system

Abstract

The system according to this embodiment aims to analyze traffic congestion information and guide the user to the shortest lane to their destination. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, a calculation unit, and a guidance unit. The acquisition unit acquires images from a camera. The analysis unit analyzes the images acquired by the acquisition unit and identifies congested lanes. The calculation unit calculates the shortest lane to the destination based on the information about the congested lanes identified by the analysis unit. The guidance unit guides the user to the shortest lane calculated by the calculation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately select the optimal lane based on congestion information, and there is room for improvement.

[0005] The system according to this embodiment aims to analyze traffic congestion information and guide the user to the shortest lane to their destination. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a calculation unit, and a guidance unit. The acquisition unit acquires images from a camera. The analysis unit analyzes the images acquired by the acquisition unit and identifies congested lanes. The calculation unit calculates the shortest lane to the destination based on the information about the congested lanes identified by the analysis unit. The guidance unit guides the user to the shortest lane calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze traffic congestion information and provide guidance on the shortest lane to a destination. [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 of form 1) A congestion avoidance system according to an embodiment of the present invention utilizes a generation AI to analyze congested lanes and guide the user to the shortest lane to a destination. In this congestion avoidance system, the generation AI analyzes road images and identifies congested lanes. Next, based on the information on the identified congested lanes, the system calculates the shortest lane to the destination. Finally, the system guides the user to the calculated shortest lane. This mechanism allows the user to avoid traffic jams and arrive at the destination efficiently. For example, when the generation AI analyzes road images, it analyzes the congestion status of each lane on the road and identifies congested lanes. The generation AI analyzes road images captured by a camera and measures the density and speed of vehicles in each lane. This allows the system to identify which lanes are congested. Next, based on the information on the identified congested lanes, the system calculates the shortest lane to the destination. The generation AI prioritizes lanes that are not congested and calculates the shortest route. For example, if there are multiple lanes, the system selects the lane that is not congested and calculates a route that passes through that lane. This allows the user to avoid traffic jams and arrive at the destination efficiently. Finally, the system guides the user to the calculated shortest lane. The generation AI sends the calculated shortest lane to a navigation system and guides the user. For example, a car navigation system displays information about the shortest lane and instructs the user to use that lane. This allows the user to avoid traffic jams and arrive at their destination efficiently. For example, to avoid traffic jams during rush hour, the generation AI can guide the user to the shortest lane, thereby shortening commuting time. Also, to avoid traffic jams while traveling, the generation AI can guide the user to the shortest lane, thereby reducing travel stress. In this way, the congestion avoidance system allows the user to avoid traffic jams and arrive at their destination efficiently.

[0029] A congestion avoidance system according to an embodiment includes an acquisition unit, an analysis unit, a calculation unit, and a guidance unit. The acquisition unit acquires images from a camera. For example, the acquisition unit can acquire images in real time from cameras installed at various locations on a road. The acquisition unit can also adjust the resolution of the camera to acquire optimal images. For example, the acquisition unit can acquire high-resolution images when the road is congested and low-resolution images when the road is not congested. The analysis unit analyzes the images acquired by the acquisition unit and identifies congested lanes. For example, the analysis unit can measure the density and speed of vehicles in each lane using image recognition technology. The analysis unit can also predict current congestion conditions by referring to past congestion data. For example, the analysis unit can predict current congestion conditions based on past congestion data. The calculation unit calculates the shortest lane to a destination based on information about congested lanes identified by the analysis unit. For example, the calculation unit can prioritize non-congested lanes and calculate the shortest route. The calculation unit can also predict an optimal route by referring to past route data. For example, the calculation unit can predict an optimal route based on past route data. The guidance unit guides the user along the shortest lane calculated by the calculation unit. For example, the guidance unit can transmit information about the calculated shortest lane to a navigation system and provide guidance to the user. The guidance unit can also estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions. For example, the guidance unit can provide guidance in a calm voice when the user is nervous, and in a cheerful voice when the user is relaxed. This allows the congestion avoidance system according to the embodiment to allow the user to avoid congestion and arrive at their destination efficiently.

[0030] The acquisition unit can acquire images in real time from cameras installed at various points along the road. Real time means, for example, that the images are updated every few seconds. The acquisition unit can acquire images taken by cameras in real time and transmit them to the analysis unit. The acquisition unit can also adjust the camera resolution to acquire the optimal image. For example, the acquisition unit can acquire high-resolution images when the road is congested and low-resolution images when it is not congested. This allows for real-time understanding of the road conditions. Some or all of the above processing in the acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the acquisition unit can input image data acquired from cameras into a generation AI and have the generation AI perform the analysis of the image data.

[0031] The analysis unit can measure the density and speed of vehicles in each lane using image recognition technology. Image recognition technology refers to technologies such as deep learning and pattern recognition. The analysis unit can measure the density and speed of vehicles in each lane using image recognition technology. For example, the analysis unit can analyze images captured by a camera and measure the density of vehicles in each lane. The analysis unit can also analyze images captured by a camera and measure the speed of vehicles in each lane. This allows for an accurate understanding of the congestion situation in each lane. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input image data acquired from a camera into a generative AI and have the generative AI perform the measurement of vehicle density and speed.

