System

Abstract

An object of a system according to an embodiment is to propose an optimal route in consideration of a user's preference and a real-time situation.SOLUTION: A system according to an embodiment comprises a user preference analyzer, a route suggester, and a real-time data analyzer. The user preference analysis unit analyzes a user's preference. The route suggestion unit suggests a route based on the preference analyzed by the user preference analysis unit. The real-time data analysis unit analyzes real-time data of season, weather, and congestion situation to optimize the route.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 do not adequately propose optimal routes that take into account user preferences and real-time conditions, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal route taking into consideration the user's preferences and real-time conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes a user preference analysis unit, a route suggestion unit, and a real-time data analysis unit. The user preference analysis unit analyzes user preferences. The route suggestion unit suggests a route based on the preferences analyzed by the user preference analysis unit. The real-time data analysis unit analyzes real-time data on season, weather, and congestion status to optimize the route. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal route taking into account the user's preferences and real-time conditions. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The AI ​​system according to the embodiment of the present invention not only suggests optimal departure times and routes based on the user's preferences, but also suggests routes with scenic views, stations where you can eat, lines where you can sit, lines where you can share a ride with friends, etc. In this way, the AI ​​system can make commuting and travelling not just a means of transportation, but also a part of enjoyment and new experiences.

[0029] The AI ​​system according to the embodiment includes a user preference analysis unit, a route suggestion unit, and a real-time data analysis unit. The user preference analysis unit analyzes the user's preferences. For example, it analyzes the user's past travel history and favorite places. The user preference analysis unit can also analyze the user's preferences from social media posts and photos. For example, it performs text analysis and image analysis of social media posts. The route suggestion unit suggests a route based on the preferences analyzed by the user preference analysis unit. For example, for a user who wants to enjoy scenery, it suggests routes with beautiful scenery for each season. For a user who wants to enjoy meals, the route suggestion unit can also suggest routes with stations and restaurants where meals are available. For example, it selects a route that includes restaurants that match the user's preferences. The real-time data analysis unit analyzes real-time data such as the season, weather, and congestion status to optimize the route. For example, it selects and suggests routes that are less crowded or have a higher chance of finding a seat. In addition, the real-time data analysis unit can also suggest routes that involve more indoor travel when the weather is bad. For example, it selects a route that uses underground passages on rainy days. As a result, the AI ​​system according to the embodiment can propose an optimal route based on the user's preferences and real-time data. For example, the user can travel comfortably without stress. The user can also enjoy new experiences.

[0030] The route suggestion unit can analyze the user's preferences from social media posts and photos as well as the user's past travel history, and propose more personalized routes. The route suggestion unit, for example, analyzes the user's social media posts to identify favorite places and activities. For example, it can propose routes that include cafes and parks that the user frequently posts about. The route suggestion unit can also analyze the user's photos to identify favorite scenery and places. For example, it can propose routes that include scenery that is frequently seen in photos taken by the user. The route suggestion unit can also analyze the user's past travel history to identify favorite routes. For example, it can re-suggest routes that the user has used many times in the past. This makes it possible to propose more personalized routes based on the user's social media data. For example, the user can enjoy routes that suit their preferences.

[0031] The route suggestion unit can suggest a healthy route by taking into account the user's health condition and fitness data. The route suggestion unit, for example, analyzes the user's fitness data and suggests a route that ensures an appropriate amount of exercise. For example, it selects a route that takes into account the number of steps and calories burned. The route suggestion unit can also analyze the user's health condition and suggest a healthy route. For example, it selects a route that ensures an appropriate amount of exercise based on the user's heart rate and blood pressure. The route suggestion unit can also suggest a route based on the user's health goals. For example, it suggests a route that burns a lot of calories to a user who is on a diet. This makes it possible to suggest a healthy route based on the user's health condition. For example, the user can maintain their health while traveling.

