analyze the user's walking speed data in real time and send feedback to the navigation system. This may allow the system to provide an optimal route based on the user's walking speed.

[0056] The navigation system may further include a temperature detection unit that monitors the user's body temperature. The temperature detection unit may acquire body temperature data from, for example, the user's smartwatch or fitness tracker and analyze the data. The temperature detection unit may also suggest an appropriate route based on the user's body temperature. The temperature detection unit may also analyze the user's body temperature data in real time and provide feedback to the navigation system. This may allow the system to provide an optimal route based on the user's body temperature.

[0057] The navigation system may further include a heart rate detector that monitors the user's heart rate. The heart rate detector may acquire heart rate data from, for example, the user's smartwatch or fitness tracker and analyze the data. The heart rate detector may also suggest an appropriate route based on the user's heart rate. The heart rate detector may also analyze the user's heart rate data in real time and provide feedback to the navigation system. This may allow the navigation system to provide an optimal route based on the user's heart rate.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The generation AI analyzes the user's input information and understands their needs and situation. For example, the generation AI receives a prompt from the user such as "I would like a route that puts less strain on my lower back," and proposes the optimal route based on that need. It can also receive a prompt from a tourist such as "I would like a route that gets around tourist spots efficiently," and propose a route that will get around tourist spots efficiently. Furthermore, it can receive a prompt from a transportation company such as "I would like the optimal route that takes vehicle characteristics into consideration," and propose the optimal route that takes vehicle characteristics into consideration. Step 2: The geographic information database provides information including road gradients, traffic volume, and the locations of tourist attractions. For example, it can provide information to suggest routes with gentle gradients that are easy to walk to for elderly people. It can also provide information to suggest routes for efficiently touring tourist attractions for tourists. It can also provide information to suggest optimal routes for transportation companies based on the characteristics of their vehicles. Step 3: The route suggestion unit proposes the optimal route based on the user's needs analyzed by the generation AI. For example, based on the needs of elderly people analyzed by the generation AI, it proposes a route that puts less strain on the lower back. It can also propose a route that efficiently tours tourist spots based on the needs of tourists analyzed by the generation AI. Furthermore, it can propose the optimal route that takes vehicle characteristics into account based on the needs of transportation companies analyzed by the generation AI.

[0060] (Example 2) A navigation system according to an embodiment of the present invention utilizes a generation AI and a geographic information database to provide an optimal route according to the individual needs of a user. This allows the navigation system to provide an optimal route according to the diverse needs of the user.

[0061] A navigation system according to an embodiment includes a generation AI, a geographic information database, and a route suggestion unit. The generation AI analyzes user input information and understands the user's needs and circumstances. For example, the generation AI receives a user prompt such as "I would like a route that puts less strain on my lower back" and proposes an optimal route based on the user's needs. The generation AI can also receive a tourist prompt such as "I would like a route that efficiently tours tourist attractions" and propose a route that efficiently tours tourist attractions. The generation AI can also receive a transportation company prompt such as "I would like an optimal route that takes vehicle characteristics into consideration" and propose an optimal route that takes vehicle characteristics into consideration. The geographic information database provides information including road gradients, traffic volume, and tourist attraction location information. For example, the geographic information database provides information for suggesting routes with gentle gradients and easy walking for elderly people. The geographic information database can also provide information for suggesting routes that efficiently tour tourist attractions for tourists. The geographic information database can also provide transportation companies with information for suggesting optimal routes based on vehicle characteristics. The route suggestion unit proposes an optimal route based on the user's needs analyzed by the generation AI. For example, the route suggestion unit suggests routes that put less strain on the lower back based on the needs of elderly people analyzed by the generation AI. The route suggestion unit can also suggest routes that efficiently tour tourist spots based on the needs of tourists analyzed by the generation AI. Furthermore, the route suggestion unit can also suggest optimal routes that take vehicle characteristics into account based on the needs of transportation companies analyzed by the generation AI. This allows the navigation system to provide optimal routes that meet the diverse needs of users.

[0062] The route suggestion unit can suggest routes that put less strain on the lower back according to the elderly person's needs. For example, the route suggestion unit collects the elderly person's past travel history, and the generation AI analyzes that data. For example, it learns the routes they regularly use and the places they visit, and predicts their future travel needs. The route suggestion unit also clusters the elderly person's travel patterns based on the travel history data and identifies user groups with similar patterns. This makes it possible to predict common needs for each group. Furthermore, the route suggestion unit analyzes the elderly person's travel history and learns their travel patterns during specific times of day and days of the week. For example, it can predict weekday commuting routes and weekend leisure routes and suggest optimal routes. This makes it possible to provide optimal routes that meet the elderly person's needs.