[0032] The calculation unit can preferentially select lanes that are not congested and calculate the shortest route. An example of a lane that is not congested is a lane with a low vehicle density and a high vehicle speed. The calculation unit can preferentially select lanes that are not congested and calculate the shortest route. For example, when there are multiple lanes, the calculation unit can select a lane that is not congested and calculate a route that passes through that lane. The calculation unit can also predict an optimal route by referring to past route data. For example, the calculation unit can predict an optimal route based on past route data. This makes it possible to calculate the shortest route that avoids traffic congestion. Some or all of the above-described processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the calculation unit can input information about congested lanes obtained from the analysis unit into the generation AI and cause the generation AI to calculate the shortest route.

[0033] The guidance unit can transmit the calculated information about the shortest lane to a navigation system and provide guidance to the user. A navigation system refers to a system having functions such as GPS, map data, and voice guidance. The guidance unit can transmit the calculated information about the shortest lane to a navigation system and provide guidance to the user. For example, the guidance unit can display information about the shortest lane on a car navigation system and instruct the user to use that lane. The guidance unit can also estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions. For example, the guidance unit can provide guidance in a calm voice when the user is nervous and in a cheerful voice when the user is relaxed. This allows the user to be provided with information about the shortest lane and arrive at their destination efficiently. Some or all of the above-described processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input information about the shortest lane obtained from the calculation unit to a generation AI and cause the generation AI to adjust the way the guidance is presented.

[0034] The acquisition unit can acquire road weather conditions in real time and adjust the frequency of image acquisition. Weather conditions refer to conditions such as sunny, rainy, or snowy. For example, the acquisition unit can increase the frequency of image acquisition during rainy weather to quickly grasp changes in road conditions. Conversely, it can decrease the frequency of image acquisition during sunny weather to provide only the minimum necessary information. Furthermore, on snowy days, the acquisition unit can increase the frequency of image acquisition to identify slippery areas. In this way, by adjusting the frequency of image acquisition according to the weather conditions, more accurate information can be provided. Weather conditions are acquired, for example, using weather sensors or weather data. Some or all of the above processing in the acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the acquisition unit can input weather data acquired from weather sensors into a generation AI and have the generation AI adjust the frequency of image acquisition.

[0035] The acquisition unit can dynamically change the resolution of the acquired images according to the road congestion status. Congestion status refers to, for example, vehicle density, speed, and dwell time. For example, the acquisition unit can acquire high-resolution images when congested to provide detailed information. Conversely, the acquisition unit can acquire low-resolution images when not congested to reduce processing load. Furthermore, the acquisition unit can dynamically adjust the resolution according to the degree of congestion to provide optimal information. In this way, by adjusting the image resolution according to the congestion status, optimal information can be provided. Adjustment of image resolution is performed, for example, by changing the number of pixels or image quality settings. Some or all of the above processing in the acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the acquisition unit can input congestion status data into a generation AI and have the generation AI perform the adjustment of the image resolution.

[0036] The acquisition unit can acquire images from the sky using a drone. A drone refers to, for example, an unmanned aerial vehicle. The acquisition unit can, for example, acquire road conditions over a wide area in real time using a drone. The acquisition unit can also acquire specific congestion locations in detail using a drone. Furthermore, the acquisition unit can grasp the overall congestion status of roads using a drone. As a result, road conditions over a wide area can be grasped in real time by using a drone. The drone is used by, for example, adjusting the flight altitude and camera performance. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input image data acquired from a drone into a generation AI and have the generation AI analyze the image.

[0037] The acquisition unit can improve the accuracy of image acquisition by integrating data from other traffic information systems. Examples of other traffic information systems include VICS (Vehicle Information and Communications System) and ITS (Intelligent Transport Systems). The acquisition unit can, for example, integrate congestion information from other traffic information systems to improve the accuracy of image acquisition. The acquisition unit can also integrate accident information from other traffic information systems to improve the accuracy of image acquisition. Furthermore, the acquisition unit can integrate construction information from other traffic information systems to improve the accuracy of image acquisition. In this way, the accuracy of image acquisition is improved by integrating data from other traffic information systems. Data integration is performed, for example, using a database or an API. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input data acquired from other traffic information systems into the generation AI and cause the generation AI to improve the accuracy of image acquisition.

[0038] The analysis unit can predict current traffic congestion by referring to past traffic congestion data during analysis. Past traffic congestion data refers to, for example, databases and historical data. The analysis unit can predict current traffic congestion based on past traffic congestion data. Furthermore, the analysis unit can predict traffic congestion for specific time periods based on past traffic congestion data. In addition, the analysis unit can predict traffic congestion for specific days of the week based on past traffic congestion data. This allows for the provision of more accurate information by predicting current traffic congestion based on past data. Data referencing is performed, for example, using database queries or APIs. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input past traffic congestion data into a generation AI and have the generation AI perform a prediction of current traffic congestion.

[0039] The analysis unit can apply different analysis algorithms depending on the type of vehicle during analysis. Vehicle types include, for example, passenger cars, trucks, and buses. For example, the analysis unit can apply a specific algorithm to analyze truck congestion. It can also apply a specific algorithm to analyze bus congestion. Furthermore, it can apply a specific algorithm to analyze passenger car congestion. This allows for more accurate information to be provided by performing analysis according to the type of vehicle. The application of analysis algorithms is carried out using technologies such as deep learning and pattern recognition. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input vehicle type data into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0040] The analysis unit can analyze audio data to supplement information about road congestion. Audio data refers to, for example, vehicle engine sounds, horn sounds, and ambient sounds. The analysis unit can, for example, analyze vehicle engine sounds to supplement information about road congestion. It can also analyze horn sounds to supplement information about road congestion. Furthermore, it can analyze ambient sounds to supplement information about road congestion. As a result, by analyzing audio data, road congestion can be grasped more accurately. Audio data analysis is performed, for example, using a microphone or speech recognition technology. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input audio data into a generative AI and have the generative AI perform the analysis of the audio data.

