system

Abstract

The system according to the embodiment aims to propose an optimal route according to the user's situation and needs. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, an estimation unit, and a selection unit. The acquisition unit acquires the user's situation and requests. The analysis unit analyzes the information acquired by the acquisition unit. The estimation unit estimates a route based on the information analyzed by the analysis unit. The selection unit selects an agent with a specific character based on the route estimated by the estimation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies are unable to propose optimal routes that meet the individual needs of users, which may cause inconvenience to users.

[0005] The system according to the embodiment aims to propose an optimal route according to the user's situation and needs. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, an estimation unit, and a selection unit. The acquisition unit acquires the user's situation and requests. The analysis unit analyzes the information acquired by the acquisition unit. The estimation unit estimates a route based on the information analyzed by the analysis unit. The selection unit selects an agent with a specific character based on the route estimated by the estimation unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal route according to the user's situation and needs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The navigation system according to an embodiment of the present invention is a novel navigation system that provides optimal routes tailored to the user's circumstances and needs. This navigation system utilizes analysis by generative AI and a geographic information database to estimate and propose routes suitable for each individual user. First, the user accesses the navigation system and inputs their departure and destination locations. Next, the generative AI analyzes the user's circumstances and needs and predicts the optimal route. For example, it can suggest routes that put less strain on the lower back for seniors, routes that efficiently tour tourist spots for tourists, and optimal routes that take vehicle characteristics into account for transportation businesses. Furthermore, when proposing routes, agents with personalities tailored to the user's interests and preferences provide guidance. For example, anime characters or animal characters can provide a sense of familiarity to users. This agent uses generative AI to analyze the user's interests and preferences and select the most appropriate character. This mechanism solves the challenges of conventional navigation systems and is expected to improve operational efficiency and reduce costs. For example, in the transportation industry, optimal route suggestions reduce fuel consumption and reduce environmental impact. It also enables more comfortable and efficient travel for seniors and tourists. In the future, the goal is to popularize optimal route guidance tailored to the user's needs and circumstances, and establish a new de facto standard for navigation systems, which will enable navigation systems to provide the optimal route tailored to the user's circumstances and requests.

[0029] A navigation system according to an embodiment includes an acquisition unit, an analysis unit, an estimation unit, and a selection unit. The acquisition unit acquires a user's situation and requests. For example, the acquisition unit can accept input of the user's departure point and destination. The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit analyzes the user's situation and requests using a generation AI. The generation AI can analyze the user's input information using technologies such as natural language processing, image recognition, and machine learning algorithms. The estimation unit estimates a route based on the information analyzed by the analysis unit. For example, the estimation unit estimates an optimal route using the generation AI. The optimal route is estimated based on criteria such as travel time, distance, and traffic conditions. The selection unit selects an agent with a specific character based on the route estimated by the estimation unit. For example, the selection unit analyzes the user's hobbies and preferences using the generation AI and selects an optimal character. This allows the navigation system to provide an optimal route tailored to the user's situation and requests.

[0030] The acquisition unit can accept input of a departure point and a destination from a user. The acquisition unit, for example, provides an interface for the user to input the departure point and the destination. For example, the acquisition unit can provide a text box or a drop-down menu for the user to input an address, a place name, coordinates, etc. The acquisition unit can also allow the user to input the departure point and the destination using voice input or a touch screen. This allows the user to input the departure point and the destination.

[0031] The analysis unit can analyze the user's situation and requests using the generative AI. The analysis unit, for example, analyzes the user's situation and requests using the generative AI. The generative AI can analyze the user's input information using technologies such as natural language processing, image recognition, and machine learning algorithms. For example, the analysis unit analyzes the departure and destination information entered by the user to understand the user's situation, such as their means of transportation and time constraints. The analysis unit can also analyze the user's past travel history and behavioral patterns to estimate the user's requests. As a result, the generative AI can be used to accurately analyze the user's situation and requests.

[0032] The estimation unit can estimate a route using a generation AI. The estimation unit, for example, estimates an optimal route using a generation AI. The generation AI can analyze user input information and estimate an optimal route using technologies such as natural language processing, image recognition, and machine learning algorithms. For example, the estimation unit estimates an optimal route based on criteria such as travel time, distance, and traffic conditions, based on the user's departure and destination information. The estimation unit can also estimate a route that takes specific conditions into account depending on the user's situation and requests. For example, it can estimate a route that puts less strain on the lower back for the elderly, a route that efficiently tours tourist spots for tourists, and an optimal route that takes vehicle characteristics into account for the transportation industry. As a result, the generation AI can be used to accurately estimate optimal routes.

[0033] The selection unit can use the generation AI to analyze the user's hobbies and preferences and select a character. For example, the selection unit can use the generation AI to analyze the user's hobbies and preferences and select the optimal character. The generation AI can analyze the user's input information and understand the user's hobbies and preferences using technologies such as natural language processing, image recognition, and machine learning algorithms. For example, the selection unit can analyze the user's past behavioral history and survey results to estimate the user's hobbies and preferences. The selection unit can also analyze the user's social media activity and online shopping history to understand the user's hobbies and preferences. As a result, the generation AI can be used to select the optimal character based on the user's hobbies and preferences.