[0063] The route suggestion unit can propose an efficient route to tour tourist spots according to the tourist's needs. The route suggestion unit, for example, acquires health data in real time from the user's smartwatch or fitness tracker, and the generation AI analyzes the data. For example, the health condition is evaluated based on the number of steps and heart rate, and an appropriate route is proposed. The route suggestion unit also estimates the user's physical condition and fatigue level based on the health data and proposes a route that is less strenuous. For example, if the heart rate is high, a route with many rest areas can be selected. Furthermore, the route suggestion unit analyzes the user's health data and learns daily fluctuations in health condition. This allows the system to dynamically adjust the route according to the user's specific health condition and provide an optimal travel experience. This makes it possible to provide an optimal route according to the tourist's needs.

[0064] The route suggestion unit can propose an optimal route that takes into account vehicle characteristics according to the needs of the transportation company. The route suggestion unit, for example, analyzes the user's facial expressions and voice to estimate the user's emotional state in real time. For example, if the user is feeling stressed, it can suggest a quiet route or a route with lots of nature. The route suggestion unit also uses an emotion estimation function to calculate the user's emotion score and propose a route according to the user's stress level. For example, it can select a route that passes through places where the user can relax. Furthermore, the route suggestion unit learns the association between the user's past travel history and emotional state based on the user's emotion data. This makes it possible to dynamically adjust a route suitable for a specific emotional state and provide a travel experience that reduces stress. This makes it possible to provide an optimal route that meets the needs of the transportation company.

[0065] Generative AI can analyze a user's past travel history, learn their travel patterns, and predict future needs. For example, generative AI collects a user's past travel history and analyzes that data. For example, it learns the routes they regularly use and the places they visit, and predicts their future travel needs. Generative AI can also cluster users' travel patterns based on travel history data and identify user groups with similar patterns. This makes it possible to predict common needs for each group. Furthermore, generative AI analyzes users' travel history and learns their travel patterns during specific times of the day and days of the week. For example, it can predict weekday commute routes and weekend leisure routes and suggest optimal routes. This makes it possible to predict future needs based on a user's past travel history.

[0066] The generative AI can acquire a user's health data in real time and suggest a route based on their health condition. For example, the generative AI acquires health data in real time from a user's smartwatch or fitness tracker and analyzes that data. For example, it evaluates the user's health condition based on the number of steps and heart rate and suggests an appropriate route. The generative AI can also estimate the user's physical condition and fatigue level based on the health data and suggest a less strenuous route. For example, if the heart rate is high, it can select a route with many rest areas. Furthermore, the generative AI analyzes the user's health data and learns daily fluctuations in their health condition. This allows it to dynamically adjust the route based on specific health conditions and provide an optimal travel experience. This makes it possible to provide the optimal route based on the user's health condition.

[0067] The generation AI can use its emotion estimation function to analyze the user's current emotional state and suggest routes that reduce stress. For example, the generation AI can analyze the user's facial expressions and voice to estimate their emotional state in real time. For example, if the user is feeling stressed, it can suggest quiet routes or routes with lots of nature. The generation AI can also use its emotion estimation function to calculate the user's emotional score and suggest routes based on their stress level. For example, it can select routes that pass through relaxing areas. Furthermore, the generation AI can learn the relationship between the user's past travel history and their emotional state based on the user's emotional data. This allows it to dynamically adjust routes appropriate for specific emotional states and provide a travel experience that reduces stress. This makes it possible to provide the optimal route based on the user's emotional state.

[0068] The generation AI can suggest routes that include stopover spots based on the user's hobbies and interests. For example, the generation AI can profile the user's hobbies and interests and suggest routes that include stopover spots based on that. For example, for a user who likes cafes, the generation AI can suggest a route that visits cafes. The generation AI can also analyze the user's social media posts and suggest routes based on their interests. For example, it can detect an interest in parks or museums from the posts and select a route that includes them. Furthermore, the generation AI can identify hobbies and interests based on the user's past travel history and search history and suggest routes that include stopover spots based on that. For example, it can analyze trends in places visited in the past and suggest routes that include similar spots. This makes it possible to provide the optimal route based on the user's hobbies and interests.