[0041] The analysis unit can improve the accuracy of its analysis by integrating data from other traffic information systems. These other traffic information systems include, for example, VICS (Vehicle Information and Communication System) and ITS (Intelligent Transport Systems). The analysis unit can, for example, integrate congestion information from other traffic information systems to improve the accuracy of its analysis. It can also integrate accident information from other traffic information systems to improve the accuracy of its analysis. Furthermore, it can integrate construction information from other traffic information systems to improve the accuracy of its analysis. In this way, integrating data from other traffic information systems improves the accuracy of the analysis. Data integration is performed, for example, using a database or API. Some or all of the above-described processes in the analysis unit may be performed, for example, using a generation AI, or without using a generation AI. For example, the analysis unit can input data acquired from other traffic information systems into a generation AI and have the generation AI perform the analysis accuracy improvement.

[0042] During calculation, the calculation unit can predict the optimal route by referring to past route data. Past route data refers to, for example, a database or historical data. The calculation unit can, for example, predict the optimal route based on the past route data. The calculation unit can also predict the optimal route for a specific time period based on the past route data. Furthermore, the calculation unit can predict the optimal route for a specific day of the week based on the past route data. This allows for predicting the optimal route based on past data, thereby providing a more accurate route. Data is referenced, for example, using a database query or an API. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the calculation unit can input past route data into the generation AI and cause the generation AI to predict the optimal route.

[0043] The calculation unit can calculate the shortest route by taking into account road construction information and accident information during calculation. Construction information and accident information refer to, for example, scheduled construction, construction locations, accident locations, and types of accidents. The calculation unit can, for example, calculate the shortest route by taking into account construction information. The calculation unit can also calculate the shortest route by taking into account accident information. Furthermore, the calculation unit can calculate the shortest route by integrating construction information and accident information. This makes it possible to provide a more accurate shortest route by taking construction information and accident information into account. Information is acquired, for example, using a database or an API. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the calculation unit can input construction information and accident information into the generation AI and have the generation AI calculate the shortest route.

[0044] The calculation unit can integrate data from other traffic information systems to improve the accuracy of the shortest route. Examples of other traffic information systems include VICS (Vehicle Information and Communications System) and ITS (Intelligent Transport Systems). The calculation unit can, for example, integrate congestion information from other traffic information systems to improve the accuracy of the shortest route. The calculation unit can also integrate accident information from other traffic information systems to improve the accuracy of the shortest route. Furthermore, the calculation unit can integrate construction information from other traffic information systems to improve the accuracy of the shortest route. In this way, the accuracy of the shortest route is improved by integrating data from other traffic information systems. Data integration is performed, for example, using a database or an API. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the calculation unit can input data obtained from other traffic information systems into the generation AI and cause the generation AI to improve the accuracy of the shortest route.

[0045] The calculation unit can calculate the shortest route taking into account operation information of public transportation. Public transportation operation information refers to, for example, operation schedules and operation status. The calculation unit can calculate the shortest route taking into account, for example, operation information of public transportation. The calculation unit can also calculate the shortest route taking into account delay information of public transportation. Furthermore, the calculation unit can calculate the shortest route taking into account operation schedules of public transportation. This makes it possible to provide a more accurate shortest route by taking into account operation information of public transportation. Information is acquired using, for example, a database or an API. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the calculation unit can input operation information of public transportation into the generation AI and cause the generation AI to calculate the shortest route.

[0046] When providing guidance, the guidance unit can select the optimal guidance method by referring to the user's past driving history. Past driving history refers to, for example, driving records and driving data. The guidance unit can select the optimal guidance method based on, for example, the user's past driving history. The guidance unit can also prioritize guidance to a specific route based on the user's past driving history. Furthermore, the guidance unit can analyze the user's past driving history and select the most efficient guidance method. This makes it possible to provide the optimal guidance method based on the user's past driving history. The driving history is referenced using, for example, a database or an API. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the guidance unit can input past driving history data into the generation AI and have the generation AI select a guidance method.

[0047] The guidance unit can dynamically change the guidance content according to the user's current driving situation when providing guidance. The current driving situation refers to, for example, speed, location, traffic conditions, etc. For example, when the user is driving on a highway, the guidance unit can prioritize guidance to the next exit. Furthermore, when the user is driving in an urban area, the guidance unit can prioritize guidance at traffic lights and intersections. Furthermore, when the user is approaching a parking lot, the guidance unit can provide guidance about parking spaces. This allows for dynamic change of the guidance content according to the user's driving situation, thereby providing more appropriate guidance. The driving situation is acquired using, for example, a GPS or a sensor. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI. For example, the guidance unit can input current driving situation data to the generation AI and cause the generation AI to dynamically change the guidance content.

