system

Abstract

The system according to the embodiment aims to alleviate traffic congestion and facilitate the passage of emergency vehicles. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, an adjustment unit, an emergency acquisition unit, and a control unit. The acquisition unit acquires location information from a smartphone. The analysis unit analyzes traffic congestion conditions based on the location information acquired by the acquisition unit. The adjustment unit adjusts the color of traffic lights based on the results of the analysis by the analysis unit. The emergency acquisition unit acquires location information from emergency vehicles. The control unit controls the traffic lights based on the information acquired by the emergency acquisition unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately control traffic lights to alleviate traffic congestion or allow emergency vehicles to pass smoothly, and there is room for improvement.

[0005] The system according to the embodiment aims to alleviate traffic congestion and facilitate the passage of emergency vehicles. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, an adjustment unit, an emergency acquisition unit, and a control unit. The acquisition unit acquires location information of a smartphone. The analysis unit analyzes traffic congestion conditions based on the location information acquired by the acquisition unit. The adjustment unit adjusts the color of the traffic light based on the results of the analysis by the analysis unit. The emergency acquisition unit acquires location information of an emergency vehicle. The control unit controls the traffic light based on the information acquired by the emergency acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can alleviate traffic congestion and facilitate the passage of emergency vehicles. [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 traffic control system according to an embodiment of the present invention links smartphone location information with autonomous vehicles to analyze and predict congestion in real time. This traffic control system analyzes current traffic conditions and predicts congestion based on location information acquired from the smartphone. It then automatically adjusts the green / red color of traffic lights based on the analysis results to optimize traffic flow. For example, when traffic volume is high, the green light time is extended to alleviate congestion. Furthermore, when an emergency vehicle approaches a traffic light, the traffic light automatically switches to green based on the emergency vehicle's location information. This supports the rapid passage of emergency vehicles. This system enables the construction of a traffic infrastructure optimized for autonomous vehicles and improves traffic efficiency and safety. For example, it includes an acquisition unit that acquires smartphone location information, an analysis unit that analyzes congestion based on that information, an adjustment unit that adjusts traffic light colors based on the analysis results, and an emergency acquisition unit that acquires emergency vehicle location information. Finally, it includes a control unit that controls traffic lights. These elements work together. This allows the traffic control system to analyze congestion in real time based on smartphone location information and automatically adjust traffic light colors to optimize traffic flow and quickly support emergency vehicles.

[0029] A traffic control system according to an embodiment includes an acquisition unit, an analysis unit, an adjustment unit, an emergency acquisition unit, and a control unit. The acquisition unit acquires location information of a smartphone. The smartphone location information includes, but is not limited to, GPS data and Wi-Fi location information. For example, the acquisition unit acquires GPS data of the smartphone in real time. The acquisition unit can also acquire location information using Wi-Fi location information. For example, the acquisition unit identifies a location based on Wi-Fi connection information of the smartphone. The acquisition unit can also acquire location information using Bluetooth (registered trademark). For example, the acquisition unit identifies a location based on a Bluetooth signal from the smartphone. The analysis unit analyzes a traffic congestion situation based on the location information acquired by the acquisition unit. Analysis of the traffic congestion situation includes, but is not limited to, vehicle speed and traffic volume. For example, the analysis unit analyzes the traffic congestion situation based on vehicle speed data. The analysis unit can also analyze the traffic congestion situation based on traffic volume data. For example, the analysis unit analyzes traffic volume on a specific road to predict the occurrence of traffic congestion. Furthermore, the analysis unit can predict traffic congestion based on past traffic data. For example, the analysis unit refers to past traffic data and predicts the current traffic congestion. The adjustment unit adjusts the color of the traffic light based on the results of the analysis by the analysis unit. Adjustment of the traffic light color includes, for example, but is not limited to, the display time of each color, red, yellow, and green. For example, the adjustment unit extends the green light time when there is heavy traffic. The adjustment unit can also shorten the red light time when there is congestion. For example, the adjustment unit adjusts the color of the traffic light to alleviate congestion. Furthermore, the adjustment unit can switch the traffic light to green when an emergency vehicle approaches. For example, the adjustment unit switches the traffic light to green based on the location information of the emergency vehicle. The emergency acquisition unit acquires location information of the emergency vehicle. The location information of the emergency vehicle includes, for example, GPS data, Wi-Fi location information, etc., but is not limited to these examples. For example, the emergency acquisition unit acquires GPS data of the emergency vehicle in real time. The emergency acquisition unit can also acquire the location information of the emergency vehicle using Wi-Fi location information.For example, the emergency acquisition unit identifies the location based on Wi-Fi connection information of the emergency vehicle. Furthermore, the emergency acquisition unit can also acquire location information of the emergency vehicle using Bluetooth. For example, the emergency acquisition unit identifies the location based on the Bluetooth signal of the emergency vehicle. The control unit controls a traffic light based on the information acquired by the emergency acquisition unit. Traffic light control includes, but is not limited to, switching to a green light or a red light. For example, the control unit switches the traffic light to a green light when the emergency vehicle approaches. Furthermore, the control unit can also return the traffic light to its original state after the emergency vehicle has passed. For example, the control unit switches the traffic light back to red after the emergency vehicle has passed. As a result, the traffic control system according to the embodiment can analyze traffic congestion conditions in real time based on the smartphone's location information and automatically adjust the color of traffic lights to optimize traffic flow and quickly support the passage of emergency vehicles.

[0030] The acquisition unit can acquire smartphone location information in real time. Acquisition of real-time location information includes, but is not limited to, data update frequency and delay time. For example, the acquisition unit acquires GPS data from the smartphone in real time. For example, the acquisition unit can update the smartphone's location information every second. The acquisition unit can also acquire location information in real time using Wi-Fi location information. For example, the acquisition unit identifies the location based on the smartphone's Wi-Fi connection information and updates the location in real time. The acquisition unit can also acquire location information in real time using Bluetooth. For example, the acquisition unit identifies the location based on the smartphone's Bluetooth signal and updates the location in real time. By acquiring the smartphone's location information in real time, the latest traffic conditions can be grasped. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input the smartphone's location information into AI and cause the AI ​​to update the location information in real time.

[0031] The analysis unit can use AI to analyze the traffic congestion situation based on the location information acquired by the acquisition unit. Examples of analysis of traffic congestion situations using AI include, but are not limited to, machine learning algorithms and neural networks. The analysis unit analyzes the traffic congestion situation using, for example, a machine learning algorithm. For example, the analysis unit predicts the traffic congestion situation based on vehicle speed data. The analysis unit can also analyze the traffic congestion situation using a neural network. For example, the analysis unit predicts the occurrence of traffic congestion based on traffic volume data. Furthermore, the analysis unit can predict the traffic congestion situation based on past traffic data. For example, the analysis unit refers to past traffic data and predicts the current traffic congestion situation. This improves the accuracy of the analysis of the traffic congestion situation by using AI. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI. For example, the analysis unit can input the location information acquired by the acquisition unit to the generation AI and cause the generation AI to analyze the traffic congestion situation.

[0032] The adjustment unit can adjust the color of the traffic light using AI based on the results of the analysis by the analysis unit. Examples of traffic light color adjustment using AI include, but are not limited to, machine learning algorithms and neural networks. The adjustment unit adjusts the color of the traffic light using, for example, a machine learning algorithm. For example, the adjustment unit extends the green light time when there is heavy traffic. The adjustment unit can also adjust the color of the traffic light using a neural network. For example, the adjustment unit shortens the red light time when there is congestion. Furthermore, the adjustment unit can switch the traffic light to green when an emergency vehicle is approaching. For example, the adjustment unit switches the traffic light to green based on the location information of the emergency vehicle. This improves the accuracy of traffic light color adjustment by using AI. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI. For example, the adjustment unit can input the results of the analysis by the analysis unit to the generation AI and cause the generation AI to perform traffic light color adjustment.

[0033] The emergency acquisition unit can acquire the location information of the emergency vehicle in real time. Acquiring real-time location information includes, but is not limited to, data update frequency and delay time, for example. The emergency acquisition unit, for example, acquires GPS data of the emergency vehicle in real time. For example, the emergency acquisition unit can update the location information of the emergency vehicle every second. The emergency acquisition unit can also acquire the location information of the emergency vehicle in real time using Wi-Fi location information. For example, the emergency acquisition unit identifies the location based on the Wi-Fi connection information of the emergency vehicle and updates the location in real time. The emergency acquisition unit can also acquire the location information of the emergency vehicle in real time using Bluetooth. For example, the emergency acquisition unit identifies the location based on the Bluetooth signal of the emergency vehicle and updates the location in real time. This enables rapid response by acquiring the location information of the emergency vehicle in real time. Some or all of the above-described processing in the emergency acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the emergency acquisition unit may input the location information of the emergency vehicle to AI and cause the AI ​​to update the location information in real time.

[0034] The control unit can switch the traffic light to green based on the information acquired by the emergency acquisition unit. Switching the traffic light to green includes, for example, but is not limited to, the green light display time and switching conditions. For example, the control unit switches the traffic light to green when an emergency vehicle approaches. For example, the control unit switches the traffic light to green based on the location information of the emergency vehicle. The control unit can also return the traffic light to its original state after the emergency vehicle has passed. For example, the control unit returns the traffic light to red after the emergency vehicle has passed. This allows the emergency vehicle to pass quickly. Some or all of the above-described processing in the control unit may be performed using, or without using, AI. For example, the control unit can input the information acquired by the emergency acquisition unit to AI and have the AI ​​control the traffic light.