[0032] The route suggestion unit can propose routes based on the user's preferences not only for commuting but also for weekend leisure activities and shopping. The route suggestion unit, for example, analyzes the user's weekend leisure plans and proposes an optimal route. For example, it selects a route that includes tourist attractions and event venues that the user wants to visit. The route suggestion unit can also analyze the user's shopping plans and propose an optimal route. For example, it selects a route that includes shopping malls and stores that the user wants to visit. The route suggestion unit can also analyze the user's commuting route and apply it to weekend leisure activities and shopping. For example, it proposes a route that includes tourist attractions and stores on the commuting route. This makes it possible to propose optimal routes not only for commuting but also for weekend leisure activities and shopping. For example, the user can enjoy traveling on the weekend.

[0033] The route suggestion unit can extend route suggestions based on the user's preferences to other means of transportation, such as cars and bicycles. The route suggestion unit, for example, analyzes the user's car travel history and suggests an optimal driving route. For example, it selects a route that includes scenic driving routes and rest spots. The route suggestion unit can also analyze the user's bicycle travel history and suggest an optimal cycling route. For example, it selects a route that includes roads suitable for cycling and rest spots. The route suggestion unit can also be applied to other means of transportation based on the user's preferences. For example, it can suggest routes that involve walking or public transportation. This makes it possible to suggest optimal routes for other means of transportation, such as cars and bicycles. For example, the user can travel comfortably using various means of transportation.

[0034] The real-time data analysis unit can propose routes taking into consideration not only traffic congestion but also real-time traffic accident and construction information. The real-time data analysis unit, for example, analyzes real-time traffic accident information and proposes routes where no accidents have occurred. For example, it selects a route that avoids areas where accidents frequently occur. The real-time data analysis unit can also analyze real-time construction information and propose routes where no construction is taking place. For example, it selects a route that avoids roads under construction. The real-time data analysis unit can also analyze real-time traffic information and propose an optimal route. For example, it selects a route that avoids traffic congestion. This makes it possible to propose an optimal route taking into consideration traffic accident and construction information. For example, the user can travel safely and smoothly.

[0035] The real-time data analysis unit can also apply route suggestions that take congestion into account when congestion is expected during specific time periods, such as at events or concerts. The real-time data analysis unit, for example, analyzes information about events or concerts and suggests routes that avoid times when congestion is expected. For example, it selects a detour route that avoids congestion around the event venue. The real-time data analysis unit can also be applied when congestion is expected during specific time periods. For example, it suggests a route that avoids congestion around tourist spots during rush hour or on holidays. The real-time data analysis unit can also analyze the user's past travel history and apply it when congestion is expected during specific time periods. For example, it re-suggests routes that have avoided congestion in the past. This makes it possible to suggest optimal routes even when congestion is expected during specific time periods, such as at events or concerts. For example, the user can travel comfortably by avoiding congestion.

[0036] The real-time data analysis unit can apply route suggestions that take congestion into account not only to public transportation but also to parking congestion. The real-time data analysis unit, for example, analyzes the real-time congestion status of parking lots and suggests routes that include vacant parking lots. For example, it may preferentially select vacant parking lots near the destination. The real-time data analysis unit can also analyze the congestion status of public transportation and suggest optimal routes. For example, it may select routes that are not congested or routes where there is a high chance of finding a seat. The real-time data analysis unit can also analyze the congestion status of parking lots and suggest optimal routes. For example, if a parking lot is full, it may suggest another parking lot. This makes it possible to suggest optimal routes that take congestion status of parking lots into consideration. For example, the user can travel comfortably by avoiding crowded parking lots.

[0037] The real-time data analysis unit can propose routes taking into consideration not only the season and weather, but also the user's clothing and belongings. The real-time data analysis unit, for example, analyzes the user's clothing data and proposes an appropriate route. For example, if the user does not have an umbrella, an indoor route is selected. The real-time data analysis unit can also analyze the user's belongings data and propose an appropriate route. For example, if the user has a camera, a route including photo spots is selected. The real-time data analysis unit can also analyze the user's past movement history and propose a route based on the user's clothing and belongings. For example, it can re-propose routes that the user has traveled comfortably in the past. This makes it possible to propose an optimal route taking into consideration the user's clothing and belongings. For example, the user can travel comfortably based on their clothing and belongings.