[0048] The guidance unit can provide guidance to the user by combining audio guidance and visual guidance. Audio guidance refers to guidance using, for example, voice synthesis technology or voice recognition technology. Visual guidance refers to guidance using, for example, display or AR technology. The guidance unit can, for example, provide information on the next intersection using audio guidance and display a detailed map using visual guidance. The guidance unit can also provide traffic congestion information using audio guidance and display a detour route using visual guidance. Furthermore, the guidance unit can provide parking information using audio guidance and display the location of parking spaces using visual guidance. This allows for more effective guidance to be provided by combining audio guidance and visual guidance. Some or all of the above-described processing in the guidance unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the guidance unit can input data for audio guidance and visual guidance into the generation AI and have the generation AI execute the combination of guidance.

[0049] The guidance unit can improve the accuracy of guidance by cooperating with other navigation systems. Examples of other navigation systems include GPS systems and map databases. The guidance unit can, for example, integrate traffic congestion information from other navigation systems to improve the accuracy of guidance. The guidance unit can also integrate accident information from other navigation systems to improve the accuracy of guidance. Furthermore, the guidance unit can integrate construction information from other navigation systems to improve the accuracy of guidance. This improves the accuracy of guidance by cooperating with other navigation systems. Data integration is performed, for example, using a database or API. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the guidance unit can input data obtained from other navigation systems into the generation AI and have the generation AI improve the accuracy of guidance.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The traffic congestion avoidance system can further learn the user's driving style and provide individually optimized routes. For example, the acquisition unit collects the user's past driving data, and the analysis unit analyzes this data to identify the user's driving patterns. Based on the identified driving patterns, the calculation unit can calculate the optimal route, taking into account the routes the user prefers and routes they want to avoid. The guidance unit guides the user along the calculated route, providing guidance tailored to the user's driving style. This allows the user to arrive at their destination more comfortably.

[0052] The acquisition unit can further improve the accuracy of image analysis by acquiring road lighting conditions in real time. For example, in dark places such as at night or inside tunnels, it can adjust the camera's exposure settings according to the lighting conditions to acquire bright images. In addition, if the lighting is insufficient, the acquisition unit can use an infrared camera to acquire images. This makes it possible to accurately understand road conditions even in dark environments.

[0053] The analysis unit can further analyze audio data to supplement information about road congestion. For example, it can analyze vehicle engine sounds and horn sounds to estimate the degree of road congestion. The analysis unit can also analyze ambient sounds to detect accidents and construction work. This allows for a more accurate understanding of road conditions by utilizing audio data.

[0054] The acquisition unit can further acquire aerial images using a drone. For example, it can use a drone to acquire real-time images of road conditions over a wide area and to acquire detailed information on specific congested areas. It can also use a drone to grasp the overall congestion situation on roads. In this way, by using a drone, it is possible to grasp road conditions over a wide area in real time.

[0055] The acquisition unit can further improve the accuracy of image acquisition by integrating data from other traffic information systems. For example, it can improve the accuracy of image acquisition by integrating traffic congestion and accident information from traffic information systems such as VICS and ITS. It can also improve the accuracy of image acquisition by integrating construction information from other traffic information systems. In this way, the accuracy of image acquisition is improved by integrating data from other traffic information systems.

[0056] The calculation unit can further calculate the shortest route by considering public transportation operation information. For example, it can calculate the optimal route by considering the operating schedule and status of public transportation. It can also calculate the shortest route by considering delay information for public transportation. As a result, by considering public transportation operation information, it is possible to provide a more accurate shortest route.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The acquisition unit acquires images from cameras. For example, the acquisition unit can acquire images in real time from cameras installed at various points along the road. The acquisition unit can also adjust the camera resolution to acquire the optimal image. For example, the acquisition unit can acquire high-resolution images when the road is congested and low-resolution images when it is not congested. Step 2: The analysis unit analyzes the images acquired by the acquisition unit to identify congested lanes. For example, the analysis unit can measure the density and speed of vehicles in each lane using image recognition technology. The analysis unit can also predict the current congestion situation by referring to past congestion data. Step 3: The calculation unit calculates the shortest lane to the destination based on the information about congested lanes identified by the analysis unit. For example, the calculation unit can prioritize selecting uncongested lanes and calculate the shortest route. The calculation unit can also predict the optimal route by referring to past route data. Step 4: The guidance unit guides the user along the shortest lane calculated by the calculation unit. For example, the guidance unit can transmit information about the calculated shortest lane to a navigation system and provide guidance to the user. The guidance unit can also estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions.

[0059] (Example 2) A congestion avoidance system according to an embodiment of the present invention utilizes a generation AI to analyze congested lanes and guide the user to the shortest lane to a destination. In this congestion avoidance system, the generation AI analyzes road images and identifies congested lanes. Next, based on the information on the identified congested lanes, the system calculates the shortest lane to the destination. Finally, the system guides the user to the calculated shortest lane. This mechanism allows the user to avoid traffic jams and arrive at the destination efficiently. For example, when the generation AI analyzes road images, it analyzes the congestion status of each lane on the road and identifies congested lanes. The generation AI analyzes road images captured by a camera and measures the density and speed of vehicles in each lane. This allows the system to identify which lanes are congested. Next, based on the information on the identified congested lanes, the system calculates the shortest lane to the destination. The generation AI prioritizes lanes that are not congested and calculates the shortest route. For example, if there are multiple lanes, the system selects the lane that is not congested and calculates a route that passes through that lane. This allows the user to avoid traffic jams and arrive at the destination efficiently. Finally, the system guides the user to the calculated shortest lane. The generation AI sends the calculated shortest lane to a navigation system and guides the user. For example, a car navigation system displays information about the shortest lane and instructs the user to use that lane. This allows the user to avoid traffic jams and arrive at their destination efficiently. For example, to avoid traffic jams during rush hour, the generation AI can guide the user to the shortest lane, thereby shortening commuting time. Also, to avoid traffic jams while traveling, the generation AI can guide the user to the shortest lane, thereby reducing travel stress. In this way, the congestion avoidance system allows the user to avoid traffic jams and arrive at their destination efficiently.