[0069] Generative AI can analyze a user's social media posts and suggest routes based on their interests. For example, generative AI can analyze a user's social media posts to identify their interests. For example, it can detect interest in specific places or activities from posted photos and comments and suggest routes based on those interests. Generative AI can also cluster users' topics of interest based on their social media posts and suggest routes that include related spots. For example, a user who posts frequently about food can be suggested a route that includes a tour of restaurants. Furthermore, generative AI can analyze a user's social media posting history and learn their interests from past posts. This allows it to dynamically adjust routes based on the user's latest interests and provide an optimal travel experience. This allows it to provide optimal routes based on the user's social media posts.

[0070] The geographic information database can incorporate real-time weather information and suggest routes that suit the weather. For example, the geographic information database integrates real-time weather information and suggests the optimal route depending on the weather. For example, in rainy weather, it selects a route that goes through a covered walkway or an underground passage. The geographic information database also builds a system that dynamically adjusts the user's travel route based on the weather information. For example, in strong winds, it can suggest a route that is less affected by the wind. Furthermore, the geographic information database analyzes real-time weather data and recalculates the route depending on changes in the weather. For example, in case of sudden rain, it can suggest a route that includes the nearest evacuation site. This makes it possible to provide the optimal route depending on the weather.

[0071] The geographic information database can add user ratings and reviews and prioritize highly rated routes. The geographic information database, for example, integrates user ratings and reviews and prioritizes highly rated routes. For example, it selects routes that include tourist spots that users have given high ratings. The geographic information database also analyzes user reviews and builds a system that quantifies the ratings of specific routes and spots. This makes it possible to automatically select highly rated routes. Furthermore, the geographic information database reflects user feedback and dynamically updates highly rated routes. For example, it can suggest routes that include newly highly rated spots. This makes it possible to provide optimal routes based on user ratings and reviews.

[0072] The geographic information database can use the emotion estimation function to integrate past user emotion data and suggest routes that are emotionally satisfying. The geographic information database, for example, uses the emotion estimation function to integrate past user emotion data. For example, a route that is highly satisfying can be suggested based on positive emotion data at specific locations. The geographic information database also analyzes the user emotion data and builds a system that identifies spots that are emotionally satisfying. This makes it possible to select routes that elicit positive emotions. Furthermore, the geographic information database integrates the emotion data and dynamically updates the routes that are emotionally satisfying. For example, it can suggest routes that include spots that have newly received positive emotion data. This makes it possible to provide routes that are emotionally satisfying.

[0073] A geographic information database can integrate operation information of public transportation facilities and propose optimal routes using public transportation facilities. For example, a geographic information database can integrate operation information of public transportation facilities and propose optimal routes using public transportation facilities. For example, a route with fewer transfers can be selected based on bus and train timetables. The geographic information database can also acquire operation information of public transportation facilities in real time and build a system that proposes routes that accommodate delays and cancellations. For example, an alternative route can be proposed in the event of a delay. Furthermore, the geographic information database can integrate operation information of public transportation facilities and dynamically adjust the optimal route according to the user's travel needs. For example, a route during less crowded times can be proposed to avoid congestion. This makes it possible to provide optimal routes using public transportation facilities.

[0074] The geographic information database can add local event information and suggest the optimal route for participating in an event. The geographic information database, for example, integrates local event information and suggests the optimal route for participating in an event. For example, it selects the shortest route based on access information to the event venue. The geographic information database also builds a system that dynamically adjusts a user's travel route based on the event information. For example, it can suggest the optimal departure time based on the start time of the event. Furthermore, the geographic information database integrates event information and suggests a route that includes events that match the user's interests. For example, it can select a route that includes events that the user is interested in, such as a music festival or a food fair. This makes it possible to provide the optimal route based on local event information.

[0075] The geographic information database can use the emotion estimation function to analyze the user's emotional response in a specific area and suggest a route that passes through emotionally positive areas. The geographic information database, for example, uses the emotion estimation function to analyze the user's emotional response in a specific area. For example, based on past emotion data, it identifies areas that elicit positive emotions and suggests a route that passes through those areas. The geographic information database also analyzes the user's emotion data and builds a system that learns the emotional responses in specific areas. This makes it possible to dynamically adjust the route that elicits positive emotions. Furthermore, the geographic information database integrates the emotion data and dynamically updates the route that passes through emotionally positive areas. For example, it can suggest a route that includes areas that have newly received positive emotion data. This makes it possible to provide a route that passes through emotionally positive areas.