[0035] The acquisition unit can analyze the user's past movement history and select the optimal acquisition method. The acquisition unit can, for example, adjust the frequency of location information acquisition based on places the user has frequently visited in the past. For example, the acquisition unit can analyze the user's past movement history and increase the frequency of location information acquisition for frequently visited places. The acquisition unit can also analyze the user's past movement patterns and prioritize acquisition of location information during specific time periods. For example, the acquisition unit prioritizes acquisition of location information during specific time periods based on the user's past movement history. Furthermore, the acquisition unit can optimize location information acquisition for specific routes based on the user's past movement history. For example, the acquisition unit optimizes location information acquisition for specific routes based on the user's past movement history. This enables efficient information collection by selecting the optimal location information acquisition method based on the past movement history. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's past movement history data into AI and have the AI ​​select the optimal acquisition method.

[0036] When acquiring location information, the acquisition unit can perform filtering based on the user's current traffic conditions and areas of interest. For example, when the user is stuck in traffic, the acquisition unit prioritizes acquiring traffic congestion information and updates it in real time. For example, the acquisition unit can prioritize acquiring traffic congestion information based on the user's current traffic conditions. Furthermore, when the user is in a tourist destination, the acquisition unit can prioritize acquiring location information of tourist spots. For example, the acquisition unit prioritizes acquiring location information of tourist spots based on the user's current areas of interest. Furthermore, when the user is in a shopping area, the acquisition unit can prioritize acquiring location information of stores. For example, the acquisition unit prioritizes acquiring store information in the shopping area based on the user's current areas of interest. This allows for the provision of more useful information by prioritizing the acquisition of information according to the user's current situation and interests. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input data on the user's current traffic conditions and areas of interest into AI and have the AI ​​perform filtering.

[0037] When acquiring location information, the acquisition unit can prioritize acquiring highly relevant information taking into account the user's geographical location information. For example, when the user is in an urban area, the acquisition unit prioritizes acquiring traffic information. For example, the acquisition unit can prioritize acquiring traffic information in urban areas based on the user's geographical location information. Furthermore, when the user is in a suburban area, the acquisition unit can prioritize acquiring information on the natural environment. For example, the acquisition unit prioritizes acquiring information on the natural environment in suburban areas based on the user's geographical location information. Furthermore, when the user is in a tourist destination, the acquisition unit can prioritize acquiring information on tourist spots. For example, the acquisition unit prioritizes acquiring information on tourist spots in tourist destinations based on the user's geographical location information. This allows for more useful information to be provided by prioritizing acquisition of highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's geographical location information to AI and cause the AI ​​to acquire highly relevant information.

[0038] The acquisition unit may analyze the user's social media activity when acquiring location information and acquire related information. The acquisition unit, for example, may prioritize acquiring information about places where the user has checked in on social media. For example, the acquisition unit may prioritize acquiring information about checked-in places based on the user's social media activity. The acquisition unit may also prioritize acquiring information about places shared by the user on social media. For example, the acquisition unit may prioritize acquiring information about shared places based on the user's social media activity. The acquisition unit may also prioritize acquiring information about places the user follows on social media. For example, the acquisition unit may prioritize acquiring information about followed places based on the user's social media activity. This allows for more useful information to be provided by acquiring related information based on the user's social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, or without, AI. For example, the acquisition unit may input the user's social media activity data into AI and cause the AI ​​to acquire related information.

[0039] When analyzing congestion, the analysis unit can predict current congestion by referring to past traffic data. The analysis unit, for example, predicts current congestion conditions based on past traffic data. For example, the analysis unit can predict current congestion conditions by referring to past traffic data. The analysis unit can also predict congestion during a specific time period based on past traffic data. For example, the analysis unit can predict congestion during a specific time period by referring to past traffic data. Furthermore, the analysis unit can predict congestion on a specific route based on past traffic data. For example, the analysis unit can predict congestion on a specific route by referring to past traffic data. This enables more accurate congestion prediction by predicting current congestion based on past traffic data. 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 can input past traffic data into AI and have the AI ​​perform current congestion prediction.

[0040] The analysis unit can apply different analysis algorithms for each traffic category when analyzing congestion. The analysis unit, for example, applies an analysis algorithm specialized for automobile traffic data. For example, the analysis unit can apply a specialized analysis algorithm based on automobile traffic data. The analysis unit can also apply an analysis algorithm specialized for pedestrian traffic data. For example, the analysis unit applies a specialized analysis algorithm based on pedestrian traffic data. The analysis unit can also apply an analysis algorithm specialized for public transportation traffic data. For example, the analysis unit applies a specialized analysis algorithm based on public transportation traffic data. This improves analysis accuracy by applying an appropriate analysis algorithm for each traffic category. 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 data for each traffic category into AI and have the AI ​​apply the analysis algorithm.

[0041] The analysis unit can determine the analysis priority based on the time of submission of traffic data during congestion analysis. The analysis unit determines the analysis priority based on, for example, the time period when traffic data is submitted. For example, the analysis unit can determine the analysis priority according to the time period when the traffic data is submitted based on the time of submission. The analysis unit can also determine the analysis priority based on the day of the week when the traffic data is submitted. For example, the analysis unit determines the analysis priority according to the day of the week when the traffic data is submitted based on the time of submission. The analysis unit can also determine the analysis priority based on the season when the traffic data is submitted. For example, the analysis unit determines the analysis priority according to the season when the traffic data is submitted based on the time of submission. This enables efficient analysis by determining the analysis priority based on the time of submission of traffic data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the time of submission of traffic data into AI and have the AI ​​determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on traffic relevance during congestion analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of traffic data. For example, the analysis unit can prioritize analysis of highly relevant data based on the relevance of traffic data. The analysis unit can also adjust the order of analysis based on the importance of traffic data. For example, the analysis unit prioritizes analysis of highly important data based on the importance of traffic data. The analysis unit can also adjust the order of analysis based on the urgency of traffic data. For example, the analysis unit prioritizes analysis of highly urgent data based on the urgency of traffic data. This enables efficient analysis by adjusting the order of analysis based on the relevance of traffic data. 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 can input the relevance of traffic data to AI and have the AI ​​adjust the order of analysis.

[0043] The adjustment unit can adjust the level of detail of the adjustment based on the importance of traffic when adjusting the color of a traffic light. The adjustment unit, for example, adjusts the color of a traffic light in detail based on important traffic data. For example, the adjustment unit can adjust the color of a traffic light in detail for important traffic data based on the importance of traffic. The adjustment unit can also adjust the color of a traffic light simply based on general traffic data. For example, the adjustment unit adjusts the color of a traffic light simply for general traffic data based on the importance of traffic. The adjustment unit can also quickly adjust the color of a traffic light based on urgent traffic data. For example, the adjustment unit quickly adjusts the color of a traffic light for urgent traffic data based on the importance of traffic. This enables efficient traffic control by adjusting the level of detail of the color adjustment of a traffic light based on the importance of traffic. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input traffic importance data to AI and have the AI ​​perform the level of detail of the color adjustment of a traffic light.

[0044] The adjustment unit can apply different adjustment algorithms depending on the traffic category when adjusting the color of a traffic light. The adjustment unit, for example, applies an adjustment algorithm specialized for automobile traffic data. For example, the adjustment unit can apply a specialized adjustment algorithm based on automobile traffic data. The adjustment unit can also apply a specialized adjustment algorithm for pedestrian traffic data. For example, the adjustment unit applies a specialized adjustment algorithm based on pedestrian traffic data. The adjustment unit can also apply a specialized adjustment algorithm for public transportation traffic data. For example, the adjustment unit applies a specialized adjustment algorithm based on public transportation traffic data. This improves the accuracy of traffic light color adjustment by applying an appropriate adjustment algorithm according to the traffic category. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data for each traffic category into AI and cause the AI ​​to apply the adjustment algorithm.

[0045] When adjusting the color of a traffic light, the adjustment unit can determine the priority of adjustment based on the time of submission of traffic data. The adjustment unit can determine the priority of color adjustment of a traffic light based on, for example, the time period when traffic data is submitted. For example, the adjustment unit can determine the priority of color adjustment of a traffic light according to the time period when the traffic data is submitted. The adjustment unit can also determine the priority of color adjustment of a traffic light based on the day of the week when the traffic data is submitted. For example, the adjustment unit can determine the priority of color adjustment of a traffic light according to the day of the week when the traffic data is submitted based on the time of submission of the traffic data. Furthermore, the adjustment unit can also determine the priority of color adjustment of a traffic light based on the season when the traffic data is submitted. For example, the adjustment unit can determine the priority of color adjustment of a traffic light according to the season when the traffic data is submitted based on the time of submission of the traffic data. This enables efficient traffic control by determining the priority of color adjustment of a traffic light based on the time of submission of the traffic data. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or without AI. For example, the adjustment unit can input the time of submission of traffic data to AI and have the AI ​​execute the priority of color adjustment of a traffic light.

