system

Abstract

The system according to the embodiment aims to optimize the traffic light switching time according to the traffic volume and the number of pedestrians. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, an implementation unit, and a real-time adjustment unit. The collection unit collects data from recording monitors or surveillance cameras installed at each traffic light, car navigation systems, and smartphones. The analysis unit analyzes the data collected by the collection unit to determine traffic volume and the number of pedestrians. The proposal unit proposes appropriate switching times for each traffic light and pedestrian-vehicle separated traffic lights based on the analysis results obtained by the analysis unit. The implementation unit reflects the content proposed by the proposal unit in the traffic lights. The real-time adjustment unit adjusts the lighting times of the traffic lights in real time based on the data collected in the central server.

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 optimize traffic light switching times according to traffic volume and the number of pedestrians, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize the traffic light switching time according to the traffic volume and the number of pedestrians. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, an implementation unit, and a real-time adjustment unit. The collection unit collects data from recording monitors or surveillance cameras installed at each traffic light, car navigation systems, and smartphones. The analysis unit analyzes the data collected by the collection unit to determine traffic volume and the number of pedestrians. The proposal unit proposes appropriate switching times for each traffic light and pedestrian-vehicle separated traffic lights based on the analysis results obtained by the analysis unit. The implementation unit reflects the content proposed by the proposal unit in the traffic lights. The real-time adjustment unit adjusts the lighting times of the traffic lights in real time based on the data aggregated in the central server. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the traffic light switching time according to the traffic volume and the number of pedestrians. [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 signal optimization system according to an embodiment of the present invention uses AI to propose optimal traffic signal switching times and achieve real-time signal switching according to traffic volume. This traffic signal optimization system first installs a video monitor at each traffic signal and measures the volume of automobile and bicycle traffic and the number of pedestrians over a certain period of time. This data serves as the basis for proposing optimal switching times for each traffic signal. Next, based on the collected video data, AI is used to propose optimal switching times for each traffic signal. The AI ​​optimizes the lighting duration of each color based on traffic volume and the number of pedestrians. The AI ​​also proposes and implements pedestrian-vehicle separation for traffic signals. Furthermore, the collected data is expanded to include information captured by surveillance cameras installed on roads, location information from car navigation systems and dashcams, and location information from smartphones carried by passengers and pedestrians. This data is aggregated in real time on a central server. Based on the traffic volume data (video, still images, and location information data) aggregated on the central server, AI calculates the optimal lighting duration for each traffic signal in real time and reflects the results in each traffic signal. This enables real-time optimization of traffic signal switching according to traffic volume. For example, if the volume of automobile traffic at an intersection increases, the AI ​​will use that information to adjust the duration of traffic lights to ensure smooth traffic flow. It will also extend the duration of pedestrian signals during times of high pedestrian traffic to ensure safe crossing. This system is expected to smooth traffic flow, alleviate congestion, and reduce traffic accidents. It also improves pedestrian safety and contributes to an overall improvement in the traffic environment. This allows the traffic signal optimization system to achieve real-time optimization of signal switching according to traffic volume.

[0029] A traffic signal optimization system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, an implementation unit, and a real-time adjustment unit. The collection unit collects data from recording monitors or surveillance cameras installed at each traffic signal, car navigation systems, and smartphones. The collection unit can collect, for example, image capture information from surveillance cameras installed on roads, location information from car navigation systems and dashcams, and location information from smartphones carried by passengers and pedestrians. The analysis unit analyzes the data collected by the collection unit to determine traffic volume and the number of pedestrians. The analysis unit can accurately determine traffic volume and the number of pedestrians, for example, using an image analysis algorithm or a data mining method. The proposal unit proposes optimal switching times for each traffic signal and pedestrian-vehicle separated traffic signals based on the analysis results obtained by the analysis unit. The proposal unit can, for example, adjust traffic times based on traffic volume and set peak hours. The implementation unit reflects the content proposed by the proposal unit in the traffic signal. The implementation unit can quickly reflect the content proposed in the traffic signal using, for example, a traffic signal program update method or a remote control procedure. The real-time adjustment unit adjusts the illumination times of traffic lights in real time based on data collected in the central server. The real-time adjustment unit can adjust the illumination times of traffic lights in real time, for example, by using dynamic adjustment according to traffic conditions or a predictive algorithm. This allows the traffic signal optimization system according to the embodiment to achieve real-time optimization of signal switching according to traffic volume.

[0030] The collection unit can collect image information captured by surveillance cameras installed on roads, location information from car navigation systems or dashcams, and location information from smartphones carried by passengers and pedestrians. The collection unit, for example, collects image information captured by surveillance cameras installed on roads. Surveillance cameras can be fixed or mobile, and can collect information from different perspectives depending on their installation location and height. The collection unit can also collect location information from car navigation systems and dashcams. Car navigation systems provide GPS data and real-time traffic information, and dashcams record video footage and location information while driving. The collection unit can also collect location information from smartphones carried by passengers and pedestrians. Smartphones provide GPS data, Wi-Fi location information, acceleration sensor data, etc. This enables the collection unit to collect information from a variety of data sources. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired from surveillance cameras, car navigation systems, and smartphones into AI and have the AI ​​collect and analyze the data.

[0031] The analysis unit can analyze the collected data and determine traffic volume and the number of pedestrians. The analysis unit can analyze the collected data using, for example, an image analysis algorithm. The image analysis algorithm can analyze video data obtained from surveillance cameras and dashcams to determine traffic volume and the number of pedestrians. The analysis unit can also analyze location information data obtained from car navigation systems and smartphones using data mining techniques. Data mining techniques are used to extract useful information from large amounts of data and accurately determine traffic volume and the number of pedestrians. The analysis unit can also analyze the collected data using AI. The AI ​​can predict traffic volume and the number of pedestrians using, for example, a machine learning algorithm and provide analysis results. This enables the analysis unit to accurately determine traffic volume and the number of pedestrians. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0032] The proposal unit can propose appropriate switching times for each traffic signal and pedestrian-vehicle separated traffic signals based on the analysis results. The proposal unit, for example, adjusts the time based on traffic volume. When traffic volume is heavy, the lighting time of the traffic signal is extended to smooth traffic flow. The proposal unit can also set peak hours. During peak hours, traffic volume increases, so the lighting time of the traffic signal is adjusted to alleviate congestion. The proposal unit can also propose pedestrian-vehicle separated traffic signals. Pedestrian-vehicle separated traffic signals are used to separate signals for pedestrians and vehicles and ensure pedestrian safety. This enables the proposal unit to propose optimal switching times for traffic signals and pedestrian-vehicle separated traffic signals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the analysis results into AI and have the AI ​​execute the proposal of switching times for traffic signals and pedestrian-vehicle separated traffic signals.