[0038] The real-time data analysis unit can take seasonal event information into consideration and propose a route that allows the user to enjoy an event. The real-time data analysis unit, for example, analyzes seasonal event information and proposes an optimal route. For example, it selects a route that includes a location where a fireworks display is being held. The real-time data analysis unit can also analyze the user's past movement history and propose a route that allows the user to enjoy an event. For example, it re-proposes a route that includes an event that the user enjoyed in the past. The real-time data analysis unit can also analyze the user's emotional data and propose a route that allows the user to enjoy an event. For example, it selects a route that includes an event that the user enjoyed. In this way, it is possible to propose an optimal route that takes seasonal event information into consideration. For example, the user can enjoy seasonal events.

[0039] The real-time data analysis unit can also apply route suggestions that take seasons and weather into consideration to outdoor activities. The real-time data analysis unit, for example, considers seasons and weather to suggest optimal hiking routes. For example, it selects mountain trails where flowers bloom in spring. The real-time data analysis unit can also analyze information about outdoor activities and suggest optimal routes. For example, it selects routes that include campsites and cycling courses. The real-time data analysis unit can also analyze the user's past movement history and suggest routes suitable for outdoor activities. For example, it re-suggests routes that include outdoor activities that the user enjoyed in the past. This makes it possible to suggest optimal routes for outdoor activities as well. For example, the user can enjoy outdoor activities according to the season and weather.

[0040] The real-time data analysis unit can also apply route suggestions that take seasons and weather into consideration to indoor activities. The real-time data analysis unit, for example, takes seasons and weather into consideration to suggest an optimal route for touring museums. For example, on a rainy day, it selects a route that visits multiple museums. The real-time data analysis unit can also analyze information about indoor activities and suggest optimal routes. For example, it selects a route that includes shopping malls and movie theaters. The real-time data analysis unit can also analyze the user's past movement history and suggest routes that are suitable for indoor activities. For example, it re-suggests routes that include indoor activities that the user enjoyed in the past. This makes it possible to suggest optimal routes for indoor activities as well. For example, the user can enjoy indoor activities depending on the season and weather.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The route suggestion unit can work in conjunction with the user preference analysis unit to suggest routes along specific themes based on the user's hobbies and interests. For example, for a user who loves history, a route that takes in historical sites and museums can be suggested. For a user who is interested in art, a route that includes galleries and art events can be suggested. Furthermore, for a user who loves food, a route that takes in local specialties and popular restaurants can be suggested. This allows users to enjoy special experiences that are tailored to their hobbies and interests.

[0043] The route suggestion unit can work in cooperation with the user preference analysis unit to suggest routes that can be enjoyed together with the user's pet. For example, for a user who has a dog, it can suggest routes that include parks and dog runs where the user can walk the dog. It can also suggest routes that include pet-friendly cafes and restaurants. It can also suggest routes that include events and activities that the user can participate in together with their pet. This allows the user to spend a fun time with their pet.

[0044] The route suggestion unit can also suggest routes that match the user's health goals, taking into account the user's health condition and fitness data. For example, it can suggest routes that burn a lot of calories to a user who is on a diet. It can also suggest routes that ensure a reasonable amount of exercise to a user who is undergoing rehabilitation. It can also suggest routes that allow users to enjoy relaxing scenery and nature to a user who is trying to relieve stress. This allows the user to select the optimal route that matches their health goals.

[0045] The route suggestion unit can work in conjunction with the user preference analysis unit to suggest routes that allow the user to discover new hobbies and interests. For example, it can suggest routes that include places and activities that the user has not yet visited. It can also suggest routes that include events and workshops that the user may be interested in. It can also suggest routes that include places where the user has the opportunity to make new friends. This allows the user to discover new hobbies and interests and spend their time fulfillingly.

[0046] The route suggestion unit can also work in conjunction with the user preference analysis unit to suggest routes that allow users to learn about culture and history. For example, it can suggest routes that take users around local historical sites and museums. It can also suggest routes that include cultural events and traditional festivals. It can also suggest routes that include local art galleries and theaters. This allows users to learn about culture and history while traveling and enjoy a richer experience.