[0046] The adjustment unit can adjust the order of adjustment based on traffic relevance when adjusting the colors of traffic lights. The adjustment unit adjusts the order of color adjustment of traffic lights based on, for example, the relevance of traffic data. For example, the adjustment unit can prioritize adjustment of highly relevant data based on the relevance of traffic data. The adjustment unit can also adjust the order of color adjustment of traffic lights based on the importance of traffic data. For example, the adjustment unit prioritizes adjustment of highly important data based on the importance of traffic data. The adjustment unit can also adjust the order of color adjustment of traffic lights based on the urgency of traffic data. For example, the adjustment unit prioritizes adjustment of highly urgent data based on the urgency of traffic data. This enables efficient traffic control by adjusting the order of color adjustment of traffic lights based on the relevance of traffic data. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the relevance of traffic data to AI and have the AI ​​execute the order of color adjustment of traffic lights.

[0047] The emergency acquisition unit can analyze the past movement history of the emergency vehicle and select the optimal acquisition method. The emergency acquisition unit can adjust the frequency of location information acquisition based on, for example, routes that the emergency vehicle has frequently traveled in the past. For example, the emergency acquisition unit can analyze the past movement history of the emergency vehicle and increase the frequency of location information acquisition for frequently traveled routes. The emergency acquisition unit can also analyze the past movement patterns of the emergency vehicle and prioritize acquisition of location information during specific time periods. For example, the emergency acquisition unit prioritizes acquisition of location information during specific time periods based on the past movement history of the emergency vehicle. Furthermore, the emergency acquisition unit can optimize location information acquisition for specific routes based on the past movement history of the emergency vehicle. For example, the emergency acquisition unit optimizes location information acquisition for specific routes based on the past movement history. This enables efficient information collection by selecting the optimal location information acquisition method based on the past movement history. Some or all of the above-described processing in the emergency acquisition unit can be performed using, for example, AI, or without AI. For example, the emergency acquisition unit can input the past movement history data of emergency vehicles into the AI ​​and have the AI ​​select the optimal acquisition method.

[0048] The emergency acquisition unit can perform filtering based on current traffic conditions and areas of interest when acquiring the location information of the emergency vehicle. For example, if the emergency vehicle is stuck in traffic, the emergency acquisition unit prioritizes acquiring traffic information and updates it in real time. For example, the emergency acquisition unit can prioritize acquiring traffic information based on the current traffic conditions of the emergency vehicle. Furthermore, if the emergency vehicle is in a specific area, the emergency acquisition unit can also prioritize acquiring traffic information for that area. For example, the emergency acquisition unit prioritizes acquiring traffic information for that area based on the current area of ​​interest of the emergency vehicle. Furthermore, if the emergency vehicle is traveling along a specific route, the emergency acquisition unit can also prioritize acquiring traffic information for that route. For example, the emergency acquisition unit prioritizes acquiring traffic information for that route based on the current area of ​​interest of the emergency vehicle. This allows for the provision of more useful information by prioritizing the acquisition of information according to the emergency vehicle's current situation and interests. Some or all of the above-described processing in the emergency acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the emergency acquisition unit can input data on the current traffic situation and areas of interest of emergency vehicles into the AI ​​and have the AI ​​perform filtering.

[0049] When acquiring the location information of an emergency vehicle, the emergency acquisition unit can prioritize acquiring highly relevant information taking into consideration the geographical location information. For example, if the emergency vehicle is in an urban area, the emergency acquisition unit prioritizes acquiring traffic information. For example, the emergency acquisition unit can prioritize acquiring traffic information in urban areas based on the geographical location information of the emergency vehicle. Furthermore, if the emergency vehicle is in a suburban area, the emergency acquisition unit can prioritize acquiring information about the natural environment. For example, the emergency acquisition unit prioritizes acquiring information about the natural environment in suburban areas based on the geographical location information of the emergency vehicle. Furthermore, if the emergency vehicle is in a specific area, the emergency acquisition unit can prioritize acquiring traffic information for that area. For example, the emergency acquisition unit prioritizes acquiring traffic information for that area based on the geographical location information of the emergency vehicle. This prioritizes acquiring highly relevant information based on the geographical location information of the emergency vehicle, making it possible to provide more useful information. Some or all of the above-described processing in the emergency acquisition unit may be performed, for example, using AI or without AI. For example, the emergency acquisition unit can input the geographical location information of emergency vehicles into the AI ​​and have the AI ​​acquire highly relevant information.

[0050] The emergency acquisition unit can analyze social media activity and acquire related information when acquiring location information of the emergency vehicle. For example, the emergency acquisition unit prioritizes acquiring information on locations where the emergency vehicle has checked in on social media. For example, the emergency acquisition unit can prioritize acquiring information on locations where the emergency vehicle has checked in based on the social media activity of the emergency vehicle. The emergency acquisition unit can also prioritize acquiring information on locations shared by the emergency vehicle on social media. For example, the emergency acquisition unit prioritizes acquiring information on shared locations based on the social media activity of the emergency vehicle. Furthermore, the emergency acquisition unit can also prioritize acquiring information on locations followed by the emergency vehicle on social media. For example, the emergency acquisition unit prioritizes acquiring information on followed locations based on the social media activity of the emergency vehicle. This enables more useful information to be provided by acquiring related information based on the social media activity of the emergency vehicle. Some or all of the above-described processing in the emergency acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the emergency acquisition unit may input social media activity data of the emergency vehicle into AI and cause the AI ​​to acquire related information.

[0051] When controlling a traffic light, the control unit can select an appropriate control method by referring to past control data. The control unit, for example, selects an optimal traffic light control method based on past control data. For example, the control unit can select an optimal traffic light control method by referring to past control data. The control unit can also select an optimal traffic light control method for a specific time period based on past control data. For example, the control unit can select an optimal traffic light control method for a specific time period by referring to past control data. The control unit can also select an optimal traffic light control method for a specific route based on past control data. For example, the control unit can select an optimal traffic light control method for a specific route by referring to past control data. This enables efficient traffic control by selecting an optimal traffic light control method based on past control data. Some or all of the above-described processing in the control unit may be performed using, or without, AI. For example, the control unit can input past control data into AI and have the AI ​​select an optimal traffic light control method.

[0052] When controlling a traffic light, the control unit can customize the control means based on the current traffic conditions. The control unit, for example, customizes the traffic light control means based on the current traffic conditions. For example, the control unit can customize the traffic light control means based on the current traffic conditions. The control unit can also customize the traffic light control means optimal for a specific time period based on the current traffic conditions. For example, the control unit customizes the traffic light control means optimal for a specific time period based on the current traffic conditions. The control unit can also customize the traffic light control means optimal for a specific route based on the current traffic conditions. For example, the control unit customizes the traffic light control means optimal for a specific route based on the current traffic conditions. This enables efficient traffic control by customizing the traffic light control means based on the current traffic conditions. Some or all of the above-described processing in the control unit may be performed using, or without, AI. For example, the control unit can input current traffic condition data into AI and have the AI ​​customize the traffic light control means.

[0053] When controlling a traffic light, the control unit can select an appropriate control method by taking geographical location information into consideration. The control unit selects the optimal traffic light control method based on, for example, the geographical location information. For example, the control unit can select the optimal traffic light control method by referring to the geographical location information. The control unit can also select the optimal traffic light control method for a specific time period based on the geographical location information. For example, the control unit selects the optimal traffic light control method for a specific time period by referring to the geographical location information. Furthermore, the control unit can also select the optimal traffic light control method for a specific route based on the geographical location information. For example, the control unit selects the optimal traffic light control method for a specific route by referring to the geographical location information. This enables efficient traffic control by selecting the optimal traffic light control method based on the geographical location information. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input geographical location information to AI and have the AI ​​select the optimal traffic light control method.

[0054] The control unit can analyze social media activity and propose control measures when controlling traffic lights. The control unit can propose traffic light control measures based on, for example, traffic information on social media. For example, the control unit can analyze social media activity and propose traffic light control measures based on the traffic information. The control unit can also propose traffic light control measures based on event information on social media. For example, the control unit can analyze social media activity and propose traffic light control measures based on the event information. The control unit can also propose traffic light control measures based on emergency information on social media. For example, the control unit can analyze social media activity and propose traffic light control measures based on the emergency information. This enables efficient traffic control by proposing traffic light control measures based on social media activity. Some or all of the above-mentioned processing in the control unit can be performed using, for example, AI, or can be performed without using AI. For example, the control unit can input social media activity data into AI and have the AI ​​execute the proposal of traffic light control measures.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The acquisition unit can monitor the user's health condition and adjust the frequency of acquiring location information based on the health condition. For example, the acquisition unit can monitor the user's heart rate and blood pressure, and increase the frequency of acquiring location information if an abnormality is detected. The acquisition unit can also increase the frequency of acquiring location information when the user is exercising and collect detailed movement data. Furthermore, the acquisition unit can reduce the frequency of acquiring location information when the user is resting and reduce battery consumption. In this way, by adjusting the frequency of acquiring location information according to the user's health condition, more appropriate information can be provided.

[0057] The analysis unit can analyze the user's past driving history and improve the accuracy of traffic congestion predictions based on driving patterns. For example, the analysis unit can predict the occurrence of traffic congestion based on routes that the user has frequently traveled in the past. The analysis unit can also analyze the user's driving style (such as the frequency of sudden braking or sudden acceleration) and reflect this in the traffic congestion prediction. Furthermore, the analysis unit can predict traffic congestion for specific time periods based on the user's past driving time periods. This improves the accuracy of traffic congestion predictions based on the user's driving history, making it possible to provide more accurate information.