[0033] The implementation unit can reflect the proposed content in the traffic light. The implementation unit can reflect the proposed content in the traffic light, for example, using a traffic light program update method. The traffic light program update method is used to update the traffic light's control program and reflect the proposed content. The implementation unit can also reflect the proposed content in the traffic light using a remote control procedure. The remote control procedure is used to send instructions to the traffic light from a central server and control the traffic light's operation. This enables the implementation unit to quickly reflect the proposed content in the traffic light. Some or all of the above-mentioned processing in the implementation unit can be performed using, for example, AI, or can be performed without using AI. For example, the implementation unit can input the proposed content into AI and have the AI ​​perform program updates and remote control of the traffic light.

[0034] The real-time adjustment unit can adjust the lighting time of the traffic lights in real time based on data collected in the central server. The real-time adjustment unit, for example, performs dynamic adjustment according to traffic conditions. When traffic volume increases, the lighting time of the traffic lights is extended to smooth traffic flow. The real-time adjustment unit can also adjust the lighting time of the traffic lights using a predictive algorithm. The predictive algorithm is used to predict traffic volume based on past data and optimize the lighting time of the traffic lights. This enables the real-time adjustment unit to adjust the lighting time of the traffic lights in real time. Some or all of the above-mentioned processing in the real-time adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time adjustment unit can input data collected in the central server into AI and have the AI ​​adjust the lighting time of the traffic lights.

[0035] The collection unit can analyze past traffic data and select the optimal data collection method. For example, the collection unit can predict peak traffic volume during a specific time period based on the past traffic data and concentrate data collection during that time period. The collection unit can also analyze past traffic data to understand traffic patterns for specific days of the week or seasons and adjust the data collection method based on that understanding. Furthermore, the collection unit can refer to past traffic data and collect data taking into account the impact of specific events or occasions on traffic volume. This enables the collection unit to select the optimal data collection method based on past traffic data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past traffic data into AI and have the AI ​​select the optimal data collection method.

[0036] The collection unit can filter data based on specific traffic conditions or events when collecting data. For example, when a traffic accident occurs, the collection unit prioritizes collecting data from the surrounding area to obtain detailed information. In addition, when a large-scale event is held, the collection unit can also focus on collecting data from areas affected by the event. Furthermore, when traffic conditions fluctuate due to changes in weather, the collection unit can filter and collect data affected by the weather. This enables the collection unit to filter data collection based on specific traffic conditions or events. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can cause AI to filter data collection based on specific traffic conditions or events.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, the collection unit can prioritize collecting data from intersections and major roads with high traffic volume to understand traffic conditions in detail. The collection unit can also prioritize collecting data from areas affected by events and construction information in specific areas by taking into consideration such information. Furthermore, the collection unit can prioritize collecting data around geographically important points (schools, hospitals, etc.) to ensure safety. This enables the collection unit to prioritize collecting highly relevant data based on geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information into AI and cause the AI ​​to prioritize collecting highly relevant data.

[0038] The collection unit can analyze social media activities and collect related data when collecting data. The collection unit, for example, collects traffic information and congestion information on social media and updates the information in real time. The collection unit can also analyze posts about events and accidents and prioritize the collection of data from areas affected by them. Furthermore, the collection unit can analyze social media activities based on user location information and collect related data. This enables the collection unit to collect related data based on social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media activities into AI and have the AI ​​collect related data.

[0039] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis data. For example, the analysis unit learns specific traffic patterns based on past analysis data and optimizes the analysis algorithm. The analysis unit can also refer to past analysis results and adjust parameters to improve the accuracy of the analysis. Furthermore, the analysis unit can also optimize the algorithm for detecting abnormal traffic patterns using past analysis data. This enables the analysis unit to optimize the analysis algorithm based on the past analysis data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis data into AI and have the AI ​​optimize the analysis algorithm.

[0040] During analysis, the analysis unit can apply a special analysis method for detecting specific traffic patterns or abnormalities. For example, the analysis unit applies a special analysis method for predicting the occurrence of traffic congestion. The analysis unit can also apply a special analysis method for detecting the occurrence of traffic accidents. Furthermore, the analysis unit can apply a special method for analyzing the impact of specific events or construction works on traffic. This enables the analysis unit to detect specific traffic patterns or abnormalities. 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 cause AI to execute the analysis method for detecting specific traffic patterns or abnormalities.

[0041] The analysis unit can perform the analysis taking geographical distribution into consideration. For example, the analysis unit analyzes the distribution of traffic volume in a specific area to understand traffic patterns. The analysis unit can also analyze traffic volume around geographically important points (schools, hospitals, etc.) to evaluate safety. Furthermore, the analysis unit can analyze traffic patterns taking geographical features (mountains, rivers, etc.) into consideration. This enables the analysis unit to perform analysis based on geographical distribution. 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 geographical distribution data into AI and have the AI ​​perform the analysis.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related literature. For example, the analysis unit can refer to literature on the latest traffic analysis technology and improve the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to research papers on traffic patterns. Furthermore, the analysis unit can also optimize the anomaly detection algorithm by referring to literature on traffic accident prediction. This enables the analysis unit to improve the accuracy of the analysis based on related literature. 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 related literature data into AI and have the AI ​​improve the analysis algorithm.

[0043] The proposal unit can adjust the level of detail of the proposal based on the importance of traffic volume when making a proposal. For example, the proposal unit can make detailed proposals at intersections with heavy traffic volume to optimize traffic flow. The proposal unit can also make concise proposals in areas with light traffic volume to respond efficiently. Furthermore, the proposal unit can adjust the level of detail of the proposal by taking into account fluctuations in traffic volume during specific time periods. This enables the proposal unit to adjust the level of detail of the proposal based on the importance of traffic volume. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input traffic volume data to AI and cause the AI ​​to adjust the level of detail of the proposal.

[0044] When making a proposal, the proposal unit can apply different proposed algorithms depending on specific traffic conditions. For example, when traffic congestion occurs, the proposal unit applies a proposed algorithm for alleviating congestion. In addition, when a traffic accident occurs, the proposal unit can also apply a proposed algorithm for responding to the accident. Furthermore, the proposal unit can take into account the impact of a specific event or construction on traffic and apply a proposed algorithm depending on the situation. This enables the proposal unit to apply a proposed algorithm depending on specific traffic conditions. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input specific traffic condition data into AI and cause the AI ​​to apply the proposed algorithm.