[0060] A congestion avoidance system according to an embodiment includes an acquisition unit, an analysis unit, a calculation unit, and a guidance unit. The acquisition unit acquires images from a camera. For example, the acquisition unit can acquire images in real time from cameras installed at various locations on a road. The acquisition unit can also adjust the resolution of the camera to acquire optimal images. For example, the acquisition unit can acquire high-resolution images when the road is congested and low-resolution images when the road is not congested. The analysis unit analyzes the images acquired by the acquisition unit and identifies congested lanes. For example, the analysis unit can measure the density and speed of vehicles in each lane using image recognition technology. The analysis unit can also predict current congestion conditions by referring to past congestion data. For example, the analysis unit can predict current congestion conditions based on past congestion data. The calculation unit calculates the shortest lane to a destination based on information about congested lanes identified by the analysis unit. For example, the calculation unit can prioritize non-congested lanes and calculate the shortest route. The calculation unit can also predict an optimal route by referring to past route data. For example, the calculation unit can predict an optimal route based on past route data. The guidance unit guides the user along the shortest lane calculated by the calculation unit. For example, the guidance unit can transmit information about the calculated shortest lane to a navigation system and provide guidance to the user. The guidance unit can also estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions. For example, the guidance unit can provide guidance in a calm voice when the user is nervous, and in a cheerful voice when the user is relaxed. This allows the congestion avoidance system according to the embodiment to allow the user to avoid congestion and arrive at their destination efficiently.

[0061] The acquisition unit can acquire images in real time from cameras installed at various points along the road. Real time means, for example, that the images are updated every few seconds. The acquisition unit can acquire images taken by cameras in real time and transmit them to the analysis unit. The acquisition unit can also adjust the camera resolution to acquire the optimal image. For example, the acquisition unit can acquire high-resolution images when the road is congested and low-resolution images when it is not congested. This allows for real-time understanding of the road conditions. Some or all of the above processing in the acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the acquisition unit can input image data acquired from cameras into a generation AI and have the generation AI perform the analysis of the image data.

[0062] The analysis unit can measure the density and speed of vehicles in each lane using image recognition technology. Image recognition technology refers to technologies such as deep learning and pattern recognition. The analysis unit can measure the density and speed of vehicles in each lane using image recognition technology. For example, the analysis unit can analyze images captured by a camera and measure the density of vehicles in each lane. The analysis unit can also analyze images captured by a camera and measure the speed of vehicles in each lane. This allows for an accurate understanding of the congestion situation in each lane. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input image data acquired from a camera into a generative AI and have the generative AI perform the measurement of vehicle density and speed.

[0063] The calculation unit can preferentially select lanes that are not congested and calculate the shortest route. An example of a lane that is not congested is a lane with a low vehicle density and a high vehicle speed. The calculation unit can preferentially select lanes that are not congested and calculate the shortest route. For example, when there are multiple lanes, the calculation unit can select a lane that is not congested and calculate a route that passes through that lane. The calculation unit can also predict an optimal route by referring to past route data. For example, the calculation unit can predict an optimal route based on past route data. This makes it possible to calculate the shortest route that avoids traffic congestion. Some or all of the above-described processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the calculation unit can input information about congested lanes obtained from the analysis unit into the generation AI and cause the generation AI to calculate the shortest route.

[0064] The guidance unit can transmit the calculated information about the shortest lane to a navigation system and provide guidance to the user. A navigation system refers to a system having functions such as GPS, map data, and voice guidance. The guidance unit can transmit the calculated information about the shortest lane to a navigation system and provide guidance to the user. For example, the guidance unit can display information about the shortest lane on a car navigation system and instruct the user to use that lane. The guidance unit can also estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions. For example, the guidance unit can provide guidance in a calm voice when the user is nervous and in a cheerful voice when the user is relaxed. This allows the user to be provided with information about the shortest lane and arrive at their destination efficiently. Some or all of the above-described processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input information about the shortest lane obtained from the calculation unit to a generation AI and cause the generation AI to adjust the way the guidance is presented.

[0065] The acquisition unit can estimate the user's emotion and adjust the timing of image acquisition based on the estimated user's emotion. The user's emotion refers to, for example, a state such as stress, relaxation, or hurry. For example, when the user is stressed, the acquisition unit can increase the frequency of image acquisition and provide more detailed information. Furthermore, when the user is relaxed, the acquisition unit can reduce the frequency of image acquisition and provide the minimum necessary information. Furthermore, when the user is in a hurry, the acquisition unit can acquire images in real time and provide information quickly. This allows for adjusting the timing of image acquisition according to the user's emotion, thereby providing more appropriate information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 acquisition unit may be performed using, for example, the generation AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of image acquisition.