[0047] The route suggestion unit can work in conjunction with the user preference analysis unit to suggest routes that allow the user to learn new skills. For example, it can suggest routes that include workshops and seminars that the user might be interested in. It can also suggest routes that include places where the user can try new sports or activities. It can also suggest routes that include places where the user can learn a new language or culture. This allows the user to learn new skills while traveling and promote self-development.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The user preference analysis unit analyzes the user's preferences. For example, it analyzes the user's past travel history and favorite places. It can also analyze the user's preferences from social media posts and photos. For example, it performs text analysis and image analysis of social media posts. Step 2: The route suggestion unit suggests routes based on the user's preferences analyzed by the preference analysis unit. For example, for a user who wants to enjoy scenery, routes that offer beautiful scenery in each season can be suggested. Also, for a user who wants to enjoy meals, routes with stations and restaurants where meals can be eaten can be suggested. For example, a route that includes restaurants that match the user's preferences can be selected. Step 3: The real-time data analysis unit analyzes real-time data such as season, weather, and congestion status to optimize routes. For example, it selects and suggests routes that are less crowded or have a higher chance of finding a seat. In addition, if the weather is bad, it can suggest routes that involve more indoor travel. For example, it can select routes that use underground passages on rainy days.

[0050] (Example 2) The AI ​​system according to the embodiment of the present invention not only suggests optimal departure times and routes based on the user's preferences, but also suggests routes with scenic views, stations where you can eat, lines where you can sit, lines where you can share a ride with friends, etc. In this way, the AI ​​system can make commuting and travelling not just a means of transportation, but also a part of enjoyment and new experiences.

[0051] The AI ​​system according to the embodiment includes a user preference analysis unit, a route suggestion unit, and a real-time data analysis unit. The user preference analysis unit analyzes the user's preferences. For example, it analyzes the user's past travel history and favorite places. The user preference analysis unit can also analyze the user's preferences from social media posts and photos. For example, it performs text analysis and image analysis of social media posts. The route suggestion unit suggests a route based on the preferences analyzed by the user preference analysis unit. For example, for a user who wants to enjoy scenery, it suggests routes with beautiful scenery for each season. For a user who wants to enjoy meals, the route suggestion unit can also suggest routes with stations and restaurants where meals are available. For example, it selects a route that includes restaurants that match the user's preferences. The real-time data analysis unit analyzes real-time data such as the season, weather, and congestion status to optimize the route. For example, it selects and suggests routes that are less crowded or have a higher chance of finding a seat. In addition, the real-time data analysis unit can also suggest routes that involve more indoor travel when the weather is bad. For example, it selects a route that uses underground passages on rainy days. As a result, the AI ​​system according to the embodiment can propose an optimal route based on the user's preferences and real-time data. For example, the user can travel comfortably without stress. The user can also enjoy new experiences.

[0052] The route suggestion unit can use the user's emotion estimation function to suggest a route that will elicit the most positive emotions while traveling. The route suggestion unit, for example, analyzes the user's facial expressions and voice in real time to suggest a route that will elicit positive emotions. For example, it selects a scenic route that will make the user smile. The route suggestion unit can also analyze the user's biometric data to suggest a route that will elicit positive emotions. For example, it can estimate emotions based on heart rate and electrodermal activity to select a relaxing route. The route suggestion unit can also learn the user's past emotion data to suggest a route that will elicit positive emotions. For example, it can re-suggest routes that the user enjoyed in the past. This makes it possible to suggest an optimal route based on the user's emotions. For example, the user can feel positive emotions while traveling.

[0053] The route suggestion unit can analyze the user's preferences from social media posts and photos as well as the user's past travel history, and propose more personalized routes. The route suggestion unit, for example, analyzes the user's social media posts to identify favorite places and activities. For example, it can propose routes that include cafes and parks that the user frequently posts about. The route suggestion unit can also analyze the user's photos to identify favorite scenery and places. For example, it can propose routes that include scenery that is frequently seen in photos taken by the user. The route suggestion unit can also analyze the user's past travel history to identify favorite routes. For example, it can re-suggest routes that the user has used many times in the past. This makes it possible to propose more personalized routes based on the user's social media data. For example, the user can enjoy routes that suit their preferences.