[0076] The route suggestion unit can learn the elderly person's walking speed and rest frequency and propose an optimal route based on that. For example, the route suggestion unit monitors the elderly person's walking speed and rest frequency in real time and proposes an optimal route based on that data. For example, if the walking speed is slow, a route with many rest areas is selected. The route suggestion unit also analyzes the elderly person's past movement data and learns patterns of walking speed and rest frequency. This makes it possible to dynamically adjust routes suitable for each elderly person. Furthermore, the route suggestion unit predicts the walking speed and rest frequency based on the elderly person's health data and proposes an optimal route based on that. For example, if the heart rate is high, a route with many rest areas can be selected. This makes it possible to provide an optimal route based on the elderly person's walking speed and rest frequency.

[0077] The route suggestion unit can propose a less stressful route by taking into account the elderly person's health condition. For example, the route suggestion unit acquires the elderly person's health data (e.g., joint condition) in real time and proposes a less stressful route based on that data. For example, it selects a flat road that puts less strain on the joints. The route suggestion unit also analyzes the elderly person's health condition and builds a system that proposes a route that addresses specific health issues. For example, if the elderly person has knee pain, it can select a route that avoids stairs. Furthermore, the route suggestion unit dynamically adjusts the less stressful route based on the elderly person's health data. For example, if the condition of the elderly person's joints has deteriorated, it can propose a route with many rest areas. This makes it possible to provide a less stressful route based on the elderly person's health condition.

[0078] The route suggestion unit can use the emotion estimation function to suggest a route that gives the elderly a sense of security. The route suggestion unit, for example, uses the emotion estimation function to suggest a route that gives the elderly a sense of security. For example, the route suggestion unit selects a route that elicits a sense of security based on past emotion data. The route suggestion unit also analyzes the emotion data of the elderly and builds a system that learns emotional reactions at specific locations. This makes it possible to dynamically adjust a route that gives the elderly a sense of security. Furthermore, the route suggestion unit uses the emotion estimation function to monitor in real time the sense of security that the elderly feels at specific locations and suggests a route that elicits a sense of security. For example, it can select a route that passes through places where the user can relax. This makes it possible to provide a route that gives the elderly a sense of security.

[0079] The geographic information database can integrate information on facilities for the elderly and suggest routes that include them. The geographic information database, for example, integrates information on facilities for the elderly and suggests routes that include rest areas and restrooms. For example, it selects routes with many rest areas. The geographic information database also builds a system that dynamically adjusts a user's travel route based on information on facilities for the elderly. For example, it can suggest an optimal route based on location information of restrooms. The geographic information database also integrates information on facilities for the elderly and dynamically adjusts an optimal route according to the user's needs. For example, it can suggest a route with many rest areas. This makes it possible to provide an optimal route based on information on facilities for the elderly.

[0080] The geographic information database can use the emotion estimation function to predict the sense of security that an elderly person will feel in a specific location and suggest a route that will elicit that sense of security. The geographic information database, for example, uses the emotion estimation function to predict the sense of security that an elderly person will feel in a specific location. For example, based on past emotion data, it identifies locations that will elicit a sense of security and suggests a route that includes those locations. The geographic information database also analyzes the emotion data of the elderly and builds a system that learns their emotional reactions in specific locations. This makes it possible to dynamically adjust a route that will elicit a sense of security. Furthermore, the geographic information database can use the emotion estimation function to monitor in real time the sense of security that an elderly person will feel in a specific location and suggest a route that will elicit a sense of security. For example, it can select a route that passes through places where the user can relax. This makes it possible to provide a route that will give the elderly a sense of security.

[0081] The route suggestion unit can learn the length of stay of tourists and tourist spots of interest, and propose the optimal route based on that. For example, the route suggestion unit profiles the length of stay of tourists and tourist spots of interest, and proposes the optimal route based on that. For example, it selects a route that visits major tourist spots in a short amount of time. The route suggestion unit also analyzes the tourist's past movement data and learns patterns of stay length and spots of interest. This makes it possible to dynamically adjust routes suitable for individual tourists. Furthermore, the route suggestion unit proposes routes that optimize stay length and tourist spots based on the tourist's interests. For example, it can select an efficient route taking into account the congestion status of tourist spots. This makes it possible to provide the optimal route based on the tourist's stay length and interests.