[0045] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of traffic data. The proposal unit, for example, prioritizes proposals based on the latest traffic data. The proposal unit can also refer to past traffic data and prioritize proposals with high importance. Furthermore, the proposal unit can adjust the priority of the proposal depending on the time of submission of the traffic data. This enables the proposal unit to determine the priority of the proposal based on the time of submission of the traffic data. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the time of submission of traffic data to AI and have the AI ​​perform proposal priority determination.

[0046] The suggestion unit can adjust the order of proposals based on relevance when making a proposal. For example, the suggestion unit can prioritize proposals for areas with heavy traffic volume to optimize traffic flow. The suggestion unit can also prioritize proposals for areas where traffic accidents have occurred to ensure safety. Furthermore, the suggestion unit can take into account the impact of specific events or construction works on traffic and adjust the order of proposals based on the relevance. This enables the suggestion unit to adjust the order of proposals based on relevance. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input relevance data into AI and have the AI ​​adjust the order of proposals.

[0047] During implementation, the implementation unit can optimize the implementation algorithm by referring to past implementation data. For example, the implementation unit selects the optimal implementation method for a specific traffic pattern based on the past implementation data. The implementation unit can also refer to past implementation results and adjust parameters to improve implementation accuracy. Furthermore, the implementation unit can also optimize an implementation method for an abnormal traffic pattern using the past implementation data. This enables the implementation unit to optimize the implementation algorithm based on the past implementation data. Some or all of the above-described processing in the implementation unit may be performed using, for example, AI, or may be performed without using AI. For example, the implementation unit can input past implementation data into AI and have the AI ​​execute optimization of the implementation algorithm.

[0048] The implementation unit can customize the implementation method according to specific traffic conditions during implementation. For example, when traffic congestion occurs, the implementation unit applies an implementation method for alleviating congestion. In addition, when a traffic accident occurs, the implementation unit can also apply an implementation method for responding to the accident. Furthermore, the implementation unit can customize the implementation method according to the situation, taking into account the impact of a specific event or construction on traffic. This enables the implementation unit to customize the implementation method according to specific traffic conditions. Some or all of the above-mentioned processing in the implementation unit may be performed using AI, for example, or may be performed without using AI. For example, the implementation unit can input specific traffic condition data into AI and have the AI ​​execute the customization of the implementation method.

[0049] During implementation, the implementation unit can select the optimal implementation method by taking geographical location information into consideration. For example, the implementation unit can prioritize implementation at intersections and major roads with heavy traffic, thereby optimizing traffic flow. The implementation unit can also consider events and construction information in specific areas and prioritize implementation in areas affected by those events and construction information. Furthermore, the implementation unit can prioritize implementation around geographically important points (schools, hospitals, etc.) to ensure safety. This enables the implementation unit to select the optimal implementation method based on geographical location information. Some or all of the above-mentioned processing in the implementation unit may be performed using, for example, AI, or may be performed without using AI. For example, the implementation unit can input geographical location information into AI and have the AI ​​select the optimal implementation method.

[0050] During implementation, the implementation unit can analyze social media activity and propose implementation measures. The implementation unit, for example, collects traffic and congestion information on social media and updates it in real time. The implementation unit can also analyze posts about events and accidents and prioritize implementation in areas affected by them. Furthermore, the implementation unit can analyze social media activity based on user location information and propose related implementation measures. This enables the implementation unit to propose implementation measures based on social media activity. Some or all of the above-mentioned processing in the implementation unit may be performed using, for example, AI, or may be performed without AI. For example, the implementation unit can input social media activity data into AI and have the AI ​​execute the proposed implementation measures.

[0051] The real-time adjustment unit can optimize the adjustment algorithm by referring to past adjustment data during real-time adjustment. The real-time adjustment unit, for example, selects an optimal adjustment method for a specific traffic pattern based on past adjustment data. The real-time adjustment unit can also adjust parameters to improve adjustment accuracy by referring to past adjustment results. Furthermore, the real-time adjustment unit can also optimize an adjustment method for an abnormal traffic pattern using past adjustment data. This enables the real-time adjustment unit to optimize the adjustment algorithm based on the past adjustment data. Some or all of the above-described processing in the real-time adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time adjustment unit can input past adjustment data into AI and have the AI ​​optimize the adjustment algorithm.

[0052] The real-time adjustment unit can customize the adjustment method according to specific traffic conditions during real-time adjustment. For example, when traffic congestion occurs, the real-time adjustment unit applies an adjustment method to alleviate the congestion. In addition, when a traffic accident occurs, the real-time adjustment unit can also apply an adjustment method to respond to the accident. Furthermore, the real-time adjustment unit can customize the adjustment method according to the situation, taking into account the impact of a specific event or construction on traffic. This enables the real-time adjustment unit to customize the adjustment method according to specific traffic conditions. Some or all of the above-mentioned processing in the real-time adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time adjustment unit can input specific traffic condition data into AI and have the AI ​​customize the adjustment method.

[0053] The real-time adjustment unit can select the optimal adjustment method by taking geographical location information into consideration during real-time adjustment. For example, the real-time adjustment unit prioritizes adjustments at intersections and major roads with heavy traffic volume, thereby optimizing traffic flow. The real-time adjustment unit can also prioritize adjustments in areas affected by events or construction information in specific areas, taking into account such information. Furthermore, the real-time adjustment unit can prioritize adjustments around geographically important locations (schools, hospitals, etc.) to ensure safety. This enables the real-time adjustment unit to select the optimal adjustment method based on the geographical location information. Some or all of the above-described processing in the real-time adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time adjustment unit can input geographical location information into AI and have the AI ​​select the optimal adjustment method.

[0054] The real-time adjustment unit can analyze social media activity and propose adjustment measures during real-time adjustment. The real-time adjustment unit, for example, collects traffic information and congestion information on social media and reflects the information in real time. The real-time adjustment unit can also analyze posts about events and accidents and prioritize adjustments in areas affected by them. Furthermore, the real-time adjustment unit can analyze social media activity based on user location information and propose related adjustment measures. This enables the real-time adjustment unit to propose adjustment measures based on social media activity. Some or all of the above-described processing in the real-time adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time adjustment unit can input social media activity data into AI and have the AI ​​execute the proposed adjustment 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 analysis unit can take weather data into account when optimizing traffic signals. For example, traffic volume tends to decrease during rainy weather, so the lighting time of the signal can be shortened. Also, during poor visibility due to snow or fog, the lighting time of the pedestrian signal can be extended to ensure pedestrian safety. Furthermore, the analysis unit can learn traffic patterns under specific weather conditions based on the weather data and adjust the signal optimization algorithm. This enables the analysis unit to optimize traffic signals taking weather data into account.