[0066] The acquisition unit can acquire road weather conditions in real time and adjust the frequency of image acquisition. Weather conditions refer to conditions such as sunny, rainy, or snowy. For example, the acquisition unit can increase the frequency of image acquisition during rainy weather to quickly grasp changes in road conditions. Conversely, it can decrease the frequency of image acquisition during sunny weather to provide only the minimum necessary information. Furthermore, on snowy days, the acquisition unit can increase the frequency of image acquisition to identify slippery areas. In this way, by adjusting the frequency of image acquisition according to the weather conditions, more accurate information can be provided. Weather conditions are acquired, for example, using weather sensors or weather data. Some or all of the above processing in the acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the acquisition unit can input weather data acquired from weather sensors into a generation AI and have the generation AI adjust the frequency of image acquisition.

[0067] The acquisition unit can dynamically change the resolution of the acquired images according to the road congestion status. Congestion status refers to, for example, vehicle density, speed, and dwell time. For example, the acquisition unit can acquire high-resolution images when congested to provide detailed information. Conversely, the acquisition unit can acquire low-resolution images when not congested to reduce processing load. Furthermore, the acquisition unit can dynamically adjust the resolution according to the degree of congestion to provide optimal information. In this way, by adjusting the image resolution according to the congestion status, optimal information can be provided. Adjustment of image resolution is performed, for example, by changing the number of pixels or image quality settings. Some or all of the above processing in the acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the acquisition unit can input congestion status data into a generation AI and have the generation AI perform the adjustment of the image resolution.

[0068] The acquisition unit can estimate the user's emotions and determine the priority of images to be acquired based on the estimated user emotions. The user's emotions refer to, for example, states such as stress, relaxation, and hurry. For example, when the user is stressed, the acquisition unit can prioritize acquiring important information. Furthermore, when the user is relaxed, the acquisition unit can prioritize acquiring detailed information. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring information that can be acquired quickly. This allows for more important information to be provided by determining the priority of images 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. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of images.

[0069] The acquisition unit can acquire images from the sky using a drone. A drone refers to, for example, an unmanned aerial vehicle. The acquisition unit can, for example, acquire road conditions over a wide area in real time using a drone. The acquisition unit can also acquire specific congestion locations in detail using a drone. Furthermore, the acquisition unit can grasp the overall congestion status of roads using a drone. As a result, road conditions over a wide area can be grasped in real time by using a drone. The drone is used by, for example, adjusting the flight altitude and camera performance. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input image data acquired from a drone into a generation AI and have the generation AI analyze the image.

[0070] The acquisition unit can improve the accuracy of image acquisition by integrating data from other traffic information systems. Examples of other traffic information systems include VICS (Vehicle Information and Communications System) and ITS (Intelligent Transport Systems). The acquisition unit can, for example, integrate congestion information from other traffic information systems to improve the accuracy of image acquisition. The acquisition unit can also integrate accident information from other traffic information systems to improve the accuracy of image acquisition. Furthermore, the acquisition unit can integrate construction information from other traffic information systems to improve the accuracy of image acquisition. In this way, the accuracy of image acquisition is improved by integrating data from other traffic information systems. Data integration is performed, for example, using a database or an API. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input data acquired from other traffic information systems into the generation AI and cause the generation AI to improve the accuracy of image acquisition.

[0071] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The user's emotions refer to, for example, states such as tension, relaxation, or hurry. For example, if the user is tensioned, 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 hurrying, the analysis unit can provide a display method that focuses on the main points. This allows for adjusting the display method of the analysis results according to the user's emotions, thereby providing more appropriate information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0072] The analysis unit can predict current traffic congestion by referring to past traffic congestion data during analysis. Past traffic congestion data refers to, for example, databases and historical data. The analysis unit can predict current traffic congestion based on past traffic congestion data. Furthermore, the analysis unit can predict traffic congestion for specific time periods based on past traffic congestion data. In addition, the analysis unit can predict traffic congestion for specific days of the week based on past traffic congestion data. This allows for the provision of more accurate information by predicting current traffic congestion based on past data. Data referencing is performed, for example, using database queries or APIs. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input past traffic congestion data into a generation AI and have the generation AI perform a prediction of current traffic congestion.

[0073] The analysis unit can apply different analysis algorithms depending on the type of vehicle during analysis. Vehicle types include, for example, passenger cars, trucks, and buses. For example, the analysis unit can apply a specific algorithm to analyze truck congestion. It can also apply a specific algorithm to analyze bus congestion. Furthermore, it can apply a specific algorithm to analyze passenger car congestion. This allows for more accurate information to be provided by performing analysis according to the type of vehicle. The application of analysis algorithms is carried out using technologies such as deep learning and pattern recognition. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input vehicle type data into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0074] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. User emotions refer to states such as stress, relaxation, or urgency. For example, if the user is stressed, the analysis unit can prioritize displaying important information. If the user is relaxed, the analysis unit can prioritize displaying detailed information. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying information that can be quickly retrieved. In this way, by prioritizing analysis results according to the user's emotions, more important information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis results.

[0075] The analysis unit can analyze audio data to supplement information about road congestion. Audio data refers to, for example, vehicle engine sounds, horn sounds, and ambient sounds. The analysis unit can, for example, analyze vehicle engine sounds to supplement information about road congestion. It can also analyze horn sounds to supplement information about road congestion. Furthermore, it can analyze ambient sounds to supplement information about road congestion. As a result, by analyzing audio data, road congestion can be grasped more accurately. Audio data analysis is performed, for example, using a microphone or speech recognition technology. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input audio data into a generative AI and have the generative AI perform the analysis of the audio data.