[0054] The route suggestion unit can suggest a healthy route by taking into account the user's health condition and fitness data. The route suggestion unit, for example, analyzes the user's fitness data and suggests a route that ensures an appropriate amount of exercise. For example, it selects a route that takes into account the number of steps and calories burned. The route suggestion unit can also analyze the user's health condition and suggest a healthy route. For example, it selects a route that ensures an appropriate amount of exercise based on the user's heart rate and blood pressure. The route suggestion unit can also suggest a route based on the user's health goals. For example, it suggests a route that burns a lot of calories to a user who is on a diet. This makes it possible to suggest a healthy route based on the user's health condition. For example, the user can maintain their health while traveling.

[0055] The route suggestion unit can propose routes based on the user's preferences not only for commuting but also for weekend leisure activities and shopping. The route suggestion unit, for example, analyzes the user's weekend leisure plans and proposes an optimal route. For example, it selects a route that includes tourist attractions and event venues that the user wants to visit. The route suggestion unit can also analyze the user's shopping plans and propose an optimal route. For example, it selects a route that includes shopping malls and stores that the user wants to visit. The route suggestion unit can also analyze the user's commuting route and apply it to weekend leisure activities and shopping. For example, it proposes a route that includes tourist attractions and stores on the commuting route. This makes it possible to propose optimal routes not only for commuting but also for weekend leisure activities and shopping. For example, the user can enjoy traveling on the weekend.

[0056] The route suggestion unit can extend route suggestions based on the user's preferences to other means of transportation, such as cars and bicycles. The route suggestion unit, for example, analyzes the user's car travel history and suggests an optimal driving route. For example, it selects a route that includes scenic driving routes and rest spots. The route suggestion unit can also analyze the user's bicycle travel history and suggest an optimal cycling route. For example, it selects a route that includes roads suitable for cycling and rest spots. The route suggestion unit can also be applied to other means of transportation based on the user's preferences. For example, it can suggest routes that involve walking or public transportation. This makes it possible to suggest optimal routes for other means of transportation, such as cars and bicycles. For example, the user can travel comfortably using various means of transportation.

[0057] The route suggestion unit can use the user's emotion estimation function to suggest a route for a group that also takes into account the preferences of family and friends. The route suggestion unit, for example, analyzes emotion data of the user and their family and friends to suggest a route that everyone can enjoy. For example, it selects a route that includes tourist spots that will make everyone smile. The route suggestion unit can also analyze the preferences of the user and their family and friends to suggest a route for a group. For example, it selects a route that includes restaurants and activities that everyone can enjoy. The route suggestion unit can also analyze the movement history of the user and their family and friends to suggest a route for a group. For example, it re-suggests a route that includes places that everyone has enjoyed in the past. This makes it possible to suggest a route for a group that also takes into account the preferences of family and friends. For example, the user can spend a fun time with their family and friends.

[0058] The real-time data analysis unit can use the user's emotion estimation function to suggest a route that reduces stress depending on the congestion situation. The real-time data analysis unit, for example, analyzes the user's emotion data in real time to suggest a route that is less stressful. For example, it selects uncrowded routes or quiet roads. The real-time data analysis unit can also learn the user's past emotion data to suggest a route that is less stressful. For example, it can re-suggest routes that the user found relaxing in the past. The real-time data analysis unit can also analyze the user's biometric data to suggest a route that is less stressful. For example, it can estimate stress based on heart rate and electrodermal activity and select a route that is relaxing. This makes it possible to suggest a route that reduces stress depending on the congestion situation. For example, the user can travel comfortably without stress.

[0059] The real-time data analysis unit can propose routes taking into consideration not only traffic congestion but also real-time traffic accident and construction information. The real-time data analysis unit, for example, analyzes real-time traffic accident information and proposes routes where no accidents have occurred. For example, it selects a route that avoids areas where accidents frequently occur. The real-time data analysis unit can also analyze real-time construction information and propose routes where no construction is taking place. For example, it selects a route that avoids roads under construction. The real-time data analysis unit can also analyze real-time traffic information and propose an optimal route. For example, it selects a route that avoids traffic congestion. This makes it possible to propose an optimal route taking into consideration traffic accident and construction information. For example, the user can travel safely and smoothly.

