System

Abstract

An object of a system according to an embodiment is to appropriately propose a timing and a place for a break in accordance with a change in traffic conditions.SOLUTION: A system includes a reception unit, a collection unit, an analysis unit, and a proposal unit. The reception unit receives setting of a destination or a transit point. The collection unit collects traffic information based on the information received by the reception unit. The analysis unit analyzes the traffic information collected by the collection unit. The proposing section proposes a timing or a place of a rest on the basis of the information analyzed by the analyzing section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to appropriately suggest rest times and locations in response to changes in traffic conditions.

[0005] The system according to the embodiment aims to appropriately suggest rest timing and locations in response to changes in traffic conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an analysis unit, and a suggestion unit. The reception unit receives a destination or a stopover setting. The collection unit collects traffic information based on the information received by the reception unit. The analysis unit analyzes the traffic information collected by the collection unit. The suggestion unit suggests timing or locations for resting based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately suggest the timing and location of rest breaks in accordance with changes in traffic conditions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A navigation system according to an embodiment of the present invention utilizes a generation AI to set a destination and intermediate stops in a conversational manner and adjust the timing of rest stops according to changes in traffic conditions. In the navigation system, a user sets a destination and intermediate stops in a conversational manner, and the generation AI monitors traffic conditions in real time and collects information such as congestion and accidents. If traffic conditions change, the generation AI suggests modifying the timing and location of rest stops through a conversational manner with the user. For example, when a user verbally instructs the navigation system, such as "My next destination is XX," the generation AI analyzes the instruction and sets the destination and intermediate stops. The generation AI then collects information such as congestion and accidents from a traffic information service to grasp the current traffic conditions. For example, the generation AI obtains information such as "There is currently a traffic jam on XX road." If traffic conditions change, the generation AI suggests modifying the timing and location of rest stops through a conversational manner with the user. For example, the generation AI suggests, "There is currently a traffic jam on XX road. Would you like to change the next rest stop?" If the user answers "Yes," the generation AI suggests a new rest stop and updates the navigation. This allows the navigation system to flexibly respond to changes in traffic conditions and provide a comfortable drive. This allows the navigation system to consistently set the user's destination and route, collect and analyze traffic information, and suggest rest times and locations. For example, even if traffic jams occur during a long drive, the generative AI can suggest appropriate rest spots to reduce fatigue and support safe driving.

[0029] A navigation system according to an embodiment includes a reception unit, a collection unit, an analysis unit, and a suggestion unit. The reception unit accepts a destination or an intermediate destination. The user can, for example, give a voice instruction such as, "The next destination is ____." The reception unit analyzes the instruction and sets the destination or intermediate destination. For example, the reception unit converts the user's instruction into text data using voice recognition technology and sets the destination or intermediate destination. The collection unit collects traffic information based on the information received by the reception unit. The collection unit collects information such as congestion and accidents from a traffic information service. For example, the collection unit can obtain real-time traffic information via the Internet. The analysis unit analyzes the traffic information collected by the collection unit. For example, the analysis unit analyzes the traffic information using a data analysis algorithm to understand the current traffic situation. For example, the analysis unit analyzes locations of congestion and accidents and generates information to be provided to the user. The suggestion unit suggests timing and locations for rest breaks based on the information analyzed by the analysis unit. The suggestion unit suggests the timing and location of a rest stop, taking into account the facilities and congestion at the rest stop, for example. For example, the suggestion unit makes a suggestion to the user such as, "There is currently a traffic jam. Would you like to change your next rest stop?" This allows the navigation system according to the embodiment to consistently set a destination and waypoints, collect and analyze traffic information, and suggest the timing and location of a rest stop. This allows the navigation system to consistently set a destination and waypoints for the user, collect and analyze traffic information, and suggest the timing and location of a rest stop.

[0030] The reception unit can set a destination or an intermediate stop based on the user's preferences or past driving history. The reception unit, for example, sets a destination or an intermediate stop taking into account the user's preferences. For example, the reception unit suggests a destination or an intermediate stop based on places the user has visited in the past or a preferred route. The reception unit can also set a destination or an intermediate stop taking into account the user's past driving history. For example, the reception unit analyzes the user's past driving history and suggests an optimal destination or intermediate stop. This allows a more appropriate destination or intermediate stop to be set by taking into account the user's preferences and past driving history. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past driving history data into the generation AI and cause the generation AI to suggest optimal destinations and intermediate stops.

[0031] The collection unit can collect congestion or accident information from the traffic information service. The collection unit, for example, collects real-time traffic information from the traffic information service. For example, the collection unit obtains congestion and accident information from the traffic information service via the Internet. The collection unit can also collect traffic information using an API provided by the traffic information service. For example, the collection unit obtains traffic information through the API and grasps the current traffic situation. In this way, by collecting information from the traffic information service, real-time traffic situations can be grasped. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from the traffic information service to a generation AI and cause the generation AI to analyze the traffic information.

[0032] The analysis unit can analyze the collected traffic information and grasp the current traffic situation. The analysis unit, for example, analyzes the collected traffic information using a data analysis algorithm. For example, the analysis unit analyzes the locations of congestion and accident occurrences to grasp the current traffic situation. The analysis unit can also predict the current traffic situation by referring to past traffic data. For example, the analysis unit predicts the current congestion situation based on the past traffic data. In this way, the current traffic situation can be accurately grasped by analyzing the collected traffic information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected traffic information to a generation AI and cause the generation AI to analyze the traffic situation.

[0033] The suggestion unit can suggest the timing or location of a break based on the facilities or congestion level at the rest area. The suggestion unit, for example, suggests the timing and location of a break taking into account the facilities at the rest area. For example, the suggestion unit suggests a rest area with well-equipped facilities such as restrooms, restaurants, and parking lots. The suggestion unit can also suggest the timing and location of a break taking into account the congestion level at the rest area. For example, the suggestion unit grasps the congestion level based on real-time people flow data and suggests a rest area that is not crowded. This makes it possible to suggest a more appropriate timing and location of a break by taking into account the facilities and congestion level at the rest area. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the facilities and congestion level at the rest area into the generation AI and cause the generation AI to suggest an optimal rest area.