[0034] The estimation unit can estimate a route that reduces the burden on the lower back for elderly people. For example, the estimation unit estimates a route that reduces the burden on the lower back for elderly people. A route that reduces the burden on the lower back is estimated based on criteria such as a route with few steps or a route with many rest points. For example, the estimation unit references a geographic information database to estimate a route with few steps. The estimation unit can also estimate a route with many rest points. This makes it possible to provide a route that is suitable for elderly people.

[0035] The estimation unit can estimate a route for tourists to efficiently tour tourist spots. For example, the estimation unit estimates a route for tourists to efficiently tour tourist spots. A route for efficiently touring tourist spots is estimated based on criteria such as the popularity of the tourist spots, travel time, and distance. For example, the estimation unit estimates a route based on the popularity of the tourist spots. The estimation unit can also estimate a route taking travel time and distance into consideration. This makes it possible to provide a route that is suitable for tourists.

[0036] The estimation unit can estimate an optimal route for the transportation business taking into account vehicle characteristics. For example, the estimation unit estimates an optimal route for the transportation business taking into account vehicle characteristics. Vehicle characteristics are considered based on criteria such as vehicle size, weight, and fuel efficiency. For example, the estimation unit estimates a route based on the size of the vehicle. The estimation unit can also estimate a route taking into account vehicle weight and fuel efficiency. This makes it possible to provide a route suitable for the transportation business.

[0037] The acquisition unit can analyze the user's past travel history and select the optimal input method for the departure point and destination. The acquisition unit, for example, analyzes the user's past travel history and selects the optimal input method for the departure point and destination. The past travel history is analyzed based on data such as GPS data and traffic history. For example, the acquisition unit automatically displays places that the user has frequently visited in the past as candidates. The acquisition unit can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. This improves input efficiency by selecting the optimal input method based on the user's past travel history.

[0038] The acquisition unit can perform filtering based on the user's current health condition and mood when the departure point and destination are input. For example, the acquisition unit performs filtering based on the user's current health condition and mood when the departure point and destination are input. The health condition and mood are acquired based on self-reporting or data from a wearable device. For example, if the user is tired, the acquisition unit can preferentially display short-distance destinations. Furthermore, if the user is looking for healthy exercise, the acquisition unit can also suggest long-distance destinations. In this way, by performing filtering according to the user's health condition and mood, more appropriate destinations can be suggested.

[0039] The acquisition unit can prioritize inputting highly relevant locations in consideration of the user's geographical location information when inputting a departure point and a destination. For example, the acquisition unit prioritizes inputting highly relevant locations in consideration of the user's geographical location information when inputting a departure point and a destination. The geographical location information is acquired based on GPS data, map information, etc. For example, the acquisition unit prioritizes displaying locations close to the user's current location. Furthermore, if the user is in a specific area, the acquisition unit can also prioritize suggesting destinations within that area. This allows highly relevant locations to be prioritized by taking the user's geographical location information into consideration.

[0040] The acquisition unit can analyze the user's social media activity and input related locations when the departure point and destination are input. For example, the acquisition unit analyzes the user's social media activity and inputs related locations when the departure point and destination are input. Social media activity is analyzed based on data such as the content of posts, the number of likes, and the number of followers. For example, the acquisition unit suggests places where the user has checked in on social media as candidate locations. The acquisition unit can also preferentially display places in which the user has shown interest on social media. In this way, related locations can be input by analyzing the user's social media activity.

[0041] The analysis unit can optimize the analysis algorithm by referring to the user's past movement history during analysis. For example, the analysis unit optimizes the analysis algorithm by referring to the user's past movement history during analysis. The past movement history is referenced based on data such as GPS data and traffic history. For example, the analysis unit analyzes the optimal route based on routes the user has used in the past. The analysis unit can also analyze a route that avoids congestion based on the user's past movement history. This improves the accuracy of the analysis by optimizing the analysis algorithm based on the past movement history.

[0042] The analysis unit can improve the accuracy of the analysis based on the user's current living situation and health condition during analysis. For example, the analysis unit improves the accuracy of the analysis based on the user's current living situation and health condition during analysis. The living situation and health condition are acquired based on self-reporting or data from a wearable device. For example, if the user is tired, the analysis unit prioritizes the shortest route in the analysis. Furthermore, if the user is seeking healthy exercise, the analysis unit can also analyze a longer route. This improves the accuracy of the analysis based on the user's living situation and health condition, enabling more appropriate analysis.

[0043] The analysis unit can perform the analysis taking into account the user's geographical location information. For example, the analysis unit performs the analysis taking into account the user's geographical location information. The geographical location information is acquired based on GPS data, map information, etc. For example, the analysis unit prioritizes analyzing locations close to the user's current location. Furthermore, if the user is in a specific area, the analysis unit can also prioritize analyzing routes within that area. This allows for more appropriate analysis by taking into account the user's geographical location information.

[0044] The analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's social media activity during analysis. Social media activity is referenced based on data such as the content of posts, the number of likes, and the number of followers. For example, the analysis unit performs analysis by referring to the places where the user checked in on social media. The analysis unit can also prioritize analysis of places in which the user showed interest on social media. In this way, the accuracy of the analysis is improved by referring to the user's social media activity.