[0082] The route suggestion unit can acquire congestion status at tourist spots in real time and propose a route that avoids congestion. For example, the route suggestion unit acquires congestion status at tourist spots in real time and proposes a route that avoids congestion. For example, the route suggestion unit selects a route that visits tourist spots while avoiding times when congestion is expected. The route suggestion unit also builds a system that dynamically adjusts a user's travel route based on congestion status data. For example, it can propose a route that visits tourist spots during times when congestion is less likely. Furthermore, the route suggestion unit analyzes real-time congestion data and recalculates a route to avoid congestion. For example, it can propose an alternative route if congestion occurs. This makes it possible to provide an optimal route based on the congestion status of tourist spots.

[0083] The route suggestion unit can use the emotion estimation function to suggest a route that tourists will find enjoyable. The route suggestion unit, for example, uses the emotion estimation function to suggest a route that tourists will find enjoyable. For example, the route suggestion unit selects a route that includes tourist spots that will bring out enjoyment based on past emotion data. The route suggestion unit also analyzes tourist emotion data and builds a system that learns emotional reactions at specific tourist spots. This makes it possible to dynamically adjust a route that will bring out enjoyment. Furthermore, the route suggestion unit uses the emotion estimation function to monitor in real time the enjoyment that tourists feel at specific tourist spots and suggest a route that will bring out enjoyment. For example, it can select a route that passes through places that users will enjoy. This makes it possible to provide a route that tourists will find enjoyable.

[0084] The geographic information database can integrate guided tour information for tourists and suggest routes for participating in a guided tour. The geographic information database, for example, integrates guided tour information for tourists and suggests the optimal route for participating in a guided tour. For example, the shortest route is selected based on access information to the guided tour meeting point. The geographic information database also builds a system that dynamically adjusts a user's travel route based on the guided tour information. For example, it can suggest the optimal departure time to match the start time of the guided tour. Furthermore, the geographic information database integrates guided tour information and suggests routes that include guided tours that match the user's interests. For example, it can select a route that includes a guided tour that visits historical tourist spots. This makes it possible to provide the optimal route based on the guided tour information for tourists.

[0085] The route suggestion unit can suggest a route that includes restaurants based on the tourist's food preferences. For example, the route suggestion unit profiles the tourist's food preferences and suggests a route that includes restaurants based on the profile. For example, for a vegetarian user, a route that includes vegetarian restaurants is selected. The route suggestion unit also analyzes the tourist's past dining history and learns the tourist's food preference patterns. This makes it possible to dynamically adjust a route that includes restaurants that are suitable for each tourist. Furthermore, the route suggestion unit suggests a route that optimizes restaurants based on the tourist's food preferences. For example, it can select a route that includes highly rated restaurants near tourist attractions. This makes it possible to provide an optimal route based on the tourist's food preferences.

[0086] The route suggestion unit can use the emotion estimation function to predict the emotions that tourists will feel at specific tourist spots and propose a route that will bring out their enjoyment. The route suggestion unit, for example, uses the emotion estimation function to predict the emotions that tourists will feel at specific tourist spots. For example, based on past emotion data, the route suggestion unit identifies tourist spots that will bring out their enjoyment and proposes a route that includes those tourist spots. The route suggestion unit also analyzes the emotion data of tourists and builds a system that learns their emotional reactions at specific tourist spots. This makes it possible to dynamically adjust a route that will bring out their enjoyment. Furthermore, the route suggestion unit uses the emotion estimation function to monitor in real time the enjoyment that tourists will feel at specific tourist spots and propose a route that will bring out their enjoyment. For example, it can select a route that passes through places that the user will enjoy. This makes it possible to provide a route that tourists will find enjoyable.

[0087] The generation AI can analyze the carrier's past delivery data and learn and propose efficient delivery routes. For example, the generation AI collects the carrier's past delivery data and analyzes that data. For example, it learns patterns of delivery destinations and delivery times and proposes efficient delivery routes. The generation AI also clusters the carrier's delivery patterns based on the delivery data and identifies delivery routes with similar patterns. This makes it possible to propose efficient routes. Furthermore, the generation AI analyzes the carrier's past delivery data and learns delivery patterns for specific time periods and days of the week. This makes it possible to dynamically adjust efficient delivery routes. This makes it possible to provide efficient delivery routes based on the carrier's past delivery data.