[0057] The collection unit can identify vehicle types and collect data based on them when optimizing traffic signals. For example, the collection unit can identify different vehicles such as passenger cars, trucks, buses, and bicycles and measure the traffic volume of each type individually. The collection unit can also collect data preferentially for specific vehicle types and reflect this in traffic signal optimization. Furthermore, the collection unit can learn traffic patterns for each vehicle type and adjust the lighting time of signals based on this. This enables the collection unit to collect data taking vehicle types into consideration.

[0058] The analysis unit can take traffic accident data into account when optimizing traffic signals. For example, the analysis unit can evaluate the risk of accidents occurring at specific intersections or time periods based on past traffic accident data and adjust the lighting duration of traffic signals. The analysis unit can also analyze the causes of traffic accidents and improve the signal optimization algorithm based on the results. Furthermore, the analysis unit can collect traffic accident data in real time and immediately adjust the lighting duration of traffic signals. This enables the analysis unit to optimize traffic signals by taking traffic accident data into account.

[0059] The proposal unit can take into account operation data of public transportation facilities when optimizing traffic signals. For example, the proposal unit can make a proposal to prioritize turning a traffic signal green when public transportation facilities are passing, based on bus and train operation schedules. The proposal unit can also take into account delay information of public transportation facilities and propose signal adjustments to minimize delays. Furthermore, the proposal unit can learn traffic patterns during specific time periods based on the operation data of public transportation facilities and adjust the signal optimization algorithm. This enables the proposal unit to optimize traffic signals while taking into account operation data of public transportation facilities.

[0060] The Implementation Department can select implementation methods to minimize energy consumption when optimizing traffic signals. For example, the Implementation Department can optimize the lighting times of traffic signals to reduce energy consumption. The Implementation Department can also propose the implementation of traffic signals that use renewable energy such as solar power or wind power. Furthermore, the Implementation Department can promote the introduction of energy-efficient LED traffic signals to reduce overall energy consumption. This enables the Implementation Department to optimize traffic signals to minimize energy consumption.

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

[0062] Step 1: The collection unit collects data from recording monitors or surveillance cameras installed at traffic lights, car navigation systems, and smartphones. For example, it can collect information captured by surveillance cameras installed on roads, location information from car navigation systems and dashcams, and location information from smartphones held by passengers and pedestrians. Step 2: The analysis unit analyzes the data collected by the collection unit to determine traffic volume and the number of pedestrians. For example, image analysis algorithms and data mining techniques can be used to accurately determine traffic volume and the number of pedestrians. Step 3: The proposal unit proposes optimal switching times for each traffic signal and pedestrian-vehicle separated traffic signals based on the analysis results obtained by the analysis unit. For example, it can adjust traffic times based on traffic volume and set peak hours. Step 4: The implementation unit reflects the content proposed by the proposal unit in the traffic light. For example, the proposed content can be quickly reflected in the traffic light by using a traffic light program update method or a remote operation procedure. Step 5: The real-time adjustment unit adjusts the lighting time of traffic lights in real time based on the data collected in the central server. For example, it can adjust the lighting time of traffic lights in real time using dynamic adjustments based on traffic conditions or predictive algorithms.

[0063] (Example 2) A traffic signal optimization system according to an embodiment of the present invention uses AI to propose optimal traffic signal switching times and achieve real-time signal switching according to traffic volume. This traffic signal optimization system first installs a video monitor at each traffic signal and measures the volume of automobile and bicycle traffic and the number of pedestrians over a certain period of time. This data serves as the basis for proposing optimal switching times for each traffic signal. Next, based on the collected video data, AI is used to propose optimal switching times for each traffic signal. The AI ​​optimizes the lighting duration of each color based on traffic volume and the number of pedestrians. The AI ​​also proposes and implements pedestrian-vehicle separation for traffic signals. Furthermore, the collected data is expanded to include information captured by surveillance cameras installed on roads, location information from car navigation systems and dashcams, and location information from smartphones carried by passengers and pedestrians. This data is aggregated in real time on a central server. Based on the traffic volume data (video, still images, and location information data) aggregated on the central server, AI calculates the optimal lighting duration for each traffic signal in real time and reflects the results in each traffic signal. This enables real-time optimization of traffic signal switching according to traffic volume. For example, if the volume of automobile traffic at an intersection increases, the AI ​​will use that information to adjust the duration of traffic lights to ensure smooth traffic flow. It will also extend the duration of pedestrian signals during times of high pedestrian traffic to ensure safe crossing. This system is expected to smooth traffic flow, alleviate congestion, and reduce traffic accidents. It also improves pedestrian safety and contributes to an overall improvement in the traffic environment. This allows the traffic signal optimization system to achieve real-time optimization of signal switching according to traffic volume.

[0064] A traffic signal optimization system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, an implementation unit, and a real-time adjustment unit. The collection unit collects data from recording monitors or surveillance cameras installed at each traffic signal, car navigation systems, and smartphones. The collection unit can collect, for example, image capture information from surveillance cameras installed on roads, location information from car navigation systems and dashcams, and location information from smartphones carried by passengers and pedestrians. The analysis unit analyzes the data collected by the collection unit to determine traffic volume and the number of pedestrians. The analysis unit can accurately determine traffic volume and the number of pedestrians, for example, using an image analysis algorithm or a data mining method. The proposal unit proposes optimal switching times for each traffic signal and pedestrian-vehicle separated traffic signals based on the analysis results obtained by the analysis unit. The proposal unit can, for example, adjust traffic times based on traffic volume and set peak hours. The implementation unit reflects the content proposed by the proposal unit in the traffic signal. The implementation unit can quickly reflect the content proposed in the traffic signal using, for example, a traffic signal program update method or a remote control procedure. The real-time adjustment unit adjusts the illumination times of traffic lights in real time based on data collected in the central server. The real-time adjustment unit can adjust the illumination times of traffic lights in real time, for example, by using dynamic adjustment according to traffic conditions or a predictive algorithm. This allows the traffic signal optimization system according to the embodiment to achieve real-time optimization of signal switching according to traffic volume.