[0045] The estimation unit can optimize the estimation algorithm by referring to the user's past movement history during estimation. For example, the estimation unit optimizes the estimation algorithm by referring to the user's past movement history during estimation. The past movement history is referenced based on data such as GPS data and traffic history. For example, the estimation unit estimates an optimal route based on routes the user has used in the past. The estimation unit can also estimate a route that avoids congestion based on the user's past movement history. This improves the accuracy of estimation by optimizing the estimation algorithm based on the past movement history.

[0046] The estimation unit can improve the accuracy of the estimation based on the user's current health condition and mood at the time of estimation. For example, the estimation unit improves the accuracy of the estimation based on the user's current health condition and mood at the time of estimation. The health condition and mood are acquired based on self-reporting or data from a wearable device. For example, if the user is tired, the estimation unit prioritizes estimating the shortest route. Furthermore, if the user is seeking healthy exercise, the estimation unit can also estimate a longer route. In this way, by improving the accuracy of the estimation based on the user's health condition and mood, a more appropriate route can be estimated.

[0047] The estimation unit can estimate the optimal route by taking into account the user's geographical location information during estimation. For example, the estimation unit estimates the optimal route by taking into account the user's geographical location information during estimation. The geographical location information is acquired based on GPS data, map information, etc. For example, the estimation unit prioritizes estimating a location close to the user's current location. Furthermore, if the user is in a specific area, the estimation unit can also prioritize estimating a route within that area. This makes it possible to estimate a more appropriate route by taking into account the user's geographical location information.

[0048] The estimation unit can improve the accuracy of the estimation by referring to the user's social media activity during estimation. For example, the estimation unit improves the accuracy of the estimation by referring to the user's social media activity during estimation. Social media activity is referenced based on data such as the content of posts, the number of likes, and the number of followers. For example, the estimation unit makes an estimation by referring to places where the user checked in on social media. The estimation unit can also prioritize estimation of places in which the user showed interest on social media. In this way, the accuracy of the estimation is improved by referring to the user's social media activity.

[0049] The selection unit can optimize the selection algorithm by referring to the user's past hobbies and preferences during selection. For example, the selection unit optimizes the selection algorithm by referring to the user's past hobbies and preferences during selection. Past hobbies and preferences are referenced based on data such as past behavioral history and survey results. For example, the selection unit selects the most suitable character based on characters previously selected by the user. The selection unit can also select related characters based on the user's past hobbies and preferences. This improves the accuracy of selection by optimizing the selection algorithm based on the user's past hobbies and preferences.

[0050] The selection unit may select a character based on the user's current mood and health condition at the time of selection. For example, the selection unit may select a character based on the user's current mood and health condition at the time of selection. The mood and health condition are acquired based on self-reporting or data from a wearable device. For example, if the user is tired, the selection unit may select a soothing character. Furthermore, if the user desires healthy exercise, the selection unit may select an active character. In this way, by selecting a character based on the user's mood and health condition, a more appropriate character can be selected.

[0051] The selection unit can select the most appropriate character by taking into consideration the user's geographical location information. For example, the selection unit selects the most appropriate character by taking into consideration the user's geographical location information. The geographical location information is acquired based on GPS data, map information, etc. For example, if the user is in a tourist spot, the selection unit selects a character related to that area. Furthermore, if the user is in a specific area, the selection unit can also select a character related to that area. In this way, by taking into consideration the user's geographical location information, a more appropriate character can be selected.

[0052] The selection unit may select a character by referring to the user's social media activity. For example, the selection unit may select a character by referring to the user's social media activity. The social media activity is referenced based on data such as the content of posts, the number of likes, and the number of followers. For example, the selection unit may preferentially select characters in which the user has shown interest on social media. The selection unit may also refer to characters used by the user's friends when selecting a character. In this way, by referring to the user's social media activity, a more appropriate character can be selected.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The navigation system may further include a traffic information acquisition unit that acquires real-time traffic information. The traffic information acquisition unit acquires, for example, traffic congestion and accident information in real time and provides it to the analysis unit. The analysis unit can re-analyze the optimal route based on the user's situation and requests based on the acquired traffic information. This allows the user to use the optimal route based on the latest traffic conditions. For example, if traffic congestion occurs, the analysis unit can suggest a detour route. Furthermore, if accident information is acquired, it can also preferentially suggest a safe route. This allows the user to travel more safely and efficiently.

[0055] The navigation system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit acquires data such as heart rate and blood pressure from a wearable device and provides the data to the analysis unit. The analysis unit can suggest an optimal route based on the acquired health data, taking into account the user's health condition. For example, if the user's heart rate is high, the analysis unit can suggest a relaxing route. Also, if the user's blood pressure is high, the analysis unit can suggest a route with many rest stops. This allows the user to travel optimally based on their health condition.

[0056] The navigation system may further include a history analysis unit that analyzes the user's past travel history and proposes an optimal route. The history analysis unit may propose an optimal route based on routes and places the user has visited in the past. For example, the history analysis unit may preferentially propose routes that the user has frequently used in the past. The navigation system may also propose new tourist routes based on tourist spots the user has visited in the past. This allows the user to use the optimal route based on their past travel history.

[0057] The navigation system can also adjust the frequency of route guidance based on the user's current mood and health condition. For example, if the user is tired, the frequency of guidance can be reduced to allow the user to relax. Alternatively, if the user is in a hurry, the frequency of guidance can be increased to allow the user to reach their destination quickly. This provides an appropriate guidance frequency according to the user's mood and health condition, reducing stress during travel. Furthermore, if the user is seeking healthy exercise, the frequency of guidance can be adjusted to ensure an appropriate amount of exercise. This allows the user to travel more comfortably.