[0060] The real-time data analysis unit can also apply route suggestions that take congestion into account when congestion is expected during specific time periods, such as at events or concerts. The real-time data analysis unit, for example, analyzes information about events or concerts and suggests routes that avoid times when congestion is expected. For example, it selects a detour route that avoids congestion around the event venue. The real-time data analysis unit can also be applied when congestion is expected during specific time periods. For example, it suggests a route that avoids congestion around tourist spots during rush hour or on holidays. The real-time data analysis unit can also analyze the user's past travel history and apply it when congestion is expected during specific time periods. For example, it re-suggests routes that have avoided congestion in the past. This makes it possible to suggest optimal routes even when congestion is expected during specific time periods, such as at events or concerts. For example, the user can travel comfortably by avoiding congestion.

[0061] The real-time data analysis unit can apply route suggestions that take congestion into account not only to public transportation but also to parking congestion. The real-time data analysis unit, for example, analyzes the real-time congestion status of parking lots and suggests routes that include vacant parking lots. For example, it may preferentially select vacant parking lots near the destination. The real-time data analysis unit can also analyze the congestion status of public transportation and suggest optimal routes. For example, it may select routes that are not congested or routes where there is a high chance of finding a seat. The real-time data analysis unit can also analyze the congestion status of parking lots and suggest optimal routes. For example, if a parking lot is full, it may suggest another parking lot. This makes it possible to suggest optimal routes that take congestion status of parking lots into consideration. For example, the user can travel comfortably by avoiding crowded parking lots.

[0062] The real-time data analysis unit can use the user's emotion estimation function to suggest a route that not only avoids crowds but also allows for relaxation. The real-time data analysis unit, for example, analyzes the user's emotion data and suggests a relaxing route. For example, it selects a route that includes a quiet park or a riverside road. The real-time data analysis unit can also analyze the user's biometric data and suggest a relaxing route. For example, it selects a relaxing route based on heart rate or electrodermal activity. The real-time data analysis unit can also learn the user's past emotion data and suggest a relaxing route. For example, it re-suggests routes that the user found relaxing in the past. This makes it possible to suggest a relaxing route as well as avoid crowds. For example, the user can relax while traveling.

[0063] The real-time data analysis unit can suggest routes that elicit the most positive emotions depending on the season and weather. The real-time data analysis unit, for example, analyzes the user's emotional data and suggests routes that elicit positive emotions depending on the season and weather. For example, on a sunny day, it selects a route with beautiful scenery. The real-time data analysis unit can also analyze seasonal event information and suggest routes that elicit positive emotions. For example, it selects a route where cherry blossoms can be seen in spring. The real-time data analysis unit can also learn the user's past emotional data and suggest routes that elicit positive emotions depending on the season and weather. For example, it re-suggests routes that the user enjoyed in the past. This makes it possible to suggest routes that elicit positive emotions depending on the season and weather. For example, the user can feel positive emotions depending on the season and weather.

[0064] The real-time data analysis unit can propose routes taking into consideration not only the season and weather, but also the user's clothing and belongings. The real-time data analysis unit, for example, analyzes the user's clothing data and proposes an appropriate route. For example, if the user does not have an umbrella, an indoor route is selected. The real-time data analysis unit can also analyze the user's belongings data and propose an appropriate route. For example, if the user has a camera, a route including photo spots is selected. The real-time data analysis unit can also analyze the user's past movement history and propose a route based on the user's clothing and belongings. For example, it can re-propose routes that the user has traveled comfortably in the past. This makes it possible to propose an optimal route taking into consideration the user's clothing and belongings. For example, the user can travel comfortably based on their clothing and belongings.

[0065] The real-time data analysis unit can take seasonal event information into consideration and propose a route that allows the user to enjoy an event. The real-time data analysis unit, for example, analyzes seasonal event information and proposes an optimal route. For example, it selects a route that includes a location where a fireworks display is being held. The real-time data analysis unit can also analyze the user's past movement history and propose a route that allows the user to enjoy an event. For example, it re-proposes a route that includes an event that the user enjoyed in the past. The real-time data analysis unit can also analyze the user's emotional data and propose a route that allows the user to enjoy an event. For example, it selects a route that includes an event that the user enjoyed. In this way, it is possible to propose an optimal route that takes seasonal event information into consideration. For example, the user can enjoy seasonal events.