[0065] The collection unit can collect image information captured by surveillance cameras installed on roads, location information from car navigation systems or dashcams, and location information from smartphones carried by passengers and pedestrians. The collection unit, for example, collects image information captured by surveillance cameras installed on roads. Surveillance cameras can be fixed or mobile, and can collect information from different perspectives depending on their installation location and height. The collection unit can also collect location information from car navigation systems and dashcams. Car navigation systems provide GPS data and real-time traffic information, and dashcams record video footage and location information while driving. The collection unit can also collect location information from smartphones carried by passengers and pedestrians. Smartphones provide GPS data, Wi-Fi location information, acceleration sensor data, etc. This enables the collection unit to collect information from a variety of data sources. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired from surveillance cameras, car navigation systems, and smartphones into AI and have the AI ​​collect and analyze the data.

[0066] The analysis unit can analyze the collected data and determine traffic volume and the number of pedestrians. The analysis unit can analyze the collected data using, for example, an image analysis algorithm. The image analysis algorithm can analyze video data obtained from surveillance cameras and dashcams to determine traffic volume and the number of pedestrians. The analysis unit can also analyze location information data obtained from car navigation systems and smartphones using data mining techniques. Data mining techniques are used to extract useful information from large amounts of data and accurately determine traffic volume and the number of pedestrians. The analysis unit can also analyze the collected data using AI. The AI ​​can predict traffic volume and the number of pedestrians using, for example, a machine learning algorithm and provide analysis results. This enables the analysis unit to accurately determine traffic volume and the number of pedestrians. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0067] The proposal unit can propose appropriate switching times for each traffic signal and pedestrian-vehicle separated traffic signals based on the analysis results. The proposal unit, for example, adjusts the time based on traffic volume. When traffic volume is heavy, the lighting time of the traffic signal is extended to smooth traffic flow. The proposal unit can also set peak hours. During peak hours, traffic volume increases, so the lighting time of the traffic signal is adjusted to alleviate congestion. The proposal unit can also propose pedestrian-vehicle separated traffic signals. Pedestrian-vehicle separated traffic signals are used to separate signals for pedestrians and vehicles and ensure pedestrian safety. This enables the proposal unit to propose optimal switching times for traffic signals and pedestrian-vehicle separated traffic signals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the analysis results into AI and have the AI ​​execute the proposal of switching times for traffic signals and pedestrian-vehicle separated traffic signals.

[0068] The implementation unit can reflect the proposed content in the traffic light. The implementation unit can reflect the proposed content in the traffic light, for example, using a traffic light program update method. The traffic light program update method is used to update the traffic light's control program and reflect the proposed content. The implementation unit can also reflect the proposed content in the traffic light using a remote control procedure. The remote control procedure is used to send instructions to the traffic light from a central server and control the traffic light's operation. This enables the implementation unit to quickly reflect the proposed content in the traffic light. Some or all of the above-mentioned processing in the implementation unit can be performed using, for example, AI, or can be performed without using AI. For example, the implementation unit can input the proposed content into AI and have the AI ​​perform program updates and remote control of the traffic light.

[0069] The real-time adjustment unit can adjust the lighting time of the traffic lights in real time based on data collected in the central server. The real-time adjustment unit, for example, performs dynamic adjustment according to traffic conditions. When traffic volume increases, the lighting time of the traffic lights is extended to smooth traffic flow. The real-time adjustment unit can also adjust the lighting time of the traffic lights using a predictive algorithm. The predictive algorithm is used to predict traffic volume based on past data and optimize the lighting time of the traffic lights. This enables the real-time adjustment unit to adjust the lighting time of the traffic lights in real time. Some or all of the above-mentioned processing in the real-time adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time adjustment unit can input data collected in the central server into AI and have the AI ​​adjust the lighting time of the traffic lights.

[0070] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can adjust the timing of data collection to quickly collect necessary data. This enables the collection unit to adjust the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0071] The collection unit can analyze past traffic data and select the optimal data collection method. For example, the collection unit can predict peak traffic volume during a specific time period based on the past traffic data and concentrate data collection during that time period. The collection unit can also analyze past traffic data to understand traffic patterns for specific days of the week or seasons and adjust the data collection method based on that understanding. Furthermore, the collection unit can refer to past traffic data and collect data taking into account the impact of specific events or occasions on traffic volume. This enables the collection unit to select the optimal data collection method based on past traffic data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past traffic data into AI and have the AI ​​select the optimal data collection method.

[0072] The collection unit can filter data based on specific traffic conditions or events when collecting data. For example, when a traffic accident occurs, the collection unit prioritizes collecting data from the surrounding area to obtain detailed information. In addition, when a large-scale event is held, the collection unit can also focus on collecting data from areas affected by the event. Furthermore, when traffic conditions fluctuate due to changes in weather, the collection unit can filter and collect data affected by the weather. This enables the collection unit to filter data collection based on specific traffic conditions or events. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can cause AI to filter data collection based on specific traffic conditions or events.

[0073] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting important data and processes it quickly. Furthermore, when the user is relaxed, the collection unit can collect detailed data to improve the accuracy of the analysis. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting the minimum amount of data necessary and respond quickly. This enables the collection unit to prioritize data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​perform data prioritization.

[0074] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, the collection unit can prioritize collecting data from intersections and major roads with high traffic volume to understand traffic conditions in detail. The collection unit can also prioritize collecting data from areas affected by events and construction information in specific areas by taking into consideration such information. Furthermore, the collection unit can prioritize collecting data around geographically important points (schools, hospitals, etc.) to ensure safety. This enables the collection unit to prioritize collecting highly relevant data based on geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information into AI and cause the AI ​​to prioritize collecting highly relevant data.

[0075] The collection unit can analyze social media activities and collect related data when collecting data. The collection unit, for example, collects traffic information and congestion information on social media and updates the information in real time. The collection unit can also analyze posts about events and accidents and prioritize the collection of data from areas affected by them. Furthermore, the collection unit can analyze social media activities based on user location information and collect related data. This enables the collection unit to collect related data based on social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media activities into AI and have the AI ​​collect related data.

[0076] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can use detailed data to improve the accuracy of the analysis. Furthermore, if the user is relaxed, the analysis unit can prioritize the speed of the analysis and provide results quickly. Furthermore, if the user is in a hurry, the analysis unit can quickly perform the analysis using the minimum amount of data necessary. This enables the analysis unit to adjust the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the analysis method.