[0058] The navigation system may further include a social media analysis unit that analyzes the user's social media activity and suggests an optimal route. The social media analysis unit may suggest an optimal route based on, for example, places where the user has checked in or shown interest on social media. For example, a new sightseeing route may be suggested based on tourist spots where the user has checked in on social media. The optimal route may also be suggested based on events or places where the user has shown interest on social media. This allows the user to use an optimal route based on their social media activity.

[0059] The navigation system can further improve the accuracy of route guidance by taking into account the user's geographical location information. For example, it can prioritize guidance to places close to the user's current location. Also, if the user is in a specific area, it can prioritize guidance to routes within that area. This allows more appropriate route guidance to be provided by taking into account the user's geographical location information. Furthermore, if the user is in a tourist destination, it can also guide the user to tourist routes related to that area. This allows the user to use the optimal route based on their geographical location information.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The acquisition unit acquires the user's situation and requests. For example, the acquisition unit can accept input of the user's departure point and destination. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit uses the generation AI to analyze the user's situation and requests. The generation AI can analyze the user's input information using technologies such as natural language processing, image recognition, and machine learning algorithms. Step 3: The estimation unit estimates a route based on the information analyzed by the analysis unit. For example, the estimation unit estimates an optimal route using a generation AI. The optimal route is estimated based on criteria such as travel time, distance, and traffic conditions. Step 4: The selection unit selects an agent with a specific character based on the route estimated by the estimation unit. For example, the selection unit may use a generation AI to analyze the user's hobbies and preferences and select the most suitable character.

[0062] (Example 2) The navigation system according to an embodiment of the present invention is a novel navigation system that provides optimal routes tailored to the user's circumstances and needs. This navigation system utilizes analysis by generative AI and a geographic information database to estimate and propose routes suitable for each individual user. First, the user accesses the navigation system and inputs their departure and destination locations. Next, the generative AI analyzes the user's circumstances and needs and predicts the optimal route. For example, it can suggest routes that put less strain on the lower back for seniors, routes that efficiently tour tourist spots for tourists, and optimal routes that take vehicle characteristics into account for transportation businesses. Furthermore, when proposing routes, agents with personalities tailored to the user's interests and preferences provide guidance. For example, anime characters or animal characters can provide a sense of familiarity to users. This agent uses generative AI to analyze the user's interests and preferences and select the most appropriate character. This mechanism solves the challenges of conventional navigation systems and is expected to improve operational efficiency and reduce costs. For example, in the transportation industry, optimal route suggestions reduce fuel consumption and reduce environmental impact. It also enables more comfortable and efficient travel for seniors and tourists. In the future, the goal is to popularize optimal route guidance tailored to the user's needs and circumstances, and establish a new de facto standard for navigation systems, which will enable navigation systems to provide the optimal route tailored to the user's circumstances and requests.

[0063] A navigation system according to an embodiment includes an acquisition unit, an analysis unit, an estimation unit, and a selection unit. The acquisition unit acquires a user's situation and requests. For example, the acquisition unit can accept input of the user's departure point and destination. The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit analyzes the user's situation and requests using a generation AI. The generation AI can analyze the user's input information using technologies such as natural language processing, image recognition, and machine learning algorithms. The estimation unit estimates a route based on the information analyzed by the analysis unit. For example, the estimation unit estimates an optimal route using the generation AI. The optimal route is estimated based on criteria such as travel time, distance, and traffic conditions. The selection unit selects an agent with a specific character based on the route estimated by the estimation unit. For example, the selection unit analyzes the user's hobbies and preferences using the generation AI and selects an optimal character. This allows the navigation system to provide an optimal route tailored to the user's situation and requests.

[0064] The acquisition unit can accept input of a departure point and a destination from a user. The acquisition unit, for example, provides an interface for the user to input the departure point and the destination. For example, the acquisition unit can provide a text box or a drop-down menu for the user to input an address, a place name, coordinates, etc. The acquisition unit can also allow the user to input the departure point and the destination using voice input or a touch screen. This allows the user to input the departure point and the destination.

[0065] The analysis unit can analyze the user's situation and requests using the generative AI. The analysis unit, for example, analyzes the user's situation and requests using the generative AI. The generative AI can analyze the user's input information using technologies such as natural language processing, image recognition, and machine learning algorithms. For example, the analysis unit analyzes the departure and destination information entered by the user to understand the user's situation, such as their means of transportation and time constraints. The analysis unit can also analyze the user's past travel history and behavioral patterns to estimate the user's requests. As a result, the generative AI can be used to accurately analyze the user's situation and requests.

[0066] The estimation unit can estimate a route using a generation AI. The estimation unit, for example, estimates an optimal route using a generation AI. The generation AI can analyze user input information and estimate an optimal route using technologies such as natural language processing, image recognition, and machine learning algorithms. For example, the estimation unit estimates an optimal route based on criteria such as travel time, distance, and traffic conditions, based on the user's departure and destination information. The estimation unit can also estimate a route that takes specific conditions into account depending on the user's situation and requests. For example, it can estimate a route that puts less strain on the lower back for the elderly, a route that efficiently tours tourist spots for tourists, and an optimal route that takes vehicle characteristics into account for the transportation industry. As a result, the generation AI can be used to accurately estimate optimal routes.