[0034] The suggestion unit can provide an interface that allows the user to accept or reject content proposed by the generation AI. For example, the suggestion unit can provide an interface that allows the user to accept or reject content proposed by the generation AI. For example, the suggestion unit can provide an interface that allows the user to accept or reject content proposed by the generation AI using a touch screen or voice recognition technology. The suggestion unit can also provide an easy-to-use interface by taking user interface design into consideration. For example, the suggestion unit can provide an intuitive interface that allows the user to easily accept or reject content proposed by the generation AI. This enables more flexible navigation by allowing the user to accept or reject content proposed by the generation AI. Some or all of the above-described processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can display content proposed by the generation AI on an interface and input the user's selection into the generation AI.

[0035] The reception unit can analyze the user's past driving history and automatically suggest optimal destinations and stopovers. The reception unit, for example, analyzes the user's past driving history and automatically suggests optimal destinations and stopovers. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidates. The reception unit can also predict and suggest places to visit during a specific time period based on the user's past driving history. The reception unit can also analyze the user's past driving patterns and suggest optimal stopovers. In this way, optimal destinations and stopovers can be suggested to the user by analyzing the past driving history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past driving history data into a generation AI and cause the generation AI to suggest optimal destinations and stopovers.

[0036] The reception unit can make suggestions taking into account the user's current physical condition and fatigue level when setting a destination or stopover. The reception unit, for example, can suggest destinations and stopovers taking into account the user's physical condition and fatigue level. For example, if the user is tired, the reception unit can suggest a short-distance destination. Furthermore, if the user is feeling unwell, the reception unit can also suggest a route with plenty of rest time. Furthermore, if the user is feeling energetic, the reception unit can also suggest a route that includes tourist spots. This makes it possible to suggest more appropriate destinations and stopovers by taking into account the user's physical condition and fatigue level. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's physical condition and fatigue level data into the generation AI and cause the generation AI to make suggestions based on the physical condition and fatigue level.

[0037] The reception unit can provide a feedback function to improve the accuracy of the user's voice input when setting a destination or intermediate points. The reception unit provides a feedback function that, for example, displays the recognition result and requests confirmation when the user performs voice input. For example, the reception unit converts the user's voice into text data using voice recognition technology and displays the result and requests confirmation. The reception unit can also provide pronunciation guidance to the user when performing voice input. For example, if the result of the voice input is unclear, the reception unit provides feedback to prompt the user to re-enter. This improves the accuracy of the voice input, enabling more accurate setting of the destination or intermediate points. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice input data to a generation AI and cause the generation AI to perform feedback to improve the accuracy of voice recognition.

[0038] When setting a destination or a stopover point, the reception unit can prioritize suggesting highly relevant places by taking into account the user's geographical location information. The reception unit, for example, suggests a destination or a stopover point by taking into account the user's geographical location information. For example, the reception unit prioritizes suggesting places close to the user's current location. Furthermore, if the user is in a specific region, the reception unit can prioritize suggesting tourist spots in that region. Furthermore, if the user is in a specific area, the reception unit can prioritize suggesting popular restaurants in that area. In this way, by taking the geographical location information into account, more relevant places can be suggested. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to suggest highly relevant places.

[0039] The reception unit can analyze the user's social media activity and suggest related places when setting a destination or a stopover. The reception unit, for example, analyzes the user's social media activity and suggests related places. For example, the reception unit suggests places where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidate locations. The reception unit can also suggest related places by referring to the activity of the user's friends on social media. In this way, more relevant places can be suggested by analyzing social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to suggest related places.

[0040] The reception unit can customize the setting method by reflecting the user's past feedback when setting a destination or a stopover point. The reception unit, for example, sets a destination or a stopover point by reflecting the user's past feedback. For example, the reception unit preferentially suggests routes that the user has previously preferred. The reception unit can also exclude places that the user has previously avoided from the candidates. The reception unit can also optimize the setting method based on the user's past feedback. In this way, by reflecting the past feedback, a more appropriate setting method can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the setting method.

[0041] When collecting traffic information, the collection unit can select the type of information to collect by taking into account the user's past driving history. The collection unit, for example, selects the type of information to collect by taking into account the user's past driving history. For example, the collection unit prioritizes collecting information about roads that the user has avoided in the past. The collection unit can also prioritize collecting information about routes that the user has preferred in the past. The collection unit can also select necessary information based on the user's past driving history. This makes it possible to collect more necessary information by taking the past driving history into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past driving history data into the generation AI and cause the generation AI to select the type of information to collect.

[0042] When collecting traffic information, the collection unit can determine the priority of information to be collected based on the user's current driving situation. The collection unit determines the priority of information to be collected based on, for example, the user's current driving situation. For example, when the user is driving on a highway, the collection unit prioritizes collecting highway traffic information. Furthermore, when the user is driving in an urban area, the collection unit can also prioritize collecting urban traffic information. Furthermore, when the user is caught in a traffic jam, the collection unit can also prioritize collecting information for avoiding the traffic jam. In this way, by determining the priority of information based on the current driving situation, more appropriate information can be provided. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's driving situation data to a generation AI and cause the generation AI to determine the priority of information.

[0043] When collecting traffic information, the collection unit can select the optimal collection means depending on the user's input method. The collection unit selects the optimal collection means depending on, for example, the user's input method (voice, text, image, etc.). For example, if the user uses voice input, the collection unit can collect traffic information using voice recognition technology. Also, if the user uses text input, the collection unit can collect traffic information using text analysis technology. Also, if the user uses image input, the collection unit can collect traffic information using image recognition technology. This enables more efficient information collection by selecting the optimal collection means depending on the input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0044] When collecting traffic information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, collects traffic information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting traffic information close to the user's current location. Furthermore, when the user is in a specific region, the collection unit can also prioritize collecting traffic information for that region. Furthermore, when the user is in a specific area, the collection unit can also prioritize collecting traffic information for that area. In this way, by taking the geographical location information into account, more relevant traffic information can be provided. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0045] When collecting traffic information, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, analyzes the user's social media activities and collects related traffic information. For example, the collection unit collects traffic information for locations where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related traffic information. The collection unit can also collect related traffic information by referring to the activities of the user's friends on social media. In this way, by analyzing social media activities, more relevant traffic information can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related traffic information.