[0077] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis data. For example, the analysis unit learns specific traffic patterns based on past analysis data and optimizes the analysis algorithm. The analysis unit can also refer to past analysis results and adjust parameters to improve the accuracy of the analysis. Furthermore, the analysis unit can also optimize the algorithm for detecting abnormal traffic patterns using past analysis data. This enables the analysis unit to optimize the analysis algorithm based on the past analysis data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis data into AI and have the AI ​​optimize the analysis algorithm.

[0078] During analysis, the analysis unit can apply a special analysis method for detecting specific traffic patterns or abnormalities. For example, the analysis unit applies a special analysis method for predicting the occurrence of traffic congestion. The analysis unit can also apply a special analysis method for detecting the occurrence of traffic accidents. Furthermore, the analysis unit can apply a special method for analyzing the impact of specific events or construction works on traffic. This enables the analysis unit to detect specific traffic patterns or abnormalities. 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 cause AI to execute the analysis method for detecting specific traffic patterns or abnormalities.

[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 feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This enables the analysis unit to adjust the display method of the analysis results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the display method of the analysis results.

[0080] The analysis unit can perform the analysis taking geographical distribution into consideration. For example, the analysis unit analyzes the distribution of traffic volume in a specific area to understand traffic patterns. The analysis unit can also analyze traffic volume around geographically important points (schools, hospitals, etc.) to evaluate safety. Furthermore, the analysis unit can analyze traffic patterns taking geographical features (mountains, rivers, etc.) into consideration. This enables the analysis unit to perform analysis based on geographical distribution. 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 geographical distribution data into AI and have the AI ​​perform the analysis.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related literature. For example, the analysis unit can refer to literature on the latest traffic analysis technology and improve the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to research papers on traffic patterns. Furthermore, the analysis unit can also optimize the anomaly detection algorithm by referring to literature on traffic accident prediction. This enables the analysis unit to improve the accuracy of the analysis based on related literature. 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 related literature data into AI and have the AI ​​improve the analysis algorithm.

[0082] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can make simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can make suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can make suggestions that focus on the main points. This enables the suggestion unit to adjust the way suggestions are expressed according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into an AI and have the AI ​​adjust the way suggestions are expressed.

[0083] The proposal unit can adjust the level of detail of the proposal based on the importance of traffic volume when making a proposal. For example, the proposal unit can make detailed proposals at intersections with heavy traffic volume to optimize traffic flow. The proposal unit can also make concise proposals in areas with light traffic volume to respond efficiently. Furthermore, the proposal unit can adjust the level of detail of the proposal by taking into account fluctuations in traffic volume during specific time periods. This enables the proposal unit to adjust the level of detail of the proposal based on the importance of traffic volume. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input traffic volume data to AI and cause the AI ​​to adjust the level of detail of the proposal.

[0084] When making a proposal, the proposal unit can apply different proposed algorithms depending on specific traffic conditions. For example, when traffic congestion occurs, the proposal unit applies a proposed algorithm for alleviating congestion. In addition, when a traffic accident occurs, the proposal unit can also apply a proposed algorithm for responding to the accident. Furthermore, the proposal unit can take into account the impact of a specific event or construction on traffic and apply a proposed algorithm depending on the situation. This enables the proposal unit to apply a proposed algorithm depending on specific traffic conditions. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input specific traffic condition data into AI and cause the AI ​​to apply the proposed algorithm.

[0085] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggestions with higher importance. Furthermore, if the user is relaxed, the suggestion unit can prioritize detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggestions for quick responses. This enables the suggestion unit to prioritize suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into an AI and have the AI ​​prioritize the suggestions.

[0086] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of traffic data. The proposal unit, for example, prioritizes proposals based on the latest traffic data. The proposal unit can also refer to past traffic data and prioritize proposals with high importance. Furthermore, the proposal unit can adjust the priority of the proposal depending on the time of submission of the traffic data. This enables the proposal unit to determine the priority of the proposal based on the time of submission of the traffic data. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the time of submission of traffic data to AI and have the AI ​​perform proposal priority determination.

[0087] The suggestion unit can adjust the order of proposals based on relevance when making a proposal. For example, the suggestion unit can prioritize proposals for areas with heavy traffic volume to optimize traffic flow. The suggestion unit can also prioritize proposals for areas where traffic accidents have occurred to ensure safety. Furthermore, the suggestion unit can take into account the impact of specific events or construction works on traffic and adjust the order of proposals based on the relevance. This enables the suggestion unit to adjust the order of proposals based on relevance. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input relevance data into AI and have the AI ​​adjust the order of proposals.

[0088] The implementation unit can estimate the user's emotions and adjust the implementation method based on the estimated user emotions. For example, if the user is feeling stressed, the implementation unit selects a simple and quick implementation method. Furthermore, if the user is relaxed, the implementation unit can provide detailed implementation instructions and respond carefully. Furthermore, if the user is in a hurry, the implementation unit can quickly implement the user and respond with the minimum necessary steps. This enables the implementation unit to adjust the implementation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the implementation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the implementation unit can input user emotion data into an AI and have the AI ​​adjust the implementation method.

[0089] During implementation, the implementation unit can optimize the implementation algorithm by referring to past implementation data. For example, the implementation unit selects the optimal implementation method for a specific traffic pattern based on the past implementation data. The implementation unit can also refer to past implementation results and adjust parameters to improve implementation accuracy. Furthermore, the implementation unit can also optimize an implementation method for an abnormal traffic pattern using the past implementation data. This enables the implementation unit to optimize the implementation algorithm based on the past implementation data. Some or all of the above-described processing in the implementation unit may be performed using, for example, AI, or may be performed without using AI. For example, the implementation unit can input past implementation data into AI and have the AI ​​execute optimization of the implementation algorithm.

[0090] The implementation unit can customize the implementation method according to specific traffic conditions during implementation. For example, when traffic congestion occurs, the implementation unit applies an implementation method for alleviating congestion. In addition, when a traffic accident occurs, the implementation unit can also apply an implementation method for responding to the accident. Furthermore, the implementation unit can customize the implementation method according to the situation, taking into account the impact of a specific event or construction on traffic. This enables the implementation unit to customize the implementation method according to specific traffic conditions. Some or all of the above-mentioned processing in the implementation unit may be performed using AI, for example, or may be performed without using AI. For example, the implementation unit can input specific traffic condition data into AI and have the AI ​​execute the customization of the implementation method.

[0091] The implementation unit can estimate the user's emotions and determine implementation priorities based on the estimated user emotions. For example, if the user is feeling stressed, the implementation unit can prioritize implementations with high importance. Furthermore, if the user is relaxed, the implementation unit can prioritize detailed implementations. Furthermore, if the user is in a hurry, the implementation unit can prioritize implementations for quick responses. This enables the implementation unit to determine implementation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the implementation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the implementation unit can input user emotion data into an AI and have the AI ​​determine the implementation priorities.