[0067] The selection unit can use the generation AI to analyze the user's hobbies and preferences and select a character. For example, the selection unit can use the generation AI to analyze the user's hobbies and preferences and select the optimal character. The generation AI can analyze the user's input information and understand the user's hobbies and preferences using technologies such as natural language processing, image recognition, and machine learning algorithms. For example, the selection unit can analyze the user's past behavioral history and survey results to estimate the user's hobbies and preferences. The selection unit can also analyze the user's social media activity and online shopping history to understand the user's hobbies and preferences. As a result, the generation AI can be used to select the optimal character based on the user's hobbies and preferences.

[0068] The estimation unit can estimate a route that reduces the burden on the lower back for elderly people. For example, the estimation unit estimates a route that reduces the burden on the lower back for elderly people. A route that reduces the burden on the lower back is estimated based on criteria such as a route with few steps or a route with many rest points. For example, the estimation unit references a geographic information database to estimate a route with few steps. The estimation unit can also estimate a route with many rest points. This makes it possible to provide a route that is suitable for elderly people.

[0069] The estimation unit can estimate a route for tourists to efficiently tour tourist spots. For example, the estimation unit estimates a route for tourists to efficiently tour tourist spots. A route for efficiently touring tourist spots is estimated based on criteria such as the popularity of the tourist spots, travel time, and distance. For example, the estimation unit estimates a route based on the popularity of the tourist spots. The estimation unit can also estimate a route taking travel time and distance into consideration. This makes it possible to provide a route that is suitable for tourists.

[0070] The estimation unit can estimate an optimal route for the transportation business taking into account vehicle characteristics. For example, the estimation unit estimates an optimal route for the transportation business taking into account vehicle characteristics. Vehicle characteristics are considered based on criteria such as vehicle size, weight, and fuel efficiency. For example, the estimation unit estimates a route based on the size of the vehicle. The estimation unit can also estimate a route taking into account vehicle weight and fuel efficiency. This makes it possible to provide a route suitable for the transportation business.

[0071] The acquisition unit can estimate the user's emotions and adjust the timing of inputting the departure point and destination based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and adjusts the timing of inputting the departure point and destination based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and behavioral patterns. For example, if the user is feeling stressed, the acquisition unit can delay the input timing to relax the user. Furthermore, if the user is in a hurry, the acquisition unit can also speed up the input timing to encourage quick input. This allows for more appropriate input by adjusting the input timing according to the user's emotions.

[0072] The acquisition unit can analyze the user's past travel history and select the optimal input method for the departure point and destination. The acquisition unit, for example, analyzes the user's past travel history and selects the optimal input method for the departure point and destination. The past travel history is analyzed based on data such as GPS data and traffic history. For example, the acquisition unit automatically displays places that the user has frequently visited in the past as candidates. The acquisition unit can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. This improves input efficiency by selecting the optimal input method based on the user's past travel history.

[0073] The acquisition unit can perform filtering based on the user's current health condition and mood when the departure point and destination are input. For example, the acquisition unit performs filtering based on the user's current health condition and mood when the departure point and destination are input. The health condition and mood are acquired based on self-reporting or data from a wearable device. For example, if the user is tired, the acquisition unit can preferentially display short-distance destinations. Furthermore, if the user is looking for healthy exercise, the acquisition unit can also suggest long-distance destinations. In this way, by performing filtering according to the user's health condition and mood, more appropriate destinations can be suggested.

[0074] The acquisition unit can estimate the user's emotions and determine the priority of the departure points and destinations to be input based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and determines the priority of the departure points and destinations to be input based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and behavioral patterns. For example, if the user is feeling stressed, the acquisition unit can preferentially suggest places where the user can relax. Furthermore, if the user is in a hurry, the acquisition unit can preferentially suggest destinations with the shortest distance. In this way, by determining the priority according to the user's emotions, more appropriate destinations can be suggested.

[0075] The acquisition unit can prioritize inputting highly relevant locations in consideration of the user's geographical location information when inputting a departure point and a destination. For example, the acquisition unit prioritizes inputting highly relevant locations in consideration of the user's geographical location information when inputting a departure point and a destination. The geographical location information is acquired based on GPS data, map information, etc. For example, the acquisition unit prioritizes displaying locations close to the user's current location. Furthermore, if the user is in a specific area, the acquisition unit can also prioritize suggesting destinations within that area. This allows highly relevant locations to be prioritized by taking the user's geographical location information into consideration.

[0076] The acquisition unit can analyze the user's social media activity and input related locations when the departure point and destination are input. For example, the acquisition unit analyzes the user's social media activity and inputs related locations when the departure point and destination are input. Social media activity is analyzed based on data such as the content of posts, the number of likes, and the number of followers. For example, the acquisition unit suggests places where the user has checked in on social media as candidate locations. The acquisition unit can also preferentially display places in which the user has shown interest on social media. In this way, related locations can be input by analyzing the user's social media activity.