[0058] The coordination unit can monitor traffic accident situations in real time and automatically adjust the color of traffic lights when an accident occurs. For example, when an accident occurs, the coordination unit can switch traffic lights around the accident site to red and stop traffic. The coordination unit can also switch traffic lights in the direction of emergency vehicles to green to give priority to the passage of emergency vehicles from the accident site. Furthermore, the coordination unit can analyze the extent of the accident's impact and adjust the color of traffic lights within the affected area. This enables a rapid response when a traffic accident occurs and minimizes traffic disruptions.

[0059] The emergency acquisition unit monitors the remaining fuel level of the emergency vehicle and can provide location information of the nearest gas station when the fuel level is low. For example, when the remaining fuel level of the emergency vehicle falls below a certain level, the emergency acquisition unit can provide location information of the nearest gas station in real time. The emergency acquisition unit can also suggest an optimal refueling route based on the remaining fuel level of the emergency vehicle. Furthermore, when the remaining fuel level of the emergency vehicle is very low, the emergency acquisition unit can notify the need for emergency refueling. This allows for efficient fuel management of the emergency vehicle and smooth emergency response.

[0060] The control unit can acquire weather information and adjust the traffic light control method during bad weather. For example, the control unit can extend the green light time of a traffic light during bad weather such as heavy rain or snow to ensure smooth traffic flow. The control unit can also extend the red light time of a traffic light during fog to reduce the risk of traffic accidents. Furthermore, the control unit can temporarily suspend traffic light control during strong winds to ensure traffic safety. In this way, traffic safety is improved by adjusting the traffic light control method based on weather information.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The acquisition unit acquires the smartphone's location information. The smartphone's location information includes, for example, GPS data, Wi-Fi location information, Bluetooth signals, etc. The acquisition unit acquires this data in real time and identifies the location. Step 2: The analysis unit analyzes the traffic congestion situation based on the location information acquired by the acquisition unit. The analysis of the traffic congestion situation includes vehicle speed, traffic volume, past traffic data, etc. The analysis unit predicts the current traffic congestion situation based on this data. Step 3: The adjustment unit adjusts the traffic light color based on the results of the analysis by the analysis unit. Adjusting the traffic light color includes adjusting the display time of each color, red, yellow, and green. For example, if there is heavy traffic, the green light time is extended, and if there is congestion, the red light time is shortened. Step 4: The emergency acquisition unit acquires the location information of the emergency vehicle. The location information of the emergency vehicle includes, for example, GPS data, Wi-Fi location information, Bluetooth signals, etc. The emergency acquisition unit acquires this data in real time and identifies the location. Step 5: The control unit controls the traffic light based on the information acquired by the emergency acquisition unit. Traffic light control includes switching the light to green, switching the light to red, etc. For example, when an emergency vehicle approaches, the control unit switches the traffic light to green, and after the vehicle passes, the control unit returns the traffic light to its original state.

[0063] (Example 2) A traffic control system according to an embodiment of the present invention links smartphone location information with autonomous vehicles to analyze and predict congestion in real time. This traffic control system analyzes current traffic conditions and predicts congestion based on location information acquired from the smartphone. It then automatically adjusts the green / red color of traffic lights based on the analysis results to optimize traffic flow. For example, when traffic volume is high, the green light time is extended to alleviate congestion. Furthermore, when an emergency vehicle approaches a traffic light, the traffic light automatically switches to green based on the emergency vehicle's location information. This supports the rapid passage of emergency vehicles. This system enables the construction of a traffic infrastructure optimized for autonomous vehicles and improves traffic efficiency and safety. For example, it includes an acquisition unit that acquires smartphone location information, an analysis unit that analyzes congestion based on that information, an adjustment unit that adjusts traffic light colors based on the analysis results, and an emergency acquisition unit that acquires emergency vehicle location information. Finally, it includes a control unit that controls traffic lights. These elements work together. This allows the traffic control system to analyze congestion in real time based on smartphone location information and automatically adjust traffic light colors to optimize traffic flow and quickly support emergency vehicles.

[0064] A traffic control system according to an embodiment includes an acquisition unit, an analysis unit, an adjustment unit, an emergency acquisition unit, and a control unit. The acquisition unit acquires location information of a smartphone. The smartphone's location information includes, but is not limited to, GPS data and Wi-Fi location information. For example, the acquisition unit acquires GPS data from the smartphone in real time. The acquisition unit can also acquire location information using Wi-Fi location information. For example, the acquisition unit identifies a location based on the smartphone's Wi-Fi connection information. The acquisition unit can also acquire location information using Bluetooth. For example, the acquisition unit identifies a location based on the smartphone's Bluetooth signal. The analysis unit analyzes a traffic congestion situation based on the location information acquired by the acquisition unit. Analysis of the traffic congestion situation includes, but is not limited to, vehicle speed and traffic volume. For example, the analysis unit analyzes the traffic congestion situation based on vehicle speed data. The analysis unit can also analyze the traffic congestion situation based on traffic volume data. For example, the analysis unit analyzes traffic volume on a specific road to predict the occurrence of a traffic congestion. The analysis unit can also predict a traffic congestion situation based on past traffic data. For example, the analysis unit refers to past traffic data and predicts the current congestion situation. The adjustment unit adjusts the color of the traffic light based on the results of the analysis by the analysis unit. Adjustment of the traffic light color includes, for example, but is not limited to, the display time of each color, red, yellow, and green. For example, the adjustment unit extends the green light time when there is heavy traffic. The adjustment unit can also shorten the red light time when there is congestion. For example, the adjustment unit adjusts the color of the traffic light to alleviate congestion. Furthermore, the adjustment unit can switch the traffic light to green when an emergency vehicle approaches. For example, the adjustment unit switches the traffic light to green based on location information of the emergency vehicle. The emergency acquisition unit acquires location information of the emergency vehicle. Location information of the emergency vehicle includes, for example, GPS data, Wi-Fi location information, etc., but is not limited to these examples. For example, the emergency acquisition unit acquires GPS data of the emergency vehicle in real time. The emergency acquisition unit can also acquire location information of the emergency vehicle using Wi-Fi location information.For example, the emergency acquisition unit identifies the location based on Wi-Fi connection information of the emergency vehicle. Furthermore, the emergency acquisition unit can also acquire location information of the emergency vehicle using Bluetooth. For example, the emergency acquisition unit identifies the location based on the Bluetooth signal of the emergency vehicle. The control unit controls a traffic light based on the information acquired by the emergency acquisition unit. Traffic light control includes, but is not limited to, switching to a green light or a red light. For example, the control unit switches the traffic light to a green light when the emergency vehicle approaches. Furthermore, the control unit can also return the traffic light to its original state after the emergency vehicle has passed. For example, the control unit switches the traffic light back to red after the emergency vehicle has passed. As a result, the traffic control system according to the embodiment can analyze traffic congestion conditions in real time based on the smartphone's location information and automatically adjust the color of traffic lights to optimize traffic flow and quickly support the passage of emergency vehicles.

[0065] The acquisition unit can acquire smartphone location information in real time. Acquisition of real-time location information includes, but is not limited to, data update frequency and delay time. For example, the acquisition unit acquires GPS data from the smartphone in real time. For example, the acquisition unit can update the smartphone's location information every second. The acquisition unit can also acquire location information in real time using Wi-Fi location information. For example, the acquisition unit identifies the location based on the smartphone's Wi-Fi connection information and updates the location in real time. The acquisition unit can also acquire location information in real time using Bluetooth. For example, the acquisition unit identifies the location based on the smartphone's Bluetooth signal and updates the location in real time. By acquiring the smartphone's location information in real time, the latest traffic conditions can be grasped. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input the smartphone's location information into AI and cause the AI ​​to update the location information in real time.

[0066] The analysis unit can use AI to analyze the traffic congestion situation based on the location information acquired by the acquisition unit. Examples of analysis of traffic congestion situations using AI include, but are not limited to, machine learning algorithms and neural networks. The analysis unit analyzes the traffic congestion situation using, for example, a machine learning algorithm. For example, the analysis unit predicts the traffic congestion situation based on vehicle speed data. The analysis unit can also analyze the traffic congestion situation using a neural network. For example, the analysis unit predicts the occurrence of traffic congestion based on traffic volume data. Furthermore, the analysis unit can predict the traffic congestion situation based on past traffic data. For example, the analysis unit refers to past traffic data and predicts the current traffic congestion situation. This improves the accuracy of the analysis of the traffic congestion situation by using AI. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI. For example, the analysis unit can input the location information acquired by the acquisition unit to the generation AI and cause the generation AI to analyze the traffic congestion situation.

[0067] The adjustment unit can adjust the color of the traffic light using AI based on the results of the analysis by the analysis unit. Examples of traffic light color adjustment using AI include, but are not limited to, machine learning algorithms and neural networks. The adjustment unit adjusts the color of the traffic light using, for example, a machine learning algorithm. For example, the adjustment unit extends the green light time when there is heavy traffic. The adjustment unit can also adjust the color of the traffic light using a neural network. For example, the adjustment unit shortens the red light time when there is congestion. Furthermore, the adjustment unit can switch the traffic light to green when an emergency vehicle is approaching. For example, the adjustment unit switches the traffic light to green based on the location information of the emergency vehicle. This improves the accuracy of traffic light color adjustment by using AI. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI. For example, the adjustment unit can input the results of the analysis by the analysis unit to the generation AI and cause the generation AI to perform traffic light color adjustment.