[0092] During implementation, the implementation unit can select the optimal implementation method by taking geographical location information into consideration. For example, the implementation unit can prioritize implementation at intersections and major roads with heavy traffic, thereby optimizing traffic flow. The implementation unit can also consider events and construction information in specific areas and prioritize implementation in areas affected by those events and construction information. Furthermore, the implementation unit can prioritize implementation around geographically important points (schools, hospitals, etc.) to ensure safety. This enables the implementation unit to select the optimal implementation method based on geographical location information. Some or all of the above-mentioned processing in the implementation unit may be performed using, for example, AI, or may be performed without using AI. For example, the implementation unit can input geographical location information into AI and have the AI ​​select the optimal implementation method.

[0093] During implementation, the implementation unit can analyze social media activity and propose implementation measures. The implementation unit, for example, collects traffic and congestion information on social media and updates it in real time. The implementation unit can also analyze posts about events and accidents and prioritize implementation in areas affected by them. Furthermore, the implementation unit can analyze social media activity based on user location information and propose related implementation measures. This enables the implementation unit to propose implementation measures based on social media activity. Some or all of the above-mentioned processing in the implementation unit may be performed using, for example, AI, or may be performed without AI. For example, the implementation unit can input social media activity data into AI and have the AI ​​execute the proposed implementation measures.

[0094] The real-time adjustment unit can estimate the user's emotions and adjust the real-time adjustment method based on the estimated user emotions. For example, if the user is feeling stressed, the real-time adjustment unit selects a simple and quick adjustment method. Furthermore, if the user is relaxed, the real-time adjustment unit can provide detailed adjustment procedures and respond carefully. Furthermore, if the user is in a hurry, the real-time adjustment unit can make quick adjustments and respond with the minimum necessary steps. This enables the real-time adjustment unit to adjust the real-time adjustment method 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 may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the real-time adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the real-time adjustment unit can input the user's emotion data into an AI and have the AI ​​adjust the real-time adjustment method.

[0095] The real-time adjustment unit can optimize the adjustment algorithm by referring to past adjustment data during real-time adjustment. The real-time adjustment unit, for example, selects an optimal adjustment method for a specific traffic pattern based on past adjustment data. The real-time adjustment unit can also adjust parameters to improve adjustment accuracy by referring to past adjustment results. Furthermore, the real-time adjustment unit can also optimize an adjustment method for an abnormal traffic pattern using past adjustment data. This enables the real-time adjustment unit to optimize the adjustment algorithm based on the past adjustment data. Some or all of the above-described processing in the real-time adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time adjustment unit can input past adjustment data into AI and have the AI ​​optimize the adjustment algorithm.

[0096] The real-time adjustment unit can customize the adjustment method according to specific traffic conditions during real-time adjustment. For example, when traffic congestion occurs, the real-time adjustment unit applies an adjustment method to alleviate the congestion. In addition, when a traffic accident occurs, the real-time adjustment unit can also apply an adjustment method to respond to the accident. Furthermore, the real-time adjustment unit can customize the adjustment method according to the situation, taking into account the impact of a specific event or construction on traffic. This enables the real-time adjustment unit to customize the adjustment method according to specific traffic conditions. Some or all of the above-mentioned processing in the real-time adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time adjustment unit can input specific traffic condition data into AI and have the AI ​​customize the adjustment method.

[0097] The real-time adjustment unit can estimate the user's emotions and determine the priority of real-time adjustments based on the estimated user emotions. For example, if the user is feeling stressed, the real-time adjustment unit can prioritize adjustments with high importance. Furthermore, if the user is relaxed, the real-time adjustment unit can also prioritize detailed adjustments. Furthermore, if the user is in a hurry, the real-time adjustment unit can also prioritize adjustments for quick response. This enables the real-time adjustment unit to determine the priority of real-time adjustments according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the real-time adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the real-time adjustment unit can input the user's emotion data into an AI and have the AI ​​determine the priority of real-time adjustments.

[0098] The real-time adjustment unit can select the optimal adjustment method by taking geographical location information into consideration during real-time adjustment. For example, the real-time adjustment unit prioritizes adjustments at intersections and major roads with heavy traffic volume, thereby optimizing traffic flow. The real-time adjustment unit can also prioritize adjustments in areas affected by events or construction information in specific areas, taking into account such information. Furthermore, the real-time adjustment unit can prioritize adjustments around geographically important locations (schools, hospitals, etc.) to ensure safety. This enables the real-time adjustment unit to select the optimal adjustment method based on the geographical location information. Some or all of the above-described processing in the real-time adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time adjustment unit can input geographical location information into AI and have the AI ​​select the optimal adjustment method.

[0099] The real-time adjustment unit can analyze social media activity and propose adjustment measures during real-time adjustment. The real-time adjustment unit, for example, collects traffic information and congestion information on social media and reflects the information in real time. The real-time adjustment unit can also analyze posts about events and accidents and prioritize adjustments in areas affected by them. Furthermore, the real-time adjustment unit can analyze social media activity based on user location information and propose related adjustment measures. This enables the real-time adjustment unit to propose adjustment measures based on social media activity. Some or all of the above-described processing in the real-time adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time adjustment unit can input social media activity data into AI and have the AI ​​execute the proposed adjustment measures. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, implementation unit, and real-time adjustment unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 and communication I / F 44 of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal traffic light switching time based on the analysis results. The implementation unit is realized, for example, by the control unit 46A of the smart device 14 and reflects the proposal in the traffic light. The real-time adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the traffic light lighting time in real time based on data collected in a central server. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, implementation unit, and real-time adjustment unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 and communication I / F 44 of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal traffic light switching time based on the analysis results. The implementation unit is realized, for example, by the control unit 46A of the smart glasses 214 and reflects the proposal in the traffic light. The real-time adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the traffic light illumination time in real time based on data aggregated in the central server. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, implementation unit, and real-time adjustment unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 and communication I / F 44 of the headset-type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal traffic light switching time based on the analysis results. The implementation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and reflects the proposal in the traffic light. The real-time adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the traffic light illumination time in real time based on data collected in a central server. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, implementation unit, and real-time adjustment unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 and communication I / F 44 of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal traffic light switching times based on the analysis results. The implementation unit is realized, for example, by the control unit 46A of the robot 414 and reflects the proposal in the traffic light. The real-time adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the traffic light lighting times in real time based on data collected in a central server.