[0046] When collecting traffic information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, collects traffic information by reflecting the user's past feedback. For example, the collection unit preferentially collects information that the user has previously preferred. The collection unit can also avoid collecting information that the user has previously avoided. The collection unit can also optimize the collection method based on the user's past feedback. This enables more appropriate information collection by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0047] When analyzing traffic information, the analysis unit can predict current traffic conditions by referring to past traffic data. The analysis unit, for example, predicts current traffic conditions by referring to past traffic data. For example, the analysis unit predicts current congestion conditions based on past traffic data. The analysis unit can also predict the current accident rate based on past traffic data. The analysis unit can also predict current traffic volume based on past traffic data. In this way, by referring to past traffic data, current traffic conditions can be predicted more accurately. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past traffic data to the generation AI and cause the generation AI to predict the current traffic conditions.

[0048] When analyzing traffic information, the analysis unit can optimize the analysis algorithm according to the user's driving style. The analysis unit, for example, optimizes the analysis algorithm according to the user's driving style. For example, if the user prefers high-speed driving, the analysis unit may emphasize highway information. Furthermore, if the user prefers safe driving, the analysis unit may emphasize safe routes. The analysis unit can also adjust the analysis algorithm according to the user's driving style. In this way, by optimizing the analysis algorithm according to the driving style, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's driving style data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0049] When analyzing traffic information, the analysis unit can improve the accuracy of the analysis by referring to the user's past driving history. The analysis unit, for example, analyzes traffic information by referring to the user's past driving history. For example, the analysis unit improves the accuracy of the analysis based on the user's past driving history. The analysis unit can also suggest an optimal route by referring to the user's past driving history. The analysis unit can also analyze the user's past driving history and improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the past driving history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past driving history data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0050] When analyzing traffic information, the analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information. The analysis unit, for example, analyzes traffic information by taking into account the user's geographical location information. For example, the analysis unit prioritizes analyzing traffic information close to the user's current location. Furthermore, if the user is in a specific region, the analysis unit can also prioritize analyzing traffic information for that region. Furthermore, if the user is in a specific area, the analysis unit can also prioritize analyzing traffic information for that area. In this way, by taking the geographical location information into account, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0051] When analyzing traffic information, the analysis unit can improve the accuracy of the analysis by referring to related traffic accident data. The analysis unit, for example, analyzes traffic information by referring to related traffic accident data. For example, the analysis unit analyzes the current accident risk based on past traffic accident data. The analysis unit can also identify dangerous intersections and roads based on past traffic accident data. The analysis unit can also improve the accuracy of the analysis by referring to past traffic accident data. In this way, the accuracy of the analysis can be improved by referring to the traffic accident data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past traffic accident data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0052] When analyzing traffic information, the analysis unit can weight the analysis taking into account the user's driving history. The analysis unit, for example, analyzes traffic information taking into account the user's driving history. For example, the analysis unit may prioritize routes that the user has frequently used in the past. The analysis unit may also analyze by disregarding routes that the user has avoided in the past. The analysis unit may also adjust the weighting of the analysis based on the user's driving history. In this way, by taking the driving history into consideration, the weighting of the analysis can be adjusted and more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's driving history data into the generation AI and have the generation AI perform the weighting of the analysis.

[0053] When suggesting the timing and location of a break, the suggestion unit can analyze the user's past break history to make the optimal suggestion. The suggestion unit, for example, analyzes the user's past break history to suggest the timing and location of a break. For example, the suggestion unit prioritizes suggesting rest spots that the user has previously preferred. The suggestion unit can also suggest rest spots suitable for a specific time period based on the user's past break history. The suggestion unit can also analyze the user's past break patterns to suggest the optimal timing of a break. In this way, by analyzing the past break history, more appropriate timing and location of a break can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past break history data into the generation AI and cause the generation AI to suggest the optimal timing and location of a break.

[0054] The suggestion unit can make suggestions regarding the timing and location of a break by taking into consideration the user's current physical condition and fatigue level. The suggestion unit, for example, suggests the timing and location of a break by taking into consideration the user's physical condition and fatigue level. For example, if the user is tired, the suggestion unit can suggest a nearby rest spot. Furthermore, if the user is feeling unwell, the suggestion unit can also suggest a rest spot near a medical facility. Furthermore, if the user is feeling well, the suggestion unit can also suggest a rest spot that includes a tourist spot. In this way, by taking into consideration the user's physical condition and fatigue level, more appropriate timing and location of a break can be suggested. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's physical condition and fatigue level data into the generation AI and cause the generation AI to make suggestions based on the physical condition and fatigue level.

[0055] The suggestion unit can improve the suggestion method by reflecting user feedback when suggesting the timing and location of a break. The suggestion unit, for example, suggests the timing and location of a break by reflecting user feedback. For example, the suggestion unit prioritizes suggesting rest spots that the user has previously preferred. The suggestion unit can also avoid suggesting rest spots that the user has previously avoided. The suggestion unit can also optimize the suggestion method based on user feedback. In this way, a more appropriate suggestion method can be provided by reflecting feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input user feedback data into the generation AI and cause the generation AI to improve the suggestion method.

[0056] When suggesting the timing and location of a break, the suggestion unit can make the optimal suggestion by taking into account the user's geographical location information. The suggestion unit, for example, suggests the timing and location of a break by taking into account the user's geographical location information. For example, the suggestion unit prioritizes suggesting rest spots close to the user's current location. Furthermore, if the user is in a specific region, the suggestion unit can prioritize suggesting rest spots in that region. Furthermore, if the user is in a specific area, the suggestion unit can prioritize suggesting popular rest spots in that area. In this way, by taking the geographical location information into account, more appropriate timing and location of a break can be suggested. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information to the generation AI and cause the generation AI to suggest optimal rest spots.

[0057] When suggesting the timing and location of a break, the suggestion unit can analyze the user's social media activity and make relevant suggestions. The suggestion unit, for example, analyzes the user's social media activity and suggests relevant rest spots. For example, the suggestion unit suggests rest spots near places where the user has checked in on social media. The suggestion unit can also analyze the content of the user's social media posts and suggest relevant rest spots. The suggestion unit can also suggest relevant rest spots by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, more relevant timing and locations for a break can be suggested. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media data into a generation AI and cause the generation AI to suggest relevant rest spots.