[0068] The emergency acquisition unit can acquire the location information of the emergency vehicle in real time. Acquiring real-time location information includes, but is not limited to, data update frequency and delay time, for example. The emergency acquisition unit, for example, acquires GPS data of the emergency vehicle in real time. For example, the emergency acquisition unit can update the location information of the emergency vehicle every second. The emergency acquisition unit can also acquire the location information of the emergency vehicle in real time using Wi-Fi location information. For example, the emergency acquisition unit identifies the location based on the Wi-Fi connection information of the emergency vehicle and updates the location in real time. The emergency acquisition unit can also acquire the location information of the emergency vehicle in real time using Bluetooth. For example, the emergency acquisition unit identifies the location based on the Bluetooth signal of the emergency vehicle and updates the location in real time. This enables rapid response by acquiring the location information of the emergency vehicle in real time. Some or all of the above-described processing in the emergency acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the emergency acquisition unit may input the location information of the emergency vehicle to AI and cause the AI ​​to update the location information in real time.

[0069] The control unit can switch the traffic light to green based on the information acquired by the emergency acquisition unit. Switching the traffic light to green includes, for example, but is not limited to, the green light display time and switching conditions. For example, the control unit switches the traffic light to green when an emergency vehicle approaches. For example, the control unit switches the traffic light to green based on the location information of the emergency vehicle. The control unit can also return the traffic light to its original state after the emergency vehicle has passed. For example, the control unit returns the traffic light to red after the emergency vehicle has passed. This allows the emergency vehicle to pass quickly. Some or all of the above-described processing in the control unit may be performed using, or without using, AI. For example, the control unit can input the information acquired by the emergency acquisition unit to AI and have the AI ​​control the traffic light.

[0070] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring location information based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can reduce the frequency of acquiring location information to reduce battery consumption. For example, the acquisition unit can estimate the user's emotions and reduce the frequency of acquiring location information when the user is feeling stressed. The acquisition unit can also increase the frequency of acquiring location information and collect detailed movement data when the user is relaxed. For example, the acquisition unit can estimate the user's emotions and increase the frequency of acquiring location information when the user is relaxed. Furthermore, if the user is in a hurry, the acquisition unit can frequently acquire location information in real time to provide quick navigation. For example, the acquisition unit can estimate the user's emotions and frequently acquire location information in real time when the user is in a hurry. This allows the acquisition of necessary information while reducing battery consumption by adjusting the timing of acquiring location information according to the user's emotions. 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 acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input user emotion data into AI and cause the AI ​​to adjust the timing of acquiring location information.

[0071] The acquisition unit can analyze the user's past movement history and select the optimal acquisition method. The acquisition unit can, for example, adjust the frequency of location information acquisition based on places the user has frequently visited in the past. For example, the acquisition unit can analyze the user's past movement history and increase the frequency of location information acquisition for frequently visited places. The acquisition unit can also analyze the user's past movement patterns and prioritize acquisition of location information during specific time periods. For example, the acquisition unit prioritizes acquisition of location information during specific time periods based on the user's past movement history. Furthermore, the acquisition unit can optimize location information acquisition for specific routes based on the user's past movement history. For example, the acquisition unit optimizes location information acquisition for specific routes based on the user's past movement history. This enables efficient information collection by selecting the optimal location information acquisition method based on the past movement history. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's past movement history data into AI and have the AI ​​select the optimal acquisition method.

[0072] The acquisition unit can perform filtering based on the user's current traffic conditions and areas of interest when acquiring location information. For example, when the user is stuck in traffic, the acquisition unit prioritizes acquiring traffic congestion information and updates it in real time. For example, the acquisition unit can prioritize acquiring traffic congestion information based on the user's current traffic conditions. Furthermore, when the user is in a tourist destination, the acquisition unit can prioritize acquiring location information of tourist spots. For example, the acquisition unit prioritizes acquiring location information of tourist spots based on the user's current areas of interest. Furthermore, when the user is in a shopping area, the acquisition unit can prioritize acquiring location information of stores. For example, the acquisition unit prioritizes acquiring store information in the shopping area based on the user's current areas of interest. This allows for the provision of more useful information by prioritizing the acquisition of information according to the user's current situation and interests. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input data on the user's current traffic conditions and areas of interest into AI and have the AI ​​perform filtering.

[0073] The acquisition unit can estimate the user's emotions and determine the priority of location information to be acquired based on the estimated user's emotions. For example, if the user is nervous, the acquisition unit can prioritize acquiring important location information. For example, the acquisition unit can estimate the user's emotions and prioritize acquiring important location information when the user is nervous. The acquisition unit can also prioritize acquiring detailed location information when the user is relaxed. For example, the acquisition unit can estimate the user's emotions and prioritize acquiring detailed location information when the user is relaxed. Furthermore, the acquisition unit can also prioritize acquiring location information of the shortest route when the user is in a hurry. For example, the acquisition unit can estimate the user's emotions and prioritize acquiring location information of the shortest route when the user is in a hurry. This allows more appropriate information to be acquired by prioritizing location information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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 acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input user emotion data into AI and have the AI ​​determine the priority of location information.

[0074] When acquiring location information, the acquisition unit can prioritize acquiring highly relevant information taking into account the user's geographical location information. For example, when the user is in an urban area, the acquisition unit prioritizes acquiring traffic information. For example, the acquisition unit can prioritize acquiring traffic information in urban areas based on the user's geographical location information. Furthermore, when the user is in a suburban area, the acquisition unit can prioritize acquiring information on the natural environment. For example, the acquisition unit prioritizes acquiring information on the natural environment in suburban areas based on the user's geographical location information. Furthermore, when the user is in a tourist destination, the acquisition unit can prioritize acquiring information on tourist spots. For example, the acquisition unit prioritizes acquiring information on tourist spots in tourist destinations based on the user's geographical location information. This allows for more useful information to be provided by prioritizing acquisition of highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's geographical location information to AI and cause the AI ​​to acquire highly relevant information.

[0075] The acquisition unit may analyze the user's social media activity when acquiring location information and acquire related information. The acquisition unit, for example, may prioritize acquiring information about places where the user has checked in on social media. For example, the acquisition unit may prioritize acquiring information about checked-in places based on the user's social media activity. The acquisition unit may also prioritize acquiring information about places shared by the user on social media. For example, the acquisition unit may prioritize acquiring information about shared places based on the user's social media activity. The acquisition unit may also prioritize acquiring information about places the user follows on social media. For example, the acquisition unit may prioritize acquiring information about followed places based on the user's social media activity. This allows for more useful information to be provided by acquiring related information based on the user's social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, or without, AI. For example, the acquisition unit may input the user's social media activity data into AI and cause the AI ​​to acquire related information.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation method of the traffic congestion analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can display a simple, highly visible traffic congestion analysis result. For example, the analysis unit can estimate the user's emotions and display a simple, highly visible traffic congestion analysis result when the user is nervous. The analysis unit can also display a detailed traffic congestion analysis result when the user is relaxed. For example, the analysis unit can estimate the user's emotions and display a detailed traffic congestion analysis result when the user is relaxed. Furthermore, the analysis unit can display a traffic congestion analysis result that focuses on the main points when the user is in a hurry. For example, the analysis unit can estimate the user's emotions and display a traffic congestion analysis result that focuses on the main points when the user is in a hurry. This allows for adjusting the presentation method of the traffic congestion analysis according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI and have the AI ​​adjust the method of expressing traffic congestion analysis.

[0077] When analyzing congestion, the analysis unit can predict current congestion by referring to past traffic data. The analysis unit, for example, predicts current congestion conditions based on past traffic data. For example, the analysis unit can predict current congestion conditions by referring to past traffic data. The analysis unit can also predict congestion during a specific time period based on past traffic data. For example, the analysis unit can predict congestion during a specific time period by referring to past traffic data. Furthermore, the analysis unit can predict congestion on a specific route based on past traffic data. For example, the analysis unit can predict congestion on a specific route by referring to past traffic data. This enables more accurate congestion prediction by predicting current congestion based on past traffic data. 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 can input past traffic data into AI and have the AI ​​perform current congestion prediction.

[0078] The analysis unit can apply different analysis algorithms for each traffic category when analyzing congestion. The analysis unit, for example, applies an analysis algorithm specialized for automobile traffic data. For example, the analysis unit can apply a specialized analysis algorithm based on automobile traffic data. The analysis unit can also apply an analysis algorithm specialized for pedestrian traffic data. For example, the analysis unit applies a specialized analysis algorithm based on pedestrian traffic data. The analysis unit can also apply an analysis algorithm specialized for public transportation traffic data. For example, the analysis unit applies a specialized analysis algorithm based on public transportation traffic data. This improves analysis accuracy by applying an appropriate analysis algorithm for each traffic category. 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 data for each traffic category into AI and have the AI ​​apply the analysis algorithm.

[0079] 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. For example, the analysis unit can estimate the user's emotions and provide a simple, highly visible display method if the user is nervous. The analysis unit can also provide a display method including detailed information if the user is relaxed. For example, the analysis unit can estimate the user's emotions and provide a display method including detailed information if the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points if the user is in a hurry. For example, the analysis unit can estimate the user's emotions and provide a display method that focuses on the main points if the user is in a hurry. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI and have the AI ​​adjust the display method of the analysis results.