[0066] The real-time data analysis unit can also apply route suggestions that take seasons and weather into consideration to outdoor activities. The real-time data analysis unit, for example, considers seasons and weather to suggest optimal hiking routes. For example, it selects mountain trails where flowers bloom in spring. The real-time data analysis unit can also analyze information about outdoor activities and suggest optimal routes. For example, it selects routes that include campsites and cycling courses. The real-time data analysis unit can also analyze the user's past movement history and suggest routes suitable for outdoor activities. For example, it re-suggests routes that include outdoor activities that the user enjoyed in the past. This makes it possible to suggest optimal routes for outdoor activities as well. For example, the user can enjoy outdoor activities according to the season and weather.

[0067] The real-time data analysis unit can also apply route suggestions that take seasons and weather into consideration to indoor activities. The real-time data analysis unit, for example, takes seasons and weather into consideration to suggest an optimal route for touring museums. For example, on a rainy day, it selects a route that visits multiple museums. The real-time data analysis unit can also analyze information about indoor activities and suggest optimal routes. For example, it selects a route that includes shopping malls and movie theaters. The real-time data analysis unit can also analyze the user's past movement history and suggest routes that are suitable for indoor activities. For example, it re-suggests routes that include indoor activities that the user enjoyed in the past. This makes it possible to suggest optimal routes for indoor activities as well. For example, the user can enjoy indoor activities depending on the season and weather.

[0068] The real-time data analysis unit can use the user's emotion estimation function to suggest the optimal means of transportation depending on the season and weather. The real-time data analysis unit, for example, analyzes the user's emotion data and suggests the optimal means of transportation depending on the season and weather. For example, on sunny days, it may recommend a bicycle. The real-time data analysis unit can also analyze season and weather data and suggest the optimal means of transportation depending on the season and weather. For example, it may recommend public transportation depending on rainy days. The real-time data analysis unit can also analyze the user's past travel history and suggest the optimal means of transportation depending on the season and weather. For example, it may re-suggest means of transportation that the user found comfortable in the past. This makes it possible to suggest the optimal means of transportation depending on the season and weather. For example, the user can select the optimal means of transportation depending on the season and weather.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The route suggestion unit can work in conjunction with the user preference analysis unit to suggest routes along specific themes based on the user's hobbies and interests. For example, for a user who loves history, a route that takes in historical sites and museums can be suggested. For a user who is interested in art, a route that includes galleries and art events can be suggested. Furthermore, for a user who loves food, a route that takes in local specialties and popular restaurants can be suggested. This allows users to enjoy special experiences that are tailored to their hobbies and interests.

[0071] The route suggestion unit can also use the user emotion estimation function to suggest music or podcasts that will help the user relax. For example, if the user is feeling stressed, relaxing music can be suggested. If the user wants to concentrate, music or podcasts that will help improve concentration can be suggested. Furthermore, if the user wants to have fun, fun music or entertaining podcasts can be suggested. This allows the user to enjoy music or podcasts that match their emotions while traveling.

[0072] The route suggestion unit can work in cooperation with the user preference analysis unit to suggest routes that can be enjoyed together with the user's pet. For example, for a user who has a dog, it can suggest routes that include parks and dog runs where the user can walk the dog. It can also suggest routes that include pet-friendly cafes and restaurants. It can also suggest routes that include events and activities that the user can participate in together with their pet. This allows the user to spend a fun time with their pet.

[0073] The route suggestion unit can also suggest routes that match the user's health goals, taking into account the user's health condition and fitness data. For example, it can suggest routes that burn a lot of calories to a user who is on a diet. It can also suggest routes that ensure a reasonable amount of exercise to a user who is undergoing rehabilitation. It can also suggest routes that allow users to enjoy relaxing scenery and nature to a user who is trying to relieve stress. This allows the user to select the optimal route that matches their health goals.

[0074] The route suggestion unit can also use the user's emotion estimation function to suggest a route that avoids places where the user feels a particular emotion. For example, it can suggest a route that avoids places where the user has had unpleasant experiences in the past. It can also suggest a route that avoids places where the user feels stressed. It can also suggest a route that avoids places where the user has sad memories. This allows the user to avoid unpleasant emotions while traveling and travel comfortably.