[0058] The suggestion unit can customize the suggestion method by reflecting the user's past feedback when suggesting the timing and location of a break. The suggestion unit, for example, suggests the timing and location of a break by reflecting the user's past feedback. For example, the suggestion unit prioritizes suggesting rest spots that the user has previously preferred. The suggestion unit can also avoid suggesting rest spots that the user has previously avoided. The suggestion unit can also optimize the suggestion method based on the user's past feedback. In this way, by reflecting the past feedback, a more appropriate suggestion method can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the suggestion method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When collecting traffic information, the collection unit can determine the priority of the information taking into account the user's current driving situation. For example, when the user is driving on a highway, traffic information for the highway can be collected with priority. Also, when the user is driving in an urban area, traffic information for the urban area can be collected with priority. Furthermore, when the user is caught in a traffic jam, information for avoiding the traffic jam can be collected with priority. In this way, by determining the priority of information based on the current driving situation, more appropriate information can be provided.

[0061] When analyzing collected traffic information, the analysis unit can improve the accuracy of the analysis by referring to the user's past driving history. For example, the analysis can emphasize information about routes that the user has frequently used in the past. Also, the analysis can be performed while disregarding information about routes that the user has avoided in the past. Furthermore, the analysis algorithm can be optimized according to the user's driving style. In this way, the accuracy of the analysis can be improved by referring to the user's past driving history.

[0062] When suggesting rest timings and locations, the suggestion unit can make suggestions taking into account the user's current physical condition and fatigue level. For example, if the user is tired, a nearby rest spot can be suggested. Also, if the user is feeling unwell, a rest spot near a medical facility can be suggested. Furthermore, if the user is in good health, a rest spot including a tourist spot can be suggested. In this way, by taking the user's physical condition and fatigue level into consideration, more appropriate rest timings and locations can be suggested.

[0063] When suggesting the timing and location of a break, the suggestion unit can analyze the user's past break history and make the most appropriate suggestion. For example, it can prioritize suggesting rest spots that the user has previously preferred. It can also suggest rest spots that are suitable for a specific time period based on the user's past break history. Furthermore, it can analyze the user's past break patterns and suggest the most appropriate timing for a break. In this way, by analyzing the past break history, it can suggest more appropriate timing and location for a break.

[0064] When suggesting timing and locations for breaks, the suggestion unit can analyze the user's social media activity to make relevant suggestions. For example, it can suggest rest spots near places where the user has checked in on social media. It can also analyze the content of the user's social media posts to suggest relevant rest spots. It can also suggest relevant rest spots by taking into account the activities of the user's friends on social media. In this way, by analyzing social media activity, it is possible to suggest more relevant timing and locations for breaks.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives the setting of a destination or a stopover point. The user can give instructions by voice, such as "The next destination is ____." The reception unit analyzes this instruction and sets the destination or stopover point. For example, the reception unit converts the user's instruction into text data using voice recognition technology, and sets the destination or stopover point. Step 2: The collection unit collects traffic information based on the information received by the reception unit. For example, the collection unit collects information on congestion, accidents, etc. from a traffic information service. For example, the collection unit can obtain real-time traffic information via the Internet. Step 3: The analysis unit analyzes the traffic information collected by the collection unit. The analysis unit analyzes the traffic information using, for example, a data analysis algorithm, and grasps the current traffic situation. For example, the analysis unit analyzes the locations of traffic jams and accident occurrences, and generates information to be provided to the user. Step 4: The suggestion unit suggests the timing and location of a break based on the information analyzed by the analysis unit. The suggestion unit suggests the timing and location of a break, taking into account, for example, the facilities and congestion at the rest area. For example, the suggestion unit makes a suggestion to the user such as, "There is currently a traffic jam. Would you like to change the next rest area?"

[0067] (Example 2) A navigation system according to an embodiment of the present invention utilizes a generation AI to set a destination and intermediate stops in a conversational manner and adjust the timing of rest stops according to changes in traffic conditions. In the navigation system, a user sets a destination and intermediate stops in a conversational manner, and the generation AI monitors traffic conditions in real time and collects information such as congestion and accidents. If traffic conditions change, the generation AI suggests modifying the timing and location of rest stops through a conversational manner with the user. For example, when a user verbally instructs the navigation system, such as "My next destination is XX," the generation AI analyzes the instruction and sets the destination and intermediate stops. The generation AI then collects information such as congestion and accidents from a traffic information service to grasp the current traffic conditions. For example, the generation AI obtains information such as "There is currently a traffic jam on XX road." If traffic conditions change, the generation AI suggests modifying the timing and location of rest stops through a conversational manner with the user. For example, the generation AI suggests, "There is currently a traffic jam on XX road. Would you like to change the next rest stop?" If the user answers "Yes," the generation AI suggests a new rest stop and updates the navigation. This allows the navigation system to flexibly respond to changes in traffic conditions and provide a comfortable drive. This allows the navigation system to consistently set the user's destination and route, collect and analyze traffic information, and suggest rest times and locations. For example, even if traffic jams occur during a long drive, the generative AI can suggest appropriate rest spots to reduce fatigue and support safe driving.

[0068] A navigation system according to an embodiment includes a reception unit, a collection unit, an analysis unit, and a suggestion unit. The reception unit accepts a destination or an intermediate destination. The user can, for example, give a voice instruction such as, "The next destination is ____." The reception unit analyzes the instruction and sets the destination or intermediate destination. For example, the reception unit converts the user's instruction into text data using voice recognition technology and sets the destination or intermediate destination. The collection unit collects traffic information based on the information received by the reception unit. The collection unit collects information such as congestion and accidents from a traffic information service. For example, the collection unit can obtain real-time traffic information via the Internet. The analysis unit analyzes the traffic information collected by the collection unit. For example, the analysis unit analyzes the traffic information using a data analysis algorithm to understand the current traffic situation. For example, the analysis unit analyzes locations of congestion and accidents and generates information to be provided to the user. The suggestion unit suggests timing and locations for rest breaks based on the information analyzed by the analysis unit. The suggestion unit suggests the timing and location of a rest stop, taking into account the facilities and congestion at the rest stop, for example. For example, the suggestion unit makes a suggestion to the user such as, "There is currently a traffic jam. Would you like to change your next rest stop?" This allows the navigation system according to the embodiment to consistently set a destination and waypoints, collect and analyze traffic information, and suggest the timing and location of a rest stop. This allows the navigation system to consistently set a destination and waypoints for the user, collect and analyze traffic information, and suggest the timing and location of a rest stop.