[0080] The analysis unit can determine the analysis priority based on the time of submission of traffic data during congestion analysis. The analysis unit determines the analysis priority based on, for example, the time period when traffic data is submitted. For example, the analysis unit can determine the analysis priority according to the time period when the traffic data is submitted based on the time of submission. The analysis unit can also determine the analysis priority based on the day of the week when the traffic data is submitted. For example, the analysis unit determines the analysis priority according to the day of the week when the traffic data is submitted based on the time of submission. The analysis unit can also determine the analysis priority based on the season when the traffic data is submitted. For example, the analysis unit determines the analysis priority according to the season when the traffic data is submitted based on the time of submission. This enables efficient analysis by determining the analysis priority based on the time of submission of traffic data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the time of submission of traffic data into AI and have the AI ​​determine the analysis priority.

[0081] The analysis unit can adjust the order of analysis based on traffic relevance during congestion analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of traffic data. For example, the analysis unit can prioritize analysis of highly relevant data based on the relevance of traffic data. The analysis unit can also adjust the order of analysis based on the importance of traffic data. For example, the analysis unit prioritizes analysis of highly important data based on the importance of traffic data. The analysis unit can also adjust the order of analysis based on the urgency of traffic data. For example, the analysis unit prioritizes analysis of highly urgent data based on the urgency of traffic data. This enables efficient analysis by adjusting the order of analysis based on the relevance of traffic data. 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 can input the relevance of traffic data to AI and have the AI ​​adjust the order of analysis.

[0082] The adjustment unit can estimate the user's emotion and adjust the traffic light color adjustment method based on the estimated user's emotion. For example, if the user is nervous, the adjustment unit adjusts the traffic light color simply. For example, the adjustment unit can estimate the user's emotion and adjust the traffic light color simply if the user is nervous. The adjustment unit can also adjust the traffic light color in detail if the user is relaxed. For example, the adjustment unit can estimate the user's emotion and adjust the traffic light color in detail if the user is relaxed. Furthermore, the adjustment unit can also quickly adjust the traffic light color if the user is in a hurry. For example, the adjustment unit can estimate the user's emotion and quickly adjust the traffic light color if the user is in a hurry. This enables more appropriate traffic control by adjusting the traffic light color adjustment method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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 adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit may input user emotion data into AI and have the AI ​​adjust the color adjustment method for traffic lights.

[0083] The adjustment unit can adjust the level of detail of the adjustment based on the importance of traffic when adjusting the color of a traffic light. The adjustment unit, for example, adjusts the color of a traffic light in detail based on important traffic data. For example, the adjustment unit can adjust the color of a traffic light in detail for important traffic data based on the importance of traffic. The adjustment unit can also adjust the color of a traffic light simply based on general traffic data. For example, the adjustment unit adjusts the color of a traffic light simply for general traffic data based on the importance of traffic. The adjustment unit can also quickly adjust the color of a traffic light based on urgent traffic data. For example, the adjustment unit quickly adjusts the color of a traffic light for urgent traffic data based on the importance of traffic. This enables efficient traffic control by adjusting the level of detail of the color adjustment of a traffic light based on the importance of traffic. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input traffic importance data to AI and have the AI ​​perform the level of detail of the color adjustment of a traffic light.

[0084] The adjustment unit can apply different adjustment algorithms depending on the traffic category when adjusting the color of a traffic light. The adjustment unit, for example, applies an adjustment algorithm specialized for automobile traffic data. For example, the adjustment unit can apply a specialized adjustment algorithm based on automobile traffic data. The adjustment unit can also apply a specialized adjustment algorithm for pedestrian traffic data. For example, the adjustment unit applies a specialized adjustment algorithm based on pedestrian traffic data. The adjustment unit can also apply a specialized adjustment algorithm for public transportation traffic data. For example, the adjustment unit applies a specialized adjustment algorithm based on public transportation traffic data. This improves the accuracy of traffic light color adjustment by applying an appropriate adjustment algorithm according to the traffic category. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data for each traffic category into AI and cause the AI ​​to apply the adjustment algorithm.

[0085] The adjustment unit can estimate the user's emotion and adjust the length of the traffic light color adjustment based on the estimated user's emotion. For example, the adjustment unit shortens the length of the traffic light color adjustment when the user is nervous. For example, the adjustment unit can estimate the user's emotion and shorten the length of the traffic light color adjustment when the user is nervous. The adjustment unit can also lengthen the length of the traffic light color adjustment when the user is relaxed. For example, the adjustment unit can estimate the user's emotion and lengthen the length of the traffic light color adjustment when the user is relaxed. Furthermore, the adjustment unit can quickly adjust the length of the traffic light color adjustment when the user is in a hurry. For example, the adjustment unit estimates the user's emotion and quickly adjusts the length of the traffic light color adjustment when the user is in a hurry. This enables more appropriate traffic control by adjusting the length of the traffic light color adjustment according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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 adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit may input user emotion data into AI and have the AI ​​execute the length of traffic light color adjustment.

[0086] When adjusting the color of a traffic light, the adjustment unit can determine the priority of adjustment based on the time of submission of traffic data. The adjustment unit can determine the priority of color adjustment of a traffic light based on, for example, the time period when traffic data is submitted. For example, the adjustment unit can determine the priority of color adjustment of a traffic light according to the time period when the traffic data is submitted. The adjustment unit can also determine the priority of color adjustment of a traffic light based on the day of the week when the traffic data is submitted. For example, the adjustment unit can determine the priority of color adjustment of a traffic light according to the day of the week when the traffic data is submitted based on the time of submission of the traffic data. Furthermore, the adjustment unit can also determine the priority of color adjustment of a traffic light based on the season when the traffic data is submitted. For example, the adjustment unit can determine the priority of color adjustment of a traffic light according to the season when the traffic data is submitted based on the time of submission of the traffic data. This enables efficient traffic control by determining the priority of color adjustment of a traffic light based on the time of submission of the traffic data. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or without AI. For example, the adjustment unit can input the time of submission of traffic data to AI and have the AI ​​execute the priority of color adjustment of a traffic light.

[0087] The adjustment unit can adjust the order of adjustment based on traffic relevance when adjusting the colors of traffic lights. The adjustment unit adjusts the order of color adjustment of traffic lights based on, for example, the relevance of traffic data. For example, the adjustment unit can prioritize adjustment of highly relevant data based on the relevance of traffic data. The adjustment unit can also adjust the order of color adjustment of traffic lights based on the importance of traffic data. For example, the adjustment unit prioritizes adjustment of highly important data based on the importance of traffic data. The adjustment unit can also adjust the order of color adjustment of traffic lights based on the urgency of traffic data. For example, the adjustment unit prioritizes adjustment of highly urgent data based on the urgency of traffic data. This enables efficient traffic control by adjusting the order of color adjustment of traffic lights based on the relevance of traffic data. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the relevance of traffic data to AI and have the AI ​​execute the order of color adjustment of traffic lights.

[0088] The emergency acquisition unit can estimate the emotion of the emergency vehicle and adjust the timing of acquiring location information based on the estimated emotion. For example, when the emergency vehicle is in an emergency, the emergency acquisition unit can increase the frequency of acquiring location information, enabling a rapid response. For example, the emergency acquisition unit can estimate the emotion of the emergency vehicle and increase the frequency of acquiring location information when the emergency vehicle is in an emergency. Furthermore, when the emergency vehicle is operating normally, the emergency acquisition unit can reduce the frequency of acquiring location information to reduce battery consumption. For example, the emergency acquisition unit can estimate the emotion of the emergency vehicle and reduce the frequency of acquiring location information when the emergency vehicle is operating normally. Furthermore, the emergency acquisition unit can suspend the acquisition of location information when the emergency vehicle is waiting and resume it when necessary. For example, the emergency acquisition unit can estimate the emotion of the emergency vehicle and suspend the acquisition of location information when the emergency vehicle is waiting. This allows a rapid response by adjusting the timing of acquiring location information according to the emotion of the emergency vehicle. Emotion estimation is realized using an emotion estimation function, for example, using 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 emergency acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the emergency acquisition unit may input emotion data of the emergency vehicle to AI and cause the AI ​​to adjust the timing of acquiring location information.

[0089] The emergency acquisition unit can analyze the past movement history of the emergency vehicle and select the optimal acquisition method. The emergency acquisition unit can adjust the frequency of location information acquisition based on, for example, routes that the emergency vehicle has frequently traveled in the past. For example, the emergency acquisition unit can analyze the past movement history of the emergency vehicle and increase the frequency of location information acquisition for frequently traveled routes. The emergency acquisition unit can also analyze the past movement patterns of the emergency vehicle and prioritize acquisition of location information during specific time periods. For example, the emergency acquisition unit prioritizes acquisition of location information during specific time periods based on the past movement history of the emergency vehicle. Furthermore, the emergency acquisition unit can optimize location information acquisition for specific routes based on the past movement history of the emergency vehicle. For example, the emergency acquisition unit optimizes location information acquisition for specific routes based on the past movement history. This enables efficient information collection by selecting the optimal location information acquisition method based on the past movement history. Some or all of the above-described processing in the emergency acquisition unit can be performed using, for example, AI, or without AI. For example, the emergency acquisition unit can input the past movement history data of emergency vehicles into the AI ​​and have the AI ​​select the optimal acquisition method.