[0076] The analysis unit can improve the accuracy of its analysis by integrating data from other traffic information systems. These other traffic information systems include, for example, VICS (Vehicle Information and Communication System) and ITS (Intelligent Transport Systems). The analysis unit can, for example, integrate congestion information from other traffic information systems to improve the accuracy of its analysis. It can also integrate accident information from other traffic information systems to improve the accuracy of its analysis. Furthermore, it can integrate construction information from other traffic information systems to improve the accuracy of its analysis. In this way, integrating data from other traffic information systems improves the accuracy of the analysis. Data integration is performed, for example, using a database or API. Some or all of the above-described processes in the analysis unit may be performed, for example, using a generation AI, or without using a generation AI. For example, the analysis unit can input data acquired from other traffic information systems into a generation AI and have the generation AI perform the analysis accuracy improvement.

[0077] The calculation unit can estimate the user's emotions and adjust the shortest lane calculation method based on the estimated user emotions. The user's emotions refer to, for example, states such as stress, relaxation, and hurry. For example, if the user is feeling stressed, the calculation unit can prioritize calculating the shortest route. Furthermore, if the user is relaxed, the calculation unit can prioritize calculating a scenic route. Furthermore, if the user is in a hurry, the calculation unit can prioritize calculating a route that will allow quick arrival. This allows for adjusting the shortest lane calculation method according to the user's emotions, thereby providing a more appropriate route. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the calculation unit may be performed using, for example, the generation AI. For example, the calculation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the shortest lane calculation method.

[0078] During calculation, the calculation unit can predict the optimal route by referring to past route data. Past route data refers to, for example, a database or historical data. The calculation unit can, for example, predict the optimal route based on the past route data. The calculation unit can also predict the optimal route for a specific time period based on the past route data. Furthermore, the calculation unit can predict the optimal route for a specific day of the week based on the past route data. This allows for predicting the optimal route based on past data, thereby providing a more accurate route. Data is referenced, for example, using a database query or an API. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the calculation unit can input past route data into the generation AI and cause the generation AI to predict the optimal route.

[0079] The calculation unit can calculate the shortest route by taking into account road construction information and accident information during calculation. Construction information and accident information refer to, for example, scheduled construction, construction locations, accident locations, and types of accidents. The calculation unit can, for example, calculate the shortest route by taking into account construction information. The calculation unit can also calculate the shortest route by taking into account accident information. Furthermore, the calculation unit can calculate the shortest route by integrating construction information and accident information. This makes it possible to provide a more accurate shortest route by taking construction information and accident information into account. Information is acquired, for example, using a database or an API. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the calculation unit can input construction information and accident information into the generation AI and have the generation AI calculate the shortest route.

[0080] The calculation unit can estimate the user's emotions and determine the priority of the shortest lane based on the estimated emotions. User emotions refer to states such as stress, relaxation, or urgency. For example, if the user is stressed, the calculation unit can prioritize displaying the shortest route. If the user is relaxed, the calculation unit can prioritize displaying a route with good scenery. Furthermore, if the user is in a hurry, the calculation unit can prioritize displaying a route that allows for quick arrival. In this way, by determining the priority of the shortest lane according to the user's emotions, a more appropriate route can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the calculation unit may be performed using a generative AI, or not using a generative AI. For example, the calculation unit can input user emotion data into a generative AI and have the generative AI perform the determination of the priority of the shortest lane.

[0081] The calculation unit can integrate data from other traffic information systems to improve the accuracy of the shortest route. Examples of other traffic information systems include VICS (Vehicle Information and Communications System) and ITS (Intelligent Transport Systems). The calculation unit can, for example, integrate congestion information from other traffic information systems to improve the accuracy of the shortest route. The calculation unit can also integrate accident information from other traffic information systems to improve the accuracy of the shortest route. Furthermore, the calculation unit can integrate construction information from other traffic information systems to improve the accuracy of the shortest route. In this way, the accuracy of the shortest route is improved by integrating data from other traffic information systems. Data integration is performed, for example, using a database or an API. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the calculation unit can input data obtained from other traffic information systems into the generation AI and cause the generation AI to improve the accuracy of the shortest route.

[0082] The calculation unit can calculate the shortest route taking into account operation information of public transportation. Public transportation operation information refers to, for example, operation schedules and operation status. The calculation unit can calculate the shortest route taking into account, for example, operation information of public transportation. The calculation unit can also calculate the shortest route taking into account delay information of public transportation. Furthermore, the calculation unit can calculate the shortest route taking into account operation schedules of public transportation. This makes it possible to provide a more accurate shortest route by taking into account operation information of public transportation. Information is acquired using, for example, a database or an API. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the calculation unit can input operation information of public transportation into the generation AI and cause the generation AI to calculate the shortest route.

[0083] The guidance unit can estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions. The user's emotions refer to, for example, states such as tension, relaxation, and hurry. For example, if the user is nervous, the guidance unit can provide guidance in a calm voice. Furthermore, if the user is relaxed, the guidance unit can provide guidance in a cheerful voice. Furthermore, if the user is in a hurry, the guidance unit can provide quick and concise guidance. This allows for more appropriate guidance to be provided by adjusting the way the guidance is presented 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. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the guidance unit may be performed using, for example, the generation AI. For example, the guidance unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the guidance is presented.