[0069] The reception unit can set a destination or an intermediate stop based on the user's preferences or past driving history. The reception unit, for example, sets a destination or an intermediate stop taking into account the user's preferences. For example, the reception unit suggests a destination or an intermediate stop based on places the user has visited in the past or a preferred route. The reception unit can also set a destination or an intermediate stop taking into account the user's past driving history. For example, the reception unit analyzes the user's past driving history and suggests an optimal destination or intermediate stop. This allows a more appropriate destination or intermediate stop to be set by taking into account the user's preferences and past driving history. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past driving history data into the generation AI and cause the generation AI to suggest optimal destinations and intermediate stops.

[0070] The collection unit can collect congestion or accident information from the traffic information service. The collection unit, for example, collects real-time traffic information from the traffic information service. For example, the collection unit obtains congestion and accident information from the traffic information service via the Internet. The collection unit can also collect traffic information using an API provided by the traffic information service. For example, the collection unit obtains traffic information through the API and grasps the current traffic situation. In this way, by collecting information from the traffic information service, real-time traffic situations can be grasped. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from the traffic information service to a generation AI and cause the generation AI to analyze the traffic information.

[0071] The analysis unit can analyze the collected traffic information and grasp the current traffic situation. The analysis unit, for example, analyzes the collected traffic information using a data analysis algorithm. For example, the analysis unit analyzes the locations of congestion and accident occurrences to grasp the current traffic situation. The analysis unit can also predict the current traffic situation by referring to past traffic data. For example, the analysis unit predicts the current congestion situation based on the past traffic data. In this way, the current traffic situation can be accurately grasped by analyzing the collected traffic information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected traffic information to a generation AI and cause the generation AI to analyze the traffic situation.

[0072] The suggestion unit can suggest the timing or location of a break based on the facilities or congestion level at the rest area. The suggestion unit, for example, suggests the timing and location of a break taking into account the facilities at the rest area. For example, the suggestion unit suggests a rest area with well-equipped facilities such as restrooms, restaurants, and parking lots. The suggestion unit can also suggest the timing and location of a break taking into account the congestion level at the rest area. For example, the suggestion unit grasps the congestion level based on real-time people flow data and suggests a rest area that is not crowded. This makes it possible to suggest a more appropriate timing and location of a break by taking into account the facilities and congestion level at the rest area. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the facilities and congestion level at the rest area into the generation AI and cause the generation AI to suggest an optimal rest area.

[0073] The suggestion unit can provide an interface that allows the user to accept or reject content proposed by the generation AI. For example, the suggestion unit can provide an interface that allows the user to accept or reject content proposed by the generation AI. For example, the suggestion unit can provide an interface that allows the user to accept or reject content proposed by the generation AI using a touch screen or voice recognition technology. The suggestion unit can also provide an easy-to-use interface by taking user interface design into consideration. For example, the suggestion unit can provide an intuitive interface that allows the user to easily accept or reject content proposed by the generation AI. This enables more flexible navigation by allowing the user to accept or reject content proposed by the generation AI. Some or all of the above-described processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can display content proposed by the generation AI on an interface and input the user's selection into the generation AI.

[0074] The reception unit can estimate the user's emotions and adjust the method for setting destinations and stopovers based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and adjust the method for setting destinations and stopovers based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly set destinations and stopovers. This allows the setting method to be adjusted according to the user's emotions, thereby enabling more appropriate setting of destinations and stopovers. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the setting method based on the emotion.

[0075] The reception unit can analyze the user's past driving history and automatically suggest optimal destinations and stopovers. The reception unit, for example, analyzes the user's past driving history and automatically suggests optimal destinations and stopovers. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidates. The reception unit can also predict and suggest places to visit during a specific time period based on the user's past driving history. The reception unit can also analyze the user's past driving patterns and suggest optimal stopovers. In this way, optimal destinations and stopovers can be suggested to the user by analyzing the past driving history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past driving history data into a generation AI and cause the generation AI to suggest optimal destinations and stopovers.

[0076] The reception unit can make suggestions taking into account the user's current physical condition and fatigue level when setting a destination or stopover. The reception unit, for example, can suggest destinations and stopovers taking into account the user's physical condition and fatigue level. For example, if the user is tired, the reception unit can suggest a short-distance destination. Furthermore, if the user is feeling unwell, the reception unit can also suggest a route with plenty of rest time. Furthermore, if the user is feeling energetic, the reception unit can also suggest a route that includes tourist spots. This makes it possible to suggest more appropriate destinations and stopovers by taking into account the user's physical condition and fatigue level. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's physical condition and fatigue level data into the generation AI and cause the generation AI to make suggestions based on the physical condition and fatigue level.

[0077] The reception unit can provide a feedback function to improve the accuracy of the user's voice input when setting a destination or intermediate points. The reception unit provides a feedback function that, for example, displays the recognition result and requests confirmation when the user performs voice input. For example, the reception unit converts the user's voice into text data using voice recognition technology and displays the result and requests confirmation. The reception unit can also provide pronunciation guidance to the user when performing voice input. For example, if the result of the voice input is unclear, the reception unit provides feedback to prompt the user to re-enter. This improves the accuracy of the voice input, enabling more accurate setting of the destination or intermediate points. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice input data to a generation AI and cause the generation AI to perform feedback to improve the accuracy of voice recognition.

[0078] The reception unit can estimate the user's emotions and prioritize destinations and stopovers based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and prioritizes destinations and stopovers based on the estimated user emotions. For example, if the user is relaxed, the reception unit can prioritize suggesting tourist spots. If the user is in a hurry, the reception unit can prioritize suggesting the shortest route. If the user is stressed, the reception unit can prioritize suggesting routes with many rest stops. This enables more appropriate destinations and stopovers to be suggested by determining priorities based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to determine priorities based on emotions.

[0079] When setting a destination or a stopover point, the reception unit can prioritize suggesting highly relevant places by taking into account the user's geographical location information. The reception unit, for example, suggests a destination or a stopover point by taking into account the user's geographical location information. For example, the reception unit prioritizes suggesting places close to the user's current location. Furthermore, if the user is in a specific region, the reception unit can prioritize suggesting tourist spots in that region. Furthermore, if the user is in a specific area, the reception unit can prioritize suggesting popular restaurants in that area. In this way, by taking the geographical location information into account, more relevant places can be suggested. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to suggest highly relevant places.