[0077] The analysis unit can estimate the user's emotions and adjust the analysis method for the situation and requests based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the analysis method for the situation and requests based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and behavioral patterns. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing a relaxing route. Also, if the user is in a hurry, the analysis unit can prioritize analyzing the shortest route. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions.

[0078] The analysis unit can optimize the analysis algorithm by referring to the user's past movement history during analysis. For example, the analysis unit optimizes the analysis algorithm by referring to the user's past movement history during analysis. The past movement history is referenced based on data such as GPS data and traffic history. For example, the analysis unit analyzes the optimal route based on routes the user has used in the past. The analysis unit can also analyze a route that avoids congestion based on the user's past movement history. This improves the accuracy of the analysis by optimizing the analysis algorithm based on the past movement history.

[0079] The analysis unit can improve the accuracy of the analysis based on the user's current living situation and health condition during analysis. For example, the analysis unit improves the accuracy of the analysis based on the user's current living situation and health condition during analysis. The living situation and health condition are acquired based on self-reporting or data from a wearable device. For example, if the user is tired, the analysis unit prioritizes the shortest route in the analysis. Furthermore, if the user is seeking healthy exercise, the analysis unit can also analyze a longer route. This improves the accuracy of the analysis based on the user's living situation and health condition, enabling more appropriate analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and behavioral patterns. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. This allows for more appropriate display by adjusting the display method according to the user's emotions.

[0081] The analysis unit can perform the analysis taking into account the user's geographical location information. For example, the analysis unit performs the analysis taking into account the user's geographical location information. The geographical location information is acquired based on GPS data, map information, etc. For example, the analysis unit prioritizes analyzing locations close to the user's current location. Furthermore, if the user is in a specific area, the analysis unit can also prioritize analyzing routes within that area. This allows for more appropriate analysis by taking into account the user's geographical location information.

[0082] The analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's social media activity during analysis. Social media activity is referenced based on data such as the content of posts, the number of likes, and the number of followers. For example, the analysis unit performs analysis by referring to the places where the user checked in on social media. The analysis unit can also prioritize analysis of places in which the user showed interest on social media. In this way, the accuracy of the analysis is improved by referring to the user's social media activity.

[0083] The estimation unit can estimate the user's emotions and adjust the estimation method for the optimal route based on the estimated user emotions. The estimation unit, for example, estimates the user's emotions and adjusts the estimation method for the optimal route based on the estimated user emotions. Emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and behavioral patterns. For example, if the user is feeling stressed, the estimation unit can prioritize estimating a relaxing route. Furthermore, if the user is in a hurry, the estimation unit can also prioritize estimating the shortest route. In this way, by adjusting the estimation method according to the user's emotions, a more appropriate route can be estimated.

[0084] The estimation unit can optimize the estimation algorithm by referring to the user's past movement history during estimation. For example, the estimation unit optimizes the estimation algorithm by referring to the user's past movement history during estimation. The past movement history is referenced based on data such as GPS data and traffic history. For example, the estimation unit estimates an optimal route based on routes the user has used in the past. The estimation unit can also estimate a route that avoids congestion based on the user's past movement history. This improves the accuracy of estimation by optimizing the estimation algorithm based on the past movement history.

[0085] The estimation unit can improve the accuracy of the estimation based on the user's current health condition and mood at the time of estimation. For example, the estimation unit improves the accuracy of the estimation based on the user's current health condition and mood at the time of estimation. The health condition and mood are acquired based on self-reporting or data from a wearable device. For example, if the user is tired, the estimation unit prioritizes estimating the shortest route. Furthermore, if the user is seeking healthy exercise, the estimation unit can also estimate a longer route. In this way, by improving the accuracy of the estimation based on the user's health condition and mood, a more appropriate route can be estimated.

[0086] The estimation unit can estimate the user's emotions and determine the priority of routes to be estimated based on the estimated user emotions. The estimation unit, for example, estimates the user's emotions and determines the priority of routes to be estimated based on the estimated user emotions. Emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and behavioral patterns. For example, if the user is feeling stressed, the estimation unit can prioritize a relaxing route. Furthermore, if the user is in a hurry, the estimation unit can also prioritize the shortest route. In this way, by prioritizing routes according to the user's emotions, a more appropriate route can be estimated.

[0087] The estimation unit can estimate the optimal route by taking into account the user's geographical location information during estimation. For example, the estimation unit estimates the optimal route by taking into account the user's geographical location information during estimation. The geographical location information is acquired based on GPS data, map information, etc. For example, the estimation unit prioritizes estimating a location close to the user's current location. Furthermore, if the user is in a specific area, the estimation unit can also prioritize estimating a route within that area. This makes it possible to estimate a more appropriate route by taking into account the user's geographical location information.

[0088] The estimation unit can improve the accuracy of the estimation by referring to the user's social media activity during estimation. For example, the estimation unit improves the accuracy of the estimation by referring to the user's social media activity during estimation. Social media activity is referenced based on data such as the content of posts, the number of likes, and the number of followers. For example, the estimation unit makes an estimation by referring to places where the user checked in on social media. The estimation unit can also prioritize estimation of places in which the user showed interest on social media. In this way, the accuracy of the estimation is improved by referring to the user's social media activity.

[0089] The selection unit can estimate the user's emotion and adjust the character selection method based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion and adjusts the character selection method based on the estimated user's emotion. Emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and behavioral patterns. For example, the selection unit selects a calm character when the user is relaxed. Also, the selection unit can select a lively character when the user is excited. In this way, by adjusting the character selection method according to the user's emotion, a more appropriate character can be selected.