[0090] The emergency acquisition unit can perform filtering based on current traffic conditions and areas of interest when acquiring the location information of the emergency vehicle. For example, if the emergency vehicle is stuck in traffic, the emergency acquisition unit prioritizes acquiring traffic information and updates it in real time. For example, the emergency acquisition unit can prioritize acquiring traffic information based on the current traffic conditions of the emergency vehicle. Furthermore, if the emergency vehicle is in a specific area, the emergency acquisition unit can also prioritize acquiring traffic information for that area. For example, the emergency acquisition unit prioritizes acquiring traffic information for that area based on the current area of ​​interest of the emergency vehicle. Furthermore, if the emergency vehicle is traveling along a specific route, the emergency acquisition unit can also prioritize acquiring traffic information for that route. For example, the emergency acquisition unit prioritizes acquiring traffic information for that route based on the current area of ​​interest of the emergency vehicle. This allows for the provision of more useful information by prioritizing the acquisition of information according to the emergency vehicle's current situation and interests. Some or all of the above-described processing in the emergency acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the emergency acquisition unit can input data on the current traffic situation and areas of interest of emergency vehicles into the AI ​​and have the AI ​​perform filtering.

[0091] The emergency acquisition unit can estimate the emotion of the emergency vehicle and determine the priority of location information to be acquired based on the estimated emotion. For example, when the emergency vehicle is in an emergency, the emergency acquisition unit can prioritize acquiring important location information. For example, the emergency acquisition unit can estimate the emotion of the emergency vehicle and prioritize acquiring important location information when the emergency vehicle is in an emergency. The emergency acquisition unit can also prioritize acquiring detailed location information when the emergency vehicle is operating normally. For example, the emergency acquisition unit can estimate the emotion of the emergency vehicle and prioritize acquiring detailed location information when the emergency vehicle is operating normally. Furthermore, the emergency acquisition unit can also prioritize acquiring location information of the shortest route when the emergency vehicle is waiting. For example, the emergency acquisition unit can estimate the emotion of the emergency vehicle and prioritize acquiring location information of the shortest route when the emergency vehicle is waiting. In this way, by determining the priority of location information according to the emotion of the emergency vehicle, more appropriate information can be acquired. Emotion estimation is realized using an emotion estimation function using, 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 emergency acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the emergency acquisition unit may input emotion data of the emergency vehicle to AI and have the AI ​​determine the priority of the location information.

[0092] When acquiring the location information of an emergency vehicle, the emergency acquisition unit can prioritize acquiring highly relevant information taking into consideration the geographical location information. For example, if the emergency vehicle is in an urban area, the emergency acquisition unit prioritizes acquiring traffic information. For example, the emergency acquisition unit can prioritize acquiring traffic information in urban areas based on the geographical location information of the emergency vehicle. Furthermore, if the emergency vehicle is in a suburban area, the emergency acquisition unit can prioritize acquiring information about the natural environment. For example, the emergency acquisition unit prioritizes acquiring information about the natural environment in suburban areas based on the geographical location information of the emergency vehicle. Furthermore, if the emergency vehicle is in a specific area, the emergency acquisition unit can prioritize acquiring traffic information for that area. For example, the emergency acquisition unit prioritizes acquiring traffic information for that area based on the geographical location information of the emergency vehicle. This prioritizes acquiring highly relevant information based on the geographical location information of the emergency vehicle, making it possible to provide more useful information. Some or all of the above-described processing in the emergency acquisition unit may be performed, for example, using AI or without AI. For example, the emergency acquisition unit can input the geographical location information of emergency vehicles into the AI ​​and have the AI ​​acquire highly relevant information.

[0093] The emergency acquisition unit can analyze social media activity and acquire related information when acquiring location information of the emergency vehicle. For example, the emergency acquisition unit prioritizes acquiring information on locations where the emergency vehicle has checked in on social media. For example, the emergency acquisition unit can prioritize acquiring information on locations where the emergency vehicle has checked in based on the social media activity of the emergency vehicle. The emergency acquisition unit can also prioritize acquiring information on locations shared by the emergency vehicle on social media. For example, the emergency acquisition unit prioritizes acquiring information on shared locations based on the social media activity of the emergency vehicle. Furthermore, the emergency acquisition unit can also prioritize acquiring information on locations followed by the emergency vehicle on social media. For example, the emergency acquisition unit prioritizes acquiring information on followed locations based on the social media activity of the emergency vehicle. This enables more useful information to be provided by acquiring related information based on the social media activity of the emergency vehicle. Some or all of the above-described processing in the emergency acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the emergency acquisition unit may input social media activity data of the emergency vehicle into AI and cause the AI ​​to acquire related information.

[0094] The control unit can estimate the emotion of the emergency vehicle and adjust the traffic light control method based on the estimated emotion. For example, when the emergency vehicle is in an emergency, the control unit can quickly switch the traffic light to green. For example, the control unit can estimate the emotion of the emergency vehicle and quickly switch the traffic light to green when the emergency vehicle is in an emergency. The control unit can also control the traffic light normally when the emergency vehicle is operating normally. For example, the control unit can estimate the emotion of the emergency vehicle and control the traffic light normally when the emergency vehicle is operating normally. Furthermore, the control unit can temporarily suspend control of the traffic light when the emergency vehicle is waiting and resume it when necessary. For example, the control unit can temporarily suspend control of the traffic light when the emergency vehicle is waiting. This enables a rapid response by adjusting the traffic light control method according to the emotion of the emergency vehicle. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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 control unit may be performed using AI, or may be performed without AI. For example, the control unit may input emotion data of the emergency vehicle to AI and have the AI ​​adjust the traffic light control method.

[0095] When controlling a traffic light, the control unit can select an appropriate control method by referring to past control data. The control unit, for example, selects an optimal traffic light control method based on past control data. For example, the control unit can select an optimal traffic light control method by referring to past control data. The control unit can also select an optimal traffic light control method for a specific time period based on past control data. For example, the control unit can select an optimal traffic light control method for a specific time period by referring to past control data. The control unit can also select an optimal traffic light control method for a specific route based on past control data. For example, the control unit can select an optimal traffic light control method for a specific route by referring to past control data. This enables efficient traffic control by selecting an optimal traffic light control method based on past control data. Some or all of the above-described processing in the control unit may be performed using, or without, AI. For example, the control unit can input past control data into AI and have the AI ​​select an optimal traffic light control method.

[0096] When controlling a traffic light, the control unit can customize the control means based on the current traffic conditions. The control unit, for example, customizes the traffic light control means based on the current traffic conditions. For example, the control unit can customize the traffic light control means based on the current traffic conditions. The control unit can also customize the traffic light control means optimal for a specific time period based on the current traffic conditions. For example, the control unit customizes the traffic light control means optimal for a specific time period based on the current traffic conditions. The control unit can also customize the traffic light control means optimal for a specific route based on the current traffic conditions. For example, the control unit customizes the traffic light control means optimal for a specific route based on the current traffic conditions. This enables efficient traffic control by customizing the traffic light control means based on the current traffic conditions. Some or all of the above-described processing in the control unit may be performed using, or without, AI. For example, the control unit can input current traffic condition data into AI and have the AI ​​customize the traffic light control means.

[0097] The control unit can estimate the emotion of the emergency vehicle and determine the priority of traffic light control based on the estimated emotion. For example, if the emergency vehicle is in an emergency, the control unit can increase the priority of traffic light control. For example, the control unit can estimate the emotion of the emergency vehicle and increase the priority of traffic light control when the emergency vehicle is in an emergency. The control unit can also maintain the normal priority of traffic light control when the emergency vehicle is operating normally. For example, the control unit can estimate the emotion of the emergency vehicle and maintain the normal priority of traffic light control when the emergency vehicle is operating normally. Furthermore, the control unit can also lower the priority of traffic light control when the emergency vehicle is waiting. For example, the control unit can estimate the emotion of the emergency vehicle and lower the priority of traffic light control when the emergency vehicle is waiting. This enables a rapid response by determining the priority of traffic light control according to the emotion of the emergency vehicle. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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 control unit may be performed using AI, or may be performed without using AI. For example, the control unit may input emotion data of the emergency vehicle to AI and have the AI ​​determine the priority of traffic light control.

[0098] When controlling a traffic light, the control unit can select an appropriate control method by taking geographical location information into consideration. The control unit selects the optimal traffic light control method based on, for example, the geographical location information. For example, the control unit can select the optimal traffic light control method by referring to the geographical location information. The control unit can also select the optimal traffic light control method for a specific time period based on the geographical location information. For example, the control unit selects the optimal traffic light control method for a specific time period by referring to the geographical location information. Furthermore, the control unit can also select the optimal traffic light control method for a specific route based on the geographical location information. For example, the control unit selects the optimal traffic light control method for a specific route by referring to the geographical location information. This enables efficient traffic control by selecting the optimal traffic light control method based on the geographical location information. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input geographical location information to AI and have the AI ​​select the optimal traffic light control method.