[0080] The reception unit can analyze the user's social media activity and suggest related places when setting a destination or a stopover. The reception unit, for example, analyzes the user's social media activity and suggests related places. For example, the reception unit suggests places where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidate locations. The reception unit can also suggest related places by referring to the activity of the user's friends on social media. In this way, more relevant places can be suggested by analyzing social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to suggest related places.

[0081] The reception unit can customize the setting method by reflecting the user's past feedback when setting a destination or a stopover point. The reception unit, for example, sets a destination or a stopover point by reflecting the user's past feedback. For example, the reception unit preferentially suggests routes that the user has previously preferred. The reception unit can also exclude places that the user has previously avoided from the candidates. The reception unit can also optimize the setting method based on the user's past feedback. In this way, by reflecting the past feedback, a more appropriate setting method can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the setting method.

[0082] The collection unit can estimate the user's emotions and adjust the frequency of traffic information collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the frequency of traffic information collection based on the estimated user emotions. For example, the collection unit can increase the frequency of traffic information collection when the user is in a hurry. The collection unit can also decrease the frequency of traffic information collection when the user is relaxed. The collection unit can also appropriately adjust the frequency of traffic information collection when the user is stressed. This allows for more appropriate traffic information to be provided by adjusting the collection frequency according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the collection frequency based on the emotion.

[0083] When collecting traffic information, the collection unit can select the type of information to collect by taking into account the user's past driving history. The collection unit, for example, selects the type of information to collect by taking into account the user's past driving history. For example, the collection unit prioritizes collecting information about roads that the user has avoided in the past. The collection unit can also prioritize collecting information about routes that the user has preferred in the past. The collection unit can also select necessary information based on the user's past driving history. This makes it possible to collect more necessary information by taking the past driving history into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past driving history data into the generation AI and cause the generation AI to select the type of information to collect.

[0084] When collecting traffic information, the collection unit can determine the priority of information to be collected based on the user's current driving situation. The collection unit determines the priority of information to be collected based on, for example, the user's current driving situation. For example, when the user is driving on a highway, the collection unit prioritizes collecting highway traffic information. Furthermore, when the user is driving in an urban area, the collection unit can also prioritize collecting urban traffic information. Furthermore, when the user is caught in a traffic jam, the collection unit can also prioritize collecting information for avoiding the traffic jam. In this way, by determining the priority of information based on the current driving situation, more appropriate information can be provided. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's driving situation data to a generation AI and cause the generation AI to determine the priority of information.

[0085] When collecting traffic information, the collection unit can select the optimal collection means depending on the user's input method. The collection unit selects the optimal collection means depending on, for example, the user's input method (voice, text, image, etc.). For example, if the user uses voice input, the collection unit can collect traffic information using voice recognition technology. Also, if the user uses text input, the collection unit can collect traffic information using text analysis technology. Also, if the user uses image input, the collection unit can collect traffic information using image recognition technology. This enables more efficient information collection by selecting the optimal collection means depending on the input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0086] The collection unit can estimate the user's emotions and determine the priority of traffic information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of traffic information to be collected based on the estimated user emotions. For example, if the user is in a hurry, the collection unit can prioritize collecting traffic congestion information. Also, if the user is relaxed, the collection unit can prioritize collecting information about tourist spots. Also, if the user is feeling stressed, the collection unit can prioritize collecting information about rest spots. This allows for more appropriate traffic information to be provided by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of information based on emotions.

[0087] When collecting traffic information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, collects traffic information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting traffic information close to the user's current location. Furthermore, when the user is in a specific region, the collection unit can also prioritize collecting traffic information for that region. Furthermore, when the user is in a specific area, the collection unit can also prioritize collecting traffic information for that area. In this way, by taking the geographical location information into account, more relevant traffic information can be provided. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0088] When collecting traffic information, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, analyzes the user's social media activities and collects related traffic information. For example, the collection unit collects traffic information for locations where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related traffic information. The collection unit can also collect related traffic information by referring to the activities of the user's friends on social media. In this way, by analyzing social media activities, more relevant traffic information can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related traffic information.

[0089] When collecting traffic information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, collects traffic information by reflecting the user's past feedback. For example, the collection unit preferentially collects information that the user has previously preferred. The collection unit can also avoid collecting information that the user has previously avoided. The collection unit can also optimize the collection method based on the user's past feedback. This enables more appropriate information collection by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0090] The analysis unit can estimate the user's emotions and adjust the traffic information analysis method based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the traffic information analysis method based on the estimated user emotions. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a quick analysis when the user is in a hurry. The analysis unit can also perform an analysis to reduce stress when the user is feeling stressed. This enables more appropriate traffic information analysis by adjusting the analysis method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the analysis method based on the emotion.

[0091] When analyzing traffic information, the analysis unit can predict current traffic conditions by referring to past traffic data. The analysis unit, for example, predicts current traffic conditions by referring to past traffic data. For example, the analysis unit predicts current congestion conditions based on past traffic data. The analysis unit can also predict the current accident rate based on past traffic data. The analysis unit can also predict current traffic volume based on past traffic data. In this way, by referring to past traffic data, current traffic conditions can be predicted more accurately. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past traffic data to the generation AI and cause the generation AI to predict the current traffic conditions.