[0088] The generation AI can propose fuel-efficient routes by taking into account fuel efficiency data of the transportation company's vehicles. For example, the generation AI obtains fuel efficiency data of the transportation company's vehicles in real time and proposes fuel-efficient routes based on that data. For example, it selects routes that allow travel at fuel-efficient speeds. The generation AI also builds a system that proposes routes that optimize the vehicle's fuel efficiency based on fuel efficiency data. For example, it can select routes with fewer sudden accelerations and decelerations. Furthermore, the generation AI analyzes the fuel efficiency data of the transportation company's vehicles and dynamically adjusts fuel-efficient routes. For example, if fuel efficiency deteriorates, it can recalculate a more fuel-efficient route. This makes it possible to provide fuel-efficient routes based on the fuel efficiency data of the transportation company's vehicles.

[0089] The geographic information database can integrate information on loading and unloading locations for carriers and propose routes that include the locations. The geographic information database, for example, integrates information on loading and unloading locations for carriers and proposes routes that include the locations. For example, it selects a route with many loading and unloading locations. The geographic information database also builds a system that dynamically adjusts a user's travel route based on the information on loading and unloading locations. For example, it can propose an optimal route based on location information of the loading and unloading locations. The geographic information database also integrates information on loading and unloading locations and dynamically adjusts an optimal route according to the user's needs. For example, it can propose a route with many loading and unloading locations. This makes it possible to provide an optimal route based on information on loading and unloading locations for carriers.

[0090] The geographic information database incorporates information on rest stops and service areas of the carrier and can suggest routes that take rest stops into consideration. The geographic information database, for example, integrates information on rest stops and service areas of the carrier and suggests routes that take rest stops into consideration. For example, it selects a route with many rest stops. The geographic information database also builds a system that dynamically adjusts a user's travel route based on information on rest stops and service areas. For example, it can suggest an optimal route based on location information of rest stops. Furthermore, the geographic information database integrates information on rest stops and service areas and dynamically adjusts an optimal route according to the user's needs. For example, it can suggest a route with many rest stops. This makes it possible to provide an optimal route based on information on rest stops and service areas of the carrier.

[0091] The geographic information database can use the emotion estimation function to predict the stress that a delivery agent will feel on a specific route and suggest a route that reduces stress. The geographic information database, for example, uses the emotion estimation function to predict the stress that a delivery agent will feel on a specific route. For example, routes that induce stress can be avoided based on past emotion data. The geographic information database also analyzes the emotion data of delivery agents and builds a system that learns their emotional responses on specific routes. This makes it possible to dynamically adjust routes that reduce stress. Furthermore, the geographic information database can use the emotion estimation function to monitor in real time the stress that a delivery agent will feel on a specific route and suggest routes that reduce stress. For example, a route that allows the user to relax can be selected. This makes it possible to provide routes that reduce the stress that a delivery agent will feel on a specific route.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The navigation system may further include a voice recognition unit that analyzes the user's voice commands. For example, the voice recognition unit receives a user's voice command, such as "I want to go to the nearest cafe," and analyzes the content of the command. The voice recognition unit can also search for specific locations and routes based on the user's voice command. Furthermore, the voice recognition unit can analyze the user's voice command in real time and send instructions to the navigation system. This allows the user to operate the navigation system hands-free.

[0094] The navigation system may further include an eye-tracking unit that tracks the user's gaze. For example, the eye-tracking unit may measure the time the user spends looking at a particular point on a map and determine that the point is of interest. The eye-tracking unit may also identify points of interest based on the user's gaze data and suggest routes that include those points. The eye-tracking unit may also analyze the user's gaze data in real time and send feedback to the navigation system. This may allow the navigation system to provide an optimal route based on the user's gaze.

[0095] The navigation system may further include a walking speed detection unit that monitors the user's walking speed. The walking speed detection unit may acquire walking speed data from, for example, the user's smartwatch or fitness tracker and analyze the data. The walking speed detection unit may also suggest an appropriate route based on the user's walking speed. The walking speed detection unit may also analyze the user's walking speed data in real time and send feedback to the navigation system. This may allow the system to provide an optimal route based on the user's walking speed.

[0096] The navigation system may further include a temperature detection unit that monitors the user's body temperature. The temperature detection unit may acquire body temperature data from, for example, the user's smartwatch or fitness tracker and analyze the data. The temperature detection unit may also suggest an appropriate route based on the user's body temperature. The temperature detection unit may also analyze the user's body temperature data in real time and provide feedback to the navigation system. This may allow the system to provide an optimal route based on the user's body temperature.