[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 analysis unit can take weather data into account when optimizing traffic signals. For example, traffic volume tends to decrease during rainy weather, so the lighting time of the signal can be shortened. Also, during poor visibility due to snow or fog, the lighting time of the pedestrian signal can be extended to ensure pedestrian safety. Furthermore, the analysis unit can learn traffic patterns under specific weather conditions based on the weather data and adjust the signal optimization algorithm. This enables the analysis unit to optimize traffic signals taking weather data into account.

[0102] The collection unit can identify vehicle types and collect data based on them when optimizing traffic signals. For example, the collection unit can identify different vehicles such as passenger cars, trucks, buses, and bicycles and measure the traffic volume of each type individually. The collection unit can also collect data preferentially for specific vehicle types and reflect this in traffic signal optimization. Furthermore, the collection unit can learn traffic patterns for each vehicle type and adjust the lighting time of signals based on this. This enables the collection unit to collect data taking vehicle types into consideration.

[0103] The analysis unit can take traffic accident data into account when optimizing traffic signals. For example, the analysis unit can evaluate the risk of accidents occurring at specific intersections or time periods based on past traffic accident data and adjust the lighting duration of traffic signals. The analysis unit can also analyze the causes of traffic accidents and improve the signal optimization algorithm based on the results. Furthermore, the analysis unit can collect traffic accident data in real time and immediately adjust the lighting duration of traffic signals. This enables the analysis unit to optimize traffic signals by taking traffic accident data into account.

[0104] The proposal unit can take into account operation data of public transportation facilities when optimizing traffic signals. For example, the proposal unit can make a proposal to prioritize turning a traffic signal green when public transportation facilities are passing, based on bus and train operation schedules. The proposal unit can also take into account delay information of public transportation facilities and propose signal adjustments to minimize delays. Furthermore, the proposal unit can learn traffic patterns during specific time periods based on the operation data of public transportation facilities and adjust the signal optimization algorithm. This enables the proposal unit to optimize traffic signals while taking into account operation data of public transportation facilities.

[0105] The Implementation Department can select implementation methods to minimize energy consumption when optimizing traffic signals. For example, the Implementation Department can optimize the lighting times of traffic signals to reduce energy consumption. The Implementation Department can also propose the implementation of traffic signals that use renewable energy such as solar power or wind power. Furthermore, the Implementation Department can promote the introduction of energy-efficient LED traffic signals to reduce overall energy consumption. This enables the Implementation Department to optimize traffic signals to minimize energy consumption.

[0106] The real-time adjustment unit can estimate the user's emotions and adjust the real-time adjustment method based on the estimated user emotions. For example, if the user is feeling stressed, a simple and quick adjustment method can be selected. Also, if the user is relaxed, detailed adjustment procedures can be provided and a thorough response can be provided. Furthermore, if the user is in a hurry, adjustments can be made quickly and the response can be made with the minimum necessary steps. This enables the real-time adjustment unit to adjust the real-time adjustment method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0107] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the user's burden. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, the timing of data collection can be adjusted to quickly collect necessary data. This enables the collection unit to adjust the timing of data collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0108] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is feeling stressed, detailed data can be used to improve the accuracy of the analysis. Also, if the user is relaxed, the speed of the analysis can be prioritized to provide results quickly. Furthermore, if the user is in a hurry, the analysis can be performed quickly using the minimum amount of data necessary. This allows the analysis unit to adjust the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0109] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible suggestion can be made. If the user is relaxed, a suggestion including detailed information can be made. Furthermore, if the user is in a hurry, a suggestion that focuses on the main points can be made. This enables the suggestion unit to adjust the way suggestions are expressed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0110] The implementation unit can estimate the user's emotions and adjust the implementation method based on the estimated user emotions. For example, if the user is feeling stressed, a simple and quick implementation method can be selected. Alternatively, if the user is relaxed, detailed implementation steps can be provided and a polite response can be provided. Furthermore, if the user is in a hurry, a quick implementation can be performed and a response can be provided with the minimum necessary steps. This enables the implementation unit to adjust the implementation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

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

[0112] Step 1: The collection unit collects data from recording monitors or surveillance cameras installed at traffic lights, car navigation systems, and smartphones. For example, it can collect information captured by surveillance cameras installed on roads, location information from car navigation systems and dashcams, and location information from smartphones held by passengers and pedestrians. Step 2: The analysis unit analyzes the data collected by the collection unit to determine traffic volume and the number of pedestrians. For example, image analysis algorithms and data mining techniques can be used to accurately determine traffic volume and the number of pedestrians. Step 3: The proposal unit proposes optimal switching times for each traffic signal and pedestrian-vehicle separated traffic signals based on the analysis results obtained by the analysis unit. For example, it can adjust traffic times based on traffic volume and set peak hours. Step 4: The implementation unit reflects the content proposed by the proposal unit in the traffic light. For example, the proposed content can be quickly reflected in the traffic light by using a traffic light program update method or a remote operation procedure. Step 5: The real-time adjustment unit adjusts the lighting time of traffic lights in real time based on the data collected in the central server. For example, it can adjust the lighting time of traffic lights in real time using dynamic adjustments based on traffic conditions or predictive algorithms.

[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 a 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 type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[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. A collection unit collects data from recording monitors or surveillance cameras installed at each traffic light, car navigation systems, and smartphones. an analysis unit that analyzes the data collected by the collection unit and grasps traffic volume and the number of pedestrians; a proposal unit that proposes appropriate switching times for each traffic signal and pedestrian-vehicle separated traffic signals based on the analysis results obtained by the analysis unit; an implementation unit that reflects the content proposed by the proposal unit in a traffic light; and a real-time adjustment unit that adjusts the lighting time of traffic lights in real time based on data collected in a central server. A system characterized by:

2. The collecting unit Collecting information from surveillance cameras installed on roads, location information from car navigation systems or dashcams, and location information from smartphones held by passengers and pedestrians 2. The system of claim 1.

3. The analysis unit Analyzing the collected data to understand traffic volume and pedestrian numbers 2. The system of claim 1.

4. The proposal unit Based on the analysis results, we will propose appropriate switching times for each signal and separate pedestrian and vehicle traffic signals.

2. The system of claim 1.

5. The mounting section Reflect the proposed content in traffic lights 2. The system of claim 1.

6. The real-time adjustment unit Traffic light illumination times are adjusted in real time based on data collected on a central server.

2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze past traffic data and select the optimal data collection method 2. The system of claim 1.

Images