[0092] When analyzing traffic information, the analysis unit can optimize the analysis algorithm according to the user's driving style. The analysis unit, for example, optimizes the analysis algorithm according to the user's driving style. For example, if the user prefers high-speed driving, the analysis unit may emphasize highway information. Furthermore, if the user prefers safe driving, the analysis unit may emphasize safe routes. The analysis unit can also adjust the analysis algorithm according to the user's driving style. In this way, by optimizing the analysis algorithm according to the driving style, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's driving style data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0093] When analyzing traffic information, the analysis unit can improve the accuracy of the analysis by referring to the user's past driving history. The analysis unit, for example, analyzes traffic information by referring to the user's past driving history. For example, the analysis unit improves the accuracy of the analysis based on the user's past driving history. The analysis unit can also suggest an optimal route by referring to the user's past driving history. The analysis unit can also analyze the user's past driving history and improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the past driving history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past driving history data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for adjusting the display method according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0095] When analyzing traffic information, the analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information. The analysis unit, for example, analyzes traffic information by taking into account the user's geographical location information. For example, the analysis unit prioritizes analyzing traffic information close to the user's current location. Furthermore, if the user is in a specific region, the analysis unit can also prioritize analyzing traffic information for that region. Furthermore, if the user is in a specific area, the analysis unit can also prioritize analyzing traffic information for that area. In this way, by taking the geographical location information into account, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0096] When analyzing traffic information, the analysis unit can improve the accuracy of the analysis by referring to related traffic accident data. The analysis unit, for example, analyzes traffic information by referring to related traffic accident data. For example, the analysis unit analyzes the current accident risk based on past traffic accident data. The analysis unit can also identify dangerous intersections and roads based on past traffic accident data. The analysis unit can also improve the accuracy of the analysis by referring to past traffic accident data. In this way, the accuracy of the analysis can be improved by referring to the traffic accident data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past traffic accident data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0097] When analyzing traffic information, the analysis unit can weight the analysis taking into account the user's driving history. The analysis unit, for example, analyzes traffic information taking into account the user's driving history. For example, the analysis unit may prioritize routes that the user has frequently used in the past. The analysis unit may also analyze by disregarding routes that the user has avoided in the past. The analysis unit may also adjust the weighting of the analysis based on the user's driving history. In this way, by taking the driving history into consideration, the weighting of the analysis can be adjusted and more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's driving history data into the generation AI and have the generation AI perform the weighting of the analysis.

[0098] The suggestion unit can estimate the user's emotions and adjust the method for suggesting the timing and location of a break based on the estimated user's emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the method for suggesting the timing and location of a break based on the estimated user's emotions. For example, the suggestion unit can suggest an earlier break if the user is tired. The suggestion unit can also suggest a longer break if the user is relaxed. The suggestion unit can also suggest a shorter break if the user is in a hurry. This allows the suggestion method to be adjusted according to the user's emotions, thereby suggesting more appropriate timing and location of a break. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, for example, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the suggestion method based on the emotion.

[0099] When suggesting the timing and location of a break, the suggestion unit can analyze the user's past break history to make the optimal suggestion. The suggestion unit, for example, analyzes the user's past break history to suggest the timing and location of a break. For example, the suggestion unit prioritizes suggesting rest spots that the user has previously preferred. The suggestion unit can also suggest rest spots suitable for a specific time period based on the user's past break history. The suggestion unit can also analyze the user's past break patterns to suggest the optimal timing of a break. In this way, by analyzing the past break history, more appropriate timing and location of a break can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past break history data into the generation AI and cause the generation AI to suggest the optimal timing and location of a break.

[0100] The suggestion unit can make suggestions regarding the timing and location of a break by taking into consideration the user's current physical condition and fatigue level. The suggestion unit, for example, suggests the timing and location of a break by taking into consideration the user's physical condition and fatigue level. For example, if the user is tired, the suggestion unit can suggest a nearby rest spot. Furthermore, if the user is feeling unwell, the suggestion unit can also suggest a rest spot near a medical facility. Furthermore, if the user is feeling well, the suggestion unit can also suggest a rest spot that includes a tourist spot. In this way, by taking into consideration the user's physical condition and fatigue level, more appropriate timing and location of a break can be suggested. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's physical condition and fatigue level data into the generation AI and cause the generation AI to make suggestions based on the physical condition and fatigue level.

[0101] The suggestion unit can improve the suggestion method by reflecting user feedback when suggesting the timing and location of a break. The suggestion unit, for example, suggests the timing and location of a break by reflecting user feedback. For example, the suggestion unit prioritizes suggesting rest spots that the user has previously preferred. The suggestion unit can also avoid suggesting rest spots that the user has previously avoided. The suggestion unit can also optimize the suggestion method based on user feedback. In this way, a more appropriate suggestion method can be provided by reflecting feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input user feedback data into the generation AI and cause the generation AI to improve the suggestion method.

[0102] The suggestion unit can estimate the user's emotions and determine priorities for break timing and locations based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and determines priorities for break timing and locations based on the estimated user emotions. For example, if the user is tired, the suggestion unit can prioritize suggesting an earlier break. Also, if the user is relaxed, the suggestion unit can prioritize suggesting a longer break. Also, if the user is in a hurry, the suggestion unit can prioritize suggesting a shorter break. In this way, by determining priorities according to the user's emotions, more appropriate break timing and locations can be suggested. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI. For example, the suggestion unit can input user emotion data into the generation AI and cause the generation AI to determine priorities based on emotions.

[0103] When suggesting the timing and location of a break, the suggestion unit can make the optimal suggestion by taking into account the user's geographical location information. The suggestion unit, for example, suggests the timing and location of a break by taking into account the user's geographical location information. For example, the suggestion unit prioritizes suggesting rest spots close to the user's current location. Furthermore, if the user is in a specific region, the suggestion unit can prioritize suggesting rest spots in that region. Furthermore, if the user is in a specific area, the suggestion unit can prioritize suggesting popular rest spots in that area. In this way, by taking the geographical location information into account, more appropriate timing and location of a break can be suggested. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information to the generation AI and cause the generation AI to suggest optimal rest spots.

[0104] When suggesting the timing and location of a break, the suggestion unit can analyze the user's social media activity and make relevant suggestions. The suggestion unit, for example, analyzes the user's social media activity and suggests relevant rest spots. For example, the suggestion unit suggests rest spots near places where the user has checked in on social media. The suggestion unit can also analyze the content of the user's social media posts and suggest relevant rest spots. The suggestion unit can also suggest relevant rest spots by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, more relevant timing and locations for a break can be suggested. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media data into a generation AI and cause the generation AI to suggest relevant rest spots.