[0090] The selection unit can optimize the selection algorithm by referring to the user's past hobbies and preferences during selection. For example, the selection unit optimizes the selection algorithm by referring to the user's past hobbies and preferences during selection. Past hobbies and preferences are referenced based on data such as past behavioral history and survey results. For example, the selection unit selects the most suitable character based on characters previously selected by the user. The selection unit can also select related characters based on the user's past hobbies and preferences. This improves the accuracy of selection by optimizing the selection algorithm based on the user's past hobbies and preferences.

[0091] The selection unit may select a character based on the user's current mood and health condition at the time of selection. For example, the selection unit may select a character based on the user's current mood and health condition at the time of selection. The mood and health condition are acquired based on self-reporting or data from a wearable device. For example, if the user is tired, the selection unit may select a soothing character. Furthermore, if the user desires healthy exercise, the selection unit may select an active character. In this way, by selecting a character based on the user's mood and health condition, a more appropriate character can be selected.

[0092] The selection unit can estimate the user's emotions and determine the priority of characters to be selected based on the estimated user emotions. The selection unit, for example, estimates the user's emotions and determines the priority of characters to be selected based on the estimated user emotions. Emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and behavioral patterns. For example, the selection unit can preferentially select a calm character when the user is relaxed. Also, the selection unit can preferentially select a lively character when the user is excited. In this way, by determining the priority of characters according to the user's emotions, a more appropriate character can be selected.

[0093] The selection unit can select the most appropriate character by taking into consideration the user's geographical location information. For example, the selection unit selects the most appropriate character by taking into consideration the user's geographical location information. The geographical location information is acquired based on GPS data, map information, etc. For example, if the user is in a tourist spot, the selection unit selects a character related to that area. Furthermore, if the user is in a specific area, the selection unit can also select a character related to that area. In this way, by taking into consideration the user's geographical location information, a more appropriate character can be selected.

[0094] The selection unit may select a character by referring to the user's social media activity. For example, the selection unit may select a character by referring to the user's social media activity. The social media activity is referenced based on data such as the content of posts, the number of likes, and the number of followers. For example, the selection unit may preferentially select characters in which the user has shown interest on social media. The selection unit may also refer to characters used by the user's friends when selecting a character. In this way, by referring to the user's social media activity, a more appropriate character can be selected. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, estimation unit, and selection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit receives input of the user's departure point and destination via the reception device 38 of the smart device 14. The analysis unit analyzes the user's situation and requests using a generation AI via the specific processing unit 290 of the data processing device 12. The estimation unit estimates an optimal route via the specific processing unit 290 of the data processing device 12. The selection unit analyzes the user's hobbies and preferences via the specific processing unit 290 of the data processing device 12 and selects an optimal character. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, estimation unit, and selection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit accepts input of the user's departure point and destination via the microphone 238 of the smart glasses 214. The analysis unit analyzes the user's situation and requests using a generation AI via the specific processing unit 290 of the data processing device 12. The estimation unit estimates an optimal route via the specific processing unit 290 of the data processing device 12. The selection unit analyzes the user's hobbies and preferences via the specific processing unit 290 of the data processing device 12 and selects an optimal character. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, estimation unit, and selection unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit accepts input of the user's departure point and destination via the microphone 238 of the headset-type terminal 314. The analysis unit analyzes the user's situation and requests using a generation AI via the specific processing unit 290 of the data processing device 12. The estimation unit estimates an optimal route via the specific processing unit 290 of the data processing device 12. The selection unit analyzes the user's hobbies and preferences via the specific processing unit 290 of the data processing device 12 and selects an optimal character. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, estimation unit, and selection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit receives input of the user's departure point and destination via the microphone 238 of the robot 414. The analysis unit analyzes the user's situation and requests using a generation AI via the specific processing unit 290 of the data processing device 12. The estimation unit estimates an optimal route via the specific processing unit 290 of the data processing device 12. The selection unit analyzes the user's hobbies and preferences via the specific processing unit 290 of the data processing device 12 and selects an optimal character.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The navigation system may further include a traffic information acquisition unit that acquires real-time traffic information. The traffic information acquisition unit acquires, for example, traffic congestion and accident information in real time and provides it to the analysis unit. The analysis unit can re-analyze the optimal route based on the user's situation and requests based on the acquired traffic information. This allows the user to use the optimal route based on the latest traffic conditions. For example, if traffic congestion occurs, the analysis unit can suggest a detour route. Furthermore, if accident information is acquired, it can also preferentially suggest a safe route. This allows the user to travel more safely and efficiently.

[0097] The navigation system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit acquires data such as heart rate and blood pressure from a wearable device and provides the data to the analysis unit. The analysis unit can suggest an optimal route based on the acquired health data, taking into account the user's health condition. For example, if the user's heart rate is high, the analysis unit can suggest a relaxing route. Also, if the user's blood pressure is high, the analysis unit can suggest a route with many rest stops. This allows the user to travel optimally based on their health condition.