[0099] The control unit can analyze social media activity and propose control measures when controlling traffic lights. The control unit can propose traffic light control measures based on, for example, traffic information on social media. For example, the control unit can analyze social media activity and propose traffic light control measures based on the traffic information. The control unit can also propose traffic light control measures based on event information on social media. For example, the control unit can analyze social media activity and propose traffic light control measures based on the event information. The control unit can also propose traffic light control measures based on emergency information on social media. For example, the control unit can analyze social media activity and propose traffic light control measures based on the emergency information. This enables efficient traffic control by proposing traffic light control measures based on social media activity. Some or all of the above-mentioned processing in the control unit can be performed using, for example, AI, or can be performed without using AI. For example, the control unit can input social media activity data into AI and have the AI ​​execute the proposal of traffic light control measures. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, analysis unit, adjustment unit, emergency acquisition unit, and control unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit has a function of acquiring GPS data and Wi-Fi location information of the smart device 14 and is also realized by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes traffic congestion conditions using the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The adjustment unit adjusts the color of traffic lights using the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The emergency acquisition unit has a function of acquiring GPS data and Wi-Fi location information of the smart device 14 and is also realized by the specific processing unit 290 of the data processing device 12. The control unit controls traffic lights using the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, adjustment unit, emergency acquisition unit, and control unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit has a function of acquiring GPS data and Wi-Fi location information of the smart glasses 214, and is also realized by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes traffic congestion conditions by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The adjustment unit adjusts the color of the traffic light by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The emergency acquisition unit has a function of acquiring GPS data and Wi-Fi location information of the smart glasses 214, and is also realized by the specific processing unit 290 of the data processing device 12. The control unit controls the traffic light by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, adjustment unit, emergency acquisition unit, and control unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit has a function of acquiring GPS data and Wi-Fi location information of the headset type terminal 314, and is also realized by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the congestion situation by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The adjustment unit adjusts the color of the traffic light by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The emergency acquisition unit has a function of acquiring GPS data and Wi-Fi location information of the headset type terminal 314, and is also realized by the specific processing unit 290 of the data processing device 12. The control unit controls the traffic light by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, adjustment unit, emergency acquisition unit, and control unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit has a function of acquiring GPS data and Wi-Fi location information of the robot 414, and is also realized by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the traffic congestion situation by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The adjustment unit adjusts the color of the traffic light by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The emergency acquisition unit has a function of acquiring GPS data and Wi-Fi location information of the robot 414, and is also realized by the specific processing unit 290 of the data processing device 12. The control unit controls the traffic light by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The acquisition unit can monitor the user's health condition and adjust the frequency of acquiring location information based on the health condition. For example, the acquisition unit can monitor the user's heart rate and blood pressure, and increase the frequency of acquiring location information if an abnormality is detected. The acquisition unit can also increase the frequency of acquiring location information when the user is exercising and collect detailed movement data. Furthermore, the acquisition unit can reduce the frequency of acquiring location information when the user is resting and reduce battery consumption. In this way, by adjusting the frequency of acquiring location information according to the user's health condition, more appropriate information can be provided.

[0102] The analysis unit can analyze the user's past driving history and improve the accuracy of traffic congestion predictions based on driving patterns. For example, the analysis unit can predict the occurrence of traffic congestion based on routes that the user has frequently traveled in the past. The analysis unit can also analyze the user's driving style (such as the frequency of sudden braking or sudden acceleration) and reflect this in the traffic congestion prediction. Furthermore, the analysis unit can predict traffic congestion for specific time periods based on the user's past driving time periods. This improves the accuracy of traffic congestion predictions based on the user's driving history, making it possible to provide more accurate information.

[0103] The coordination unit can monitor traffic accident situations in real time and automatically adjust the color of traffic lights when an accident occurs. For example, when an accident occurs, the coordination unit can switch traffic lights around the accident site to red and stop traffic. The coordination unit can also switch traffic lights in the direction of emergency vehicles to green to give priority to the passage of emergency vehicles from the accident site. Furthermore, the coordination unit can analyze the extent of the accident's impact and adjust the color of traffic lights within the affected area. This enables a rapid response when a traffic accident occurs and minimizes traffic disruptions.

[0104] The emergency acquisition unit monitors the remaining fuel level of the emergency vehicle and can provide location information of the nearest gas station when the fuel level is low. For example, when the remaining fuel level of the emergency vehicle falls below a certain level, the emergency acquisition unit can provide location information of the nearest gas station in real time. The emergency acquisition unit can also suggest an optimal refueling route based on the remaining fuel level of the emergency vehicle. Furthermore, when the remaining fuel level of the emergency vehicle is very low, the emergency acquisition unit can notify the need for emergency refueling. This allows for efficient fuel management of the emergency vehicle and smooth emergency response.

[0105] The control unit can acquire weather information and adjust the traffic light control method during bad weather. For example, the control unit can extend the green light time of a traffic light during bad weather such as heavy rain or snow to ensure smooth traffic flow. The control unit can also extend the red light time of a traffic light during fog to reduce the risk of traffic accidents. Furthermore, the control unit can temporarily suspend traffic light control during strong winds to ensure traffic safety. In this way, traffic safety is improved by adjusting the traffic light control method based on weather information.

[0106] The acquisition unit can estimate the user's emotions and adjust the frequency of acquiring location information based on the estimated user's emotions. For example, if the user is feeling stressed, the acquisition unit can reduce the frequency of acquiring location information to reduce battery consumption. Also, if the user is relaxed, the acquisition unit can increase the frequency of acquiring location information to collect detailed movement data. Furthermore, if the user is in a hurry, the acquisition unit can frequently acquire location information in real time to provide quick navigation. In this way, by adjusting the frequency of acquiring location information according to the user's emotions, it is possible to acquire necessary information while reducing battery consumption.

[0107] The analysis unit can estimate the user's emotions and adjust the way the traffic congestion analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can display a simple, highly visible traffic congestion analysis result. If the user is relaxed, the analysis unit can also display a detailed traffic congestion analysis result. Furthermore, if the user is in a hurry, the analysis unit can display a traffic congestion analysis result that focuses on the main points. This allows the user to provide more appropriate information by adjusting the way the traffic congestion analysis is presented based on the user's emotions.

[0108] The adjustment unit can estimate the user's emotions and adjust the traffic light color adjustment method based on the estimated user's emotions. For example, the adjustment unit can simply adjust the traffic light color when the user is nervous. The adjustment unit can also finely adjust the traffic light color when the user is relaxed. Furthermore, the adjustment unit can quickly adjust the traffic light color when the user is in a hurry. This allows for more appropriate traffic control by adjusting the traffic light color adjustment method according to the user's emotions.

[0109] The emergency acquisition unit can estimate the emotion of the emergency vehicle and adjust the timing of acquiring location information based on the estimated emotion. For example, the emergency acquisition unit can increase the frequency of acquiring location information when the emergency vehicle is in an emergency. Also, the emergency acquisition unit can reduce the frequency of acquiring location information when the emergency vehicle is operating normally to reduce battery consumption. Furthermore, the emergency acquisition unit can temporarily suspend acquisition of location information when the emergency vehicle is on standby and resume it when necessary. This allows for a rapid response by adjusting the timing of acquiring location information according to the emotion of the emergency vehicle.

[0110] The control unit can estimate the emotion of the emergency vehicle and adjust the traffic light control method based on the estimated emotion. For example, when the emergency vehicle is in an emergency, the control unit can quickly switch the traffic light to green. Also, when the emergency vehicle is operating normally, the control unit can control the traffic light as usual. Furthermore, when the emergency vehicle is waiting, the control unit can temporarily suspend traffic light control and resume it when necessary. This allows for a rapid response by adjusting the traffic light control method according to the emotion of the emergency vehicle.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The acquisition unit acquires the smartphone's location information. The smartphone's location information includes, for example, GPS data, Wi-Fi location information, Bluetooth signals, etc. The acquisition unit acquires this data in real time and identifies the location. Step 2: The analysis unit analyzes the traffic congestion situation based on the location information acquired by the acquisition unit. The analysis of the traffic congestion situation includes vehicle speed, traffic volume, past traffic data, etc. The analysis unit predicts the current traffic congestion situation based on this data. Step 3: The adjustment unit adjusts the traffic light color based on the results of the analysis by the analysis unit. Adjusting the traffic light color includes adjusting the display time of each color, red, yellow, and green. For example, if there is heavy traffic, the green light time is extended, and if there is congestion, the red light time is shortened. Step 4: The emergency acquisition unit acquires the location information of the emergency vehicle. The location information of the emergency vehicle includes, for example, GPS data, Wi-Fi location information, Bluetooth signals, etc. The emergency acquisition unit acquires this data in real time and identifies the location. Step 5: The control unit controls the traffic light based on the information acquired by the emergency acquisition unit. Traffic light control includes switching the light to green, switching the light to red, etc. For example, when an emergency vehicle approaches, the control unit switches the traffic light to green, and after the vehicle passes, the control unit returns the traffic light to its original state.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires location information of a smartphone; an analysis unit that analyzes a traffic congestion situation based on the location information acquired by the acquisition unit; an adjustment unit that adjusts the color of the traffic light based on the result of the analysis by the analysis unit; an emergency acquisition unit that acquires location information of an emergency vehicle; a control unit that controls a traffic light based on the information acquired by the emergency acquisition unit.

2. The acquisition unit Get your smartphone's location information in real time 2. The system of claim 1.

3. The analysis unit AI analyzes the traffic congestion situation based on the location information acquired by the acquisition unit.

2. The system of claim 1.

4. The adjustment unit The AI ​​adjusts the color of the traffic light based on the results of the analysis by the analysis unit.

2. The system of claim 1.

5. The emergency acquisition unit Obtaining real-time location information of emergency vehicles 2. The system of claim 1.

6. The control unit Switching the traffic light to green based on the information acquired by the emergency acquisition unit 2. The system of claim 1.

7. The acquisition unit Estimates the user's emotions and adjusts the timing of acquiring location information based on the estimated user emotions.

2. The system of claim 1.

8. The acquisition unit Analyze the user's past movement history and select the appropriate acquisition method 2. The system of claim 1.

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