[0105] The suggestion unit can customize the suggestion method by reflecting the user's past feedback when suggesting the timing and location of a break. The suggestion unit, for example, suggests the timing and location of a break by reflecting the user's past feedback. For example, the suggestion unit prioritizes suggesting rest spots that the user has previously preferred. The suggestion unit can also avoid suggesting rest spots that the user has previously avoided. The suggestion unit can also optimize the suggestion method based on the user's past feedback. In this way, by reflecting the past feedback, a more appropriate suggestion method can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the suggestion method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 or the specific processing unit 290 of the data processing device 12. The collection unit collects information from a traffic information providing service via, for example, the communication I / F 44 of the smart device 14 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected traffic information. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and suggests timing and locations for rest based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The collection unit collects information from a traffic information providing service via, for example, the communication I / F 44 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected traffic information. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and suggests timing and locations for rest based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The collection unit collects information from a traffic information providing service via, for example, the communication I / F 44 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected traffic information. The suggestion unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and suggests timing and locations for rest based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 or the specific processing unit 290 of the data processing device 12. The collection unit collects information from a traffic information providing service via, for example, the communication I / F 44 of the robot 414 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected traffic information. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and suggests timing and locations for taking a break based on the analysis results.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When receiving a user's voice input, the reception unit can analyze the user's voice tone and speed to infer the user's emotions. For example, if the user is in a hurry, the voice tone is likely to be high and the speed is likely to be fast. In this case, the reception unit can provide a quick response and present a concise interface. Also, if the user is relaxed, the voice tone is likely to be low and the speed is likely to be slow. In this case, the reception unit can provide detailed options and present a customizable interface. Furthermore, if the user is stressed, the voice tone is likely to be unstable and the speed is likely to fluctuate. In this case, the reception unit can provide a simple and intuitive interface to reduce the burden on the user.

[0108] When collecting traffic information, the collection unit can determine the priority of the information taking into account the user's current driving situation. For example, when the user is driving on a highway, traffic information for the highway can be collected with priority. Also, when the user is driving in an urban area, traffic information for the urban area can be collected with priority. Furthermore, when the user is caught in a traffic jam, information for avoiding the traffic jam can be collected with priority. In this way, by determining the priority of information based on the current driving situation, more appropriate information can be provided.

[0109] When analyzing collected traffic information, the analysis unit can improve the accuracy of the analysis by referring to the user's past driving history. For example, the analysis can emphasize information about routes that the user has frequently used in the past. Also, the analysis can be performed while disregarding information about routes that the user has avoided in the past. Furthermore, the analysis algorithm can be optimized according to the user's driving style. In this way, the accuracy of the analysis can be improved by referring to the user's past driving history.

[0110] When suggesting rest timings and locations, the suggestion unit can make suggestions taking into account the user's current physical condition and fatigue level. For example, if the user is tired, a nearby rest spot can be suggested. Also, if the user is feeling unwell, a rest spot near a medical facility can be suggested. Furthermore, if the user is in good health, a rest spot including a tourist spot can be suggested. In this way, by taking the user's physical condition and fatigue level into consideration, more appropriate rest timings and locations can be suggested.

[0111] When suggesting the timing and location of a break, the suggestion unit can analyze the user's past break history and make the most appropriate suggestion. For example, it can prioritize suggesting rest spots that the user has previously preferred. It can also suggest rest spots that are suitable for a specific time period based on the user's past break history. Furthermore, it can analyze the user's past break patterns and suggest the most appropriate timing for a break. In this way, by analyzing the past break history, it can suggest more appropriate timing and location for a break.

[0112] The suggestion unit can estimate the user's emotions and adjust the method of suggesting the timing and location of a break based on the estimated user's emotions. For example, if the user is tired, an earlier break can be suggested. If the user is relaxed, a longer break can be suggested. Furthermore, if the user is in a hurry, a shorter break can be suggested. In this way, by adjusting the suggestion method according to the user's emotions, more appropriate timing and location of a break can be suggested.

[0113] The reception unit can estimate the user's emotions and adjust the method for setting destinations and stopovers based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable the user to quickly set destinations and stopovers. This allows the setting method to be adjusted according to the user's emotions, making it possible to set more appropriate destinations and stopovers.

[0114] The collection unit can estimate the user's emotions and adjust the frequency of traffic information collection based on the estimated user's emotions. For example, if the user is in a hurry, the frequency of traffic information collection can be increased. Also, if the user is relaxed, the frequency of traffic information collection can be decreased. Furthermore, if the user is feeling stressed, the frequency of traffic information collection can be adjusted appropriately. In this way, by adjusting the collection frequency according to the user's emotions, more appropriate traffic information can be provided.

[0115] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method according to the user's emotions, more appropriate analysis results can be provided.

[0116] When suggesting timing and locations for breaks, the suggestion unit can analyze the user's social media activity to make relevant suggestions. For example, it can suggest rest spots near places where the user has checked in on social media. It can also analyze the content of the user's social media posts to suggest relevant rest spots. It can also suggest relevant rest spots by taking into account the activities of the user's friends on social media. In this way, by analyzing social media activity, it is possible to suggest more relevant timing and locations for breaks.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives the setting of a destination or a stopover point. The user can give instructions by voice, such as "The next destination is ____." The reception unit analyzes this instruction and sets the destination or stopover point. For example, the reception unit converts the user's instruction into text data using voice recognition technology, and sets the destination or stopover point. Step 2: The collection unit collects traffic information based on the information received by the reception unit. For example, the collection unit collects information on congestion, accidents, etc. from a traffic information service. For example, the collection unit can obtain real-time traffic information via the Internet. Step 3: The analysis unit analyzes the traffic information collected by the collection unit. The analysis unit analyzes the traffic information using, for example, a data analysis algorithm, and grasps the current traffic situation. For example, the analysis unit analyzes the locations of traffic jams and accident occurrences, and generates information to be provided to the user. Step 4: The suggestion unit suggests the timing and location of a break based on the information analyzed by the analysis unit. The suggestion unit suggests the timing and location of a break, taking into account, for example, the facilities and congestion at the rest area. For example, the suggestion unit makes a suggestion to the user such as, "There is currently a traffic jam. Would you like to change the next rest area?"

[0119] 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.

[0120] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 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.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the 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.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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, in order to avoid confusion and to 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a destination or a stopover setting; a collection unit that collects traffic information based on the information received by the reception unit; an analysis unit that analyzes the traffic information collected by the collection unit; a suggestion unit that suggests a timing or location for a break based on the information analyzed by the analysis unit; Equipped with A system characterized by:

2. The reception unit Set destinations or stops based on user preferences or past driving history 2. The system of claim 1.

3. The collecting unit Collecting congestion or accident information from traffic information services 2. The system of claim 1.

4. The analysis unit Analyze collected traffic information and understand current traffic conditions 2. The system of claim 1.

5. The proposal unit Suggest break timing or location based on rest stop facilities or congestion 2. The system of claim 1.

6. The proposal unit Provide an interface for users to accept or reject the content proposed by the generative AI 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the destination and route setting method based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's driving history and automatically suggests optimal destinations and stops 2. The system of claim 1.

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