[0075] The route suggestion unit can work in conjunction with the user preference analysis unit to suggest routes that allow the user to discover new hobbies and interests. For example, it can suggest routes that include places and activities that the user has not yet visited. It can also suggest routes that include events and workshops that the user may be interested in. It can also suggest routes that include places where the user has the opportunity to make new friends. This allows the user to discover new hobbies and interests and spend their time fulfillingly.

[0076] The route suggestion unit can also use the user's emotion estimation function to suggest a route that avoids music or podcasts that evoke specific emotions in the user. For example, the route suggestion unit can suggest a route that avoids music or podcasts that have previously made the user feel unpleasant. The route suggestion unit can also suggest a route that avoids music or podcasts that make the user feel stressed. Furthermore, the route suggestion unit can suggest a route that avoids music or podcasts that evoke sad memories in the user. This allows the user to avoid unpleasant emotions while traveling and travel comfortably.

[0077] The route suggestion unit can also work in conjunction with the user preference analysis unit to suggest routes that allow users to learn about culture and history. For example, it can suggest routes that take users around local historical sites and museums. It can also suggest routes that include cultural events and traditional festivals. It can also suggest routes that include local art galleries and theaters. This allows users to learn about culture and history while traveling and enjoy a richer experience.

[0078] The route suggestion unit can also use the user's emotion estimation function to suggest a route that avoids places where the user feels a particular emotion. For example, it can suggest a route that avoids places where the user has had unpleasant experiences in the past. It can also suggest a route that avoids places where the user feels stressed. It can also suggest a route that avoids places where the user has sad memories. This allows the user to avoid unpleasant emotions while traveling and travel comfortably.

[0079] The route suggestion unit can work in conjunction with the user preference analysis unit to suggest routes that allow the user to learn new skills. For example, it can suggest routes that include workshops and seminars that the user might be interested in. It can also suggest routes that include places where the user can try new sports or activities. It can also suggest routes that include places where the user can learn a new language or culture. This allows the user to learn new skills while traveling and promote self-development.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The user preference analysis unit analyzes the user's preferences. For example, it analyzes the user's past travel history and favorite places. It can also analyze the user's preferences from social media posts and photos. For example, it performs text analysis and image analysis of social media posts. Step 2: The route suggestion unit suggests routes based on the user's preferences analyzed by the preference analysis unit. For example, for a user who wants to enjoy scenery, routes that offer beautiful scenery in each season can be suggested. Also, for a user who wants to enjoy meals, routes with stations and restaurants where meals can be eaten can be suggested. For example, a route that includes restaurants that match the user's preferences can be selected. Step 3: The real-time data analysis unit analyzes real-time data such as season, weather, and congestion status to optimize routes. For example, it selects and suggests routes that are less crowded or have a higher chance of finding a seat. In addition, if the weather is bad, it can suggest routes that involve more indoor travel. For example, it can select routes that use underground passages on rainy days.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a preference analysis unit that analyzes user preferences; a route suggestion unit that suggests a route based on the preferences analyzed by the preference analysis unit; A real-time data analysis unit that analyzes real-time data on seasons, weather, and congestion status to optimize routes. A system characterized by:

2. The route suggestion unit Suggesting a route that elicits the most positive emotions during the user's journey.

2. The system of claim 1.

3. The route suggestion unit Route suggestions based on the user's preferences are applied not only to commuting but also to weekend leisure or shopping trips.

2. The system of claim 1.

4. The real-time data analysis unit and suggesting a route that reduces stress depending on the congestion situation.

2. The system of claim 1.

5. The real-time data analysis unit Proposing a route that elicits positive emotions depending on the season or weather 2. The system of claim 1.

6. The route suggestion unit Analyzing the user's preferences from their social media posts or photos, as well as their past travel history, to suggest more personalized routes 2. The system of claim 1.

7. The route suggestion unit Extending route suggestions based on the user's preferences to other modes of transportation, such as cars or bicycles.

2. The system of claim 1.

8. The real-time data analysis unit Learning the user's past congestion avoidance history and proposing more accurate congestion avoidance routes 2. The system of claim 1.

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