[0098] The navigation system can further estimate the user's emotions and adjust the voice tone of the route guidance based on the estimated emotions. For example, if the user is feeling stressed, the voice guidance can be provided in a calm tone. If the user is in a hurry, the voice guidance can be provided in a quick and clear tone. This allows appropriate voice guidance to be provided according to the user's emotions, reducing stress during travel. Furthermore, if the user is relaxed, the voice guidance can be provided in a relaxed tone. This allows the user to travel more comfortably.

[0099] The navigation system may further include a history analysis unit that analyzes the user's past travel history and proposes an optimal route. The history analysis unit may propose an optimal route based on routes and places the user has visited in the past. For example, the history analysis unit may preferentially propose routes that the user has frequently used in the past. The navigation system may also propose new tourist routes based on tourist spots the user has visited in the past. This allows the user to use the optimal route based on their past travel history.

[0100] The navigation system can further estimate the user's emotions and adjust the facial expression of the route guidance character based on the estimated emotions. For example, if the user is feeling stressed, the character can provide guidance with a calm expression. Alternatively, if the user is relaxed, the character can provide guidance with a smiling expression. This allows the character to provide an appropriate facial expression according to the user's emotions, thereby reducing stress during travel. Furthermore, if the user is excited, the character can provide guidance with a lively expression. This allows the user to travel more comfortably.

[0101] The navigation system can also adjust the frequency of route guidance based on the user's current mood and health condition. For example, if the user is tired, the frequency of guidance can be reduced to allow the user to relax. Alternatively, if the user is in a hurry, the frequency of guidance can be increased to allow the user to reach their destination quickly. This provides an appropriate guidance frequency according to the user's mood and health condition, reducing stress during travel. Furthermore, if the user is seeking healthy exercise, the frequency of guidance can be adjusted to ensure an appropriate amount of exercise. This allows the user to travel more comfortably.

[0102] The navigation system can further estimate the user's emotions and adjust the timing of route guidance based on the estimated emotions. For example, if the user is feeling stressed, the timing of the guidance can be delayed to allow the user to relax. Alternatively, if the user is in a hurry, the timing of the guidance can be accelerated to allow the user to reach the destination quickly. This provides appropriate guidance timing according to the user's emotions, reducing stress during travel. Furthermore, if the user is relaxed, the timing of the guidance can be adjusted to support a more comfortable journey. This allows the user to travel more comfortably.

[0103] The navigation system may further include a social media analysis unit that analyzes the user's social media activity and suggests an optimal route. The social media analysis unit may suggest an optimal route based on, for example, places where the user has checked in or shown interest on social media. For example, a new sightseeing route may be suggested based on tourist spots where the user has checked in on social media. The optimal route may also be suggested based on events or places where the user has shown interest on social media. This allows the user to use an optimal route based on their social media activity.

[0104] The navigation system can further estimate the user's emotions and adjust the content of the route guidance based on the estimated emotions. For example, if the user is feeling stressed, the navigation system can simplify the guidance content to help the user relax. Alternatively, if the user is relaxed, the navigation system can provide detailed guidance content. This allows the navigation system to provide appropriate guidance content according to the user's emotions, thereby reducing stress during travel. Furthermore, if the user is excited, the navigation system can make the guidance content more lively to help the user enjoy their trip. This allows the user to travel more comfortably.

[0105] The navigation system can further improve the accuracy of route guidance by taking into account the user's geographical location information. For example, it can prioritize guidance to places close to the user's current location. Also, if the user is in a specific area, it can prioritize guidance to routes within that area. This allows more appropriate route guidance to be provided by taking into account the user's geographical location information. Furthermore, if the user is in a tourist destination, it can also guide the user to tourist routes related to that area. This allows the user to use the optimal route based on their geographical location information.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The acquisition unit acquires the user's situation and requests. For example, the acquisition unit can accept input of the user's departure point and destination. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit uses the generation AI to analyze the user's situation and requests. The generation AI can analyze the user's input information using technologies such as natural language processing, image recognition, and machine learning algorithms. Step 3: The estimation unit estimates a route based on the information analyzed by the analysis unit. For example, the estimation unit estimates an optimal route using a generation AI. The optimal route is estimated based on criteria such as travel time, distance, and traffic conditions. Step 4: The selection unit selects an agent with a specific character based on the route estimated by the estimation unit. For example, the selection unit may use a generation AI to analyze the user's hobbies and preferences and select the most suitable character.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] The data processing system 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.

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

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0156] 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.

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires a user's situation and requests; an analysis unit that analyzes the information acquired by the acquisition unit; an estimation unit that estimates a route based on the information analyzed by the analysis unit; a selection unit that selects an agent having a specific character based on the route estimated by the estimation unit. A system characterized by:

2. The acquisition unit Accepts user input of origin and destination 2. The system of claim 1.

3. The analysis unit Analyzing user situations and requests using generative AI 2. The system of claim 1.

4. The estimation unit Route estimation using generative AI 2. The system of claim 1.

5. The selection unit Using generative AI to analyze the user's hobbies and preferences and select a character 2. The system of claim 1.

6. The estimation unit For elderly people, estimate the route that reduces the burden on the lower back 2. The system of claim 1.

7. The estimation unit For tourists, we estimate routes that will allow them to efficiently visit tourist spots.

2. The system of claim 1.

8. The estimation unit For the transportation industry, estimate the optimal route taking into account vehicle characteristics 2. The system of claim 1.

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