[0097] The navigation system may further include a heart rate detector that monitors the user's heart rate. The heart rate detector may acquire heart rate data from, for example, the user's smartwatch or fitness tracker and analyze the data. The heart rate detector may also suggest an appropriate route based on the user's heart rate. The heart rate detector may also analyze the user's heart rate data in real time and provide feedback to the navigation system. This may allow the navigation system to provide an optimal route based on the user's heart rate.

[0098] The navigation system can also estimate the user's emotions and suggest relaxing routes based on the estimated user emotions. For example, if the user is feeling stressed, it can suggest quieter routes or routes with more nature. Furthermore, the navigation system can learn the relationship between the user's past travel history and emotional state based on the user's emotional data. This allows it to dynamically adjust routes appropriate for specific emotional states and provide a relaxing travel experience.

[0099] The navigation system can further estimate the user's emotions and suggest routes that will bring out enjoyment based on the estimated user emotions. For example, if the user is feeling enjoyable, it can suggest a route that includes tourist spots and leisure facilities. The navigation system can also learn the association between the user's past travel history and emotional state based on the user's emotional data. This allows it to dynamically adjust routes that are appropriate for a specific emotional state and provide an enjoyable travel experience.

[0100] The navigation system can further estimate the user's emotions and suggest routes that provide a sense of security based on the estimated user emotions. For example, if the user is feeling anxious, it can suggest routes that are well-lit and have a lot of people. The navigation system can also learn the relationship between past travel history and emotional states based on the user's emotional data. This allows it to dynamically adjust routes that are appropriate for a specific emotional state and provide a travel experience that provides a sense of security.

[0101] The navigation system can also estimate the user's emotions and suggest routes that reduce stress based on the estimated user emotions. For example, if the user is feeling stressed, it can suggest quieter routes or routes with more nature. Furthermore, the navigation system can learn the relationship between the user's past travel history and emotional state based on the user's emotional data. This allows the system to dynamically adjust routes appropriate for a specific emotional state and provide a travel experience that reduces stress.

[0102] The navigation system can further estimate the user's emotions and suggest routes that elicit positive emotions based on the estimated user emotions. For example, if the user is feeling positive emotions, the system can suggest a route that includes tourist spots and leisure facilities. The navigation system can also learn the association between the user's past travel history and emotional state based on the user's emotional data. This allows the system to dynamically adjust routes appropriate for a specific emotional state and provide a travel experience that elicits positive emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The generation AI analyzes the user's input information and understands their needs and situation. For example, the generation AI receives a prompt from the user such as "I would like a route that puts less strain on my lower back," and proposes the optimal route based on that need. It can also receive a prompt from a tourist such as "I would like a route that gets around tourist spots efficiently," and propose a route that will get around tourist spots efficiently. Furthermore, it can receive a prompt from a transportation company such as "I would like the optimal route that takes vehicle characteristics into consideration," and propose the optimal route that takes vehicle characteristics into consideration. Step 2: The geographic information database provides information including road gradients, traffic volume, and the locations of tourist attractions. For example, it can provide information to suggest routes with gentle gradients that are easy to walk to for elderly people. It can also provide information to suggest routes for efficiently touring tourist attractions for tourists. It can also provide information to suggest optimal routes for transportation companies based on the characteristics of their vehicles. Step 3: The route suggestion unit proposes the optimal route based on the user's needs analyzed by the generation AI. For example, based on the needs of elderly people analyzed by the generation AI, it proposes a route that puts less strain on the lower back. It can also propose a route that efficiently tours tourist spots based on the needs of tourists analyzed by the generation AI. Furthermore, it can propose the optimal route that takes vehicle characteristics into account based on the needs of transportation companies analyzed by the generation AI.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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. Generative AI and A geographic information database; a route suggestion unit that suggests an optimal route based on the user's needs analyzed by the generation AI; A system characterized by:

2. The route suggestion unit Proposing routes that put less strain on the lower back in response to the needs of the elderly 2. The system of claim 1.

3. The route suggestion unit Proposing efficient routes to visit tourist spots according to the needs of tourists 2. The system of claim 1.

4. The route suggestion unit Proposing optimal routes that take into account vehicle characteristics according to the needs of transport companies 2. The system of claim 1.

5. The generated AI is Analyzing the user's past travel history, learning their travel patterns, and predicting their future needs.

2. The system of claim 1.

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