[0084] When providing guidance, the guidance unit can select the optimal guidance method by referring to the user's past driving history. Past driving history refers to, for example, driving records and driving data. The guidance unit can select the optimal guidance method based on, for example, the user's past driving history. The guidance unit can also prioritize guidance to a specific route based on the user's past driving history. Furthermore, the guidance unit can analyze the user's past driving history and select the most efficient guidance method. This makes it possible to provide the optimal guidance method based on the user's past driving history. The driving history is referenced using, for example, a database or an API. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the guidance unit can input past driving history data into the generation AI and have the generation AI select a guidance method.

[0085] The guidance unit can dynamically change the guidance content according to the user's current driving situation when providing guidance. The current driving situation refers to, for example, speed, location, traffic conditions, etc. For example, when the user is driving on a highway, the guidance unit can prioritize guidance to the next exit. Furthermore, when the user is driving in an urban area, the guidance unit can prioritize guidance at traffic lights and intersections. Furthermore, when the user is approaching a parking lot, the guidance unit can provide guidance about parking spaces. This allows for dynamic change of the guidance content according to the user's driving situation, thereby providing more appropriate guidance. The driving situation is acquired using, for example, a GPS or a sensor. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI. For example, the guidance unit can input current driving situation data to the generation AI and cause the generation AI to dynamically change the guidance content.

[0086] The guidance unit can estimate the user's emotions and determine the priority of guidance based on the estimated emotions. User emotions refer to states such as stress, relaxation, or urgency. For example, if the user is stressed, the guidance unit can prioritize providing important information. If the user is relaxed, the guidance unit can prioritize providing detailed information. Furthermore, if the user is in a hurry, the guidance unit can prioritize providing information that can be obtained quickly. In this way, by determining the priority of guidance according to the user's emotions, more important information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the guidance unit may be performed using a generative AI, or not using a generative AI. For example, the guidance unit can input user emotion data into a generative AI and have the generative AI determine the priority of guidance.

[0087] The guidance unit can provide guidance to the user by combining audio guidance and visual guidance. Audio guidance refers to guidance using, for example, voice synthesis technology or voice recognition technology. Visual guidance refers to guidance using, for example, display or AR technology. The guidance unit can, for example, provide information on the next intersection using audio guidance and display a detailed map using visual guidance. The guidance unit can also provide traffic congestion information using audio guidance and display a detour route using visual guidance. Furthermore, the guidance unit can provide parking information using audio guidance and display the location of parking spaces using visual guidance. This allows for more effective guidance to be provided by combining audio guidance and visual guidance. Some or all of the above-described processing in the guidance unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the guidance unit can input data for audio guidance and visual guidance into the generation AI and have the generation AI execute the combination of guidance.

[0088] The guidance unit can improve the accuracy of guidance by cooperating with other navigation systems. Examples of other navigation systems include GPS systems and map databases. The guidance unit can, for example, integrate traffic congestion information from other navigation systems to improve the accuracy of guidance. The guidance unit can also integrate accident information from other navigation systems to improve the accuracy of guidance. Furthermore, the guidance unit can integrate construction information from other navigation systems to improve the accuracy of guidance. This improves the accuracy of guidance by cooperating with other navigation systems. Data integration is performed, for example, using a database or API. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the guidance unit can input data obtained from other navigation systems into the generation AI and have the generation AI improve the accuracy of guidance. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, analysis unit, calculation unit, and guidance unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires an image of a road using the camera 42 of the smart device 14. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the acquired image to identify congested lanes. The calculation unit is realized by the identification processing unit 290 of the data processing device 12 and calculates the shortest lane based on information on the identified congested lanes. The guidance unit is realized by the control unit 46A of the smart device 14 and guides the user to the calculated shortest lane. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, calculation unit, and guidance unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires an image of a road using the camera 42 of the smart glasses 214. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the acquired image to identify congested lanes. The calculation unit is realized by the identification processing unit 290 of the data processing device 12 and calculates the shortest lane based on information on the identified congested lanes. The guidance unit is realized by the control unit 46A of the smart glasses 214 and guides the user to the calculated shortest lane. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, analysis unit, calculation unit, and guidance 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 acquisition unit acquires an image of a road using the camera 42 of the headset type terminal 314. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the acquired image to identify congested lanes. The calculation unit is realized by the identification processing unit 290 of the data processing device 12 and calculates the shortest lane based on information on the identified congested lanes. The guidance unit is realized by the control unit 46A of the headset type terminal 314 and guides the user to the calculated shortest lane. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, calculation unit, and guidance unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires images of the road using the camera 42 of the robot 414. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the acquired images to identify congested lanes. The calculation unit is realized by the identification processing unit 290 of the data processing device 12 and calculates the shortest lane based on information on the identified congested lanes. The guidance unit is realized by the control unit 46A of the robot 414 and guides the user to the calculated shortest lane.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The traffic congestion avoidance system can further learn the user's driving style and provide individually optimized routes. For example, the acquisition unit collects the user's past driving data, and the analysis unit analyzes this data to identify the user's driving patterns. Based on the identified driving patterns, the calculation unit can calculate the optimal route, taking into account the routes the user prefers and routes they want to avoid. The guidance unit guides the user along the calculated route, providing guidance tailored to the user's driving style. This allows the user to arrive at their destination more comfortably.

[0091] The acquisition unit can further improve the accuracy of image analysis by acquiring road lighting conditions in real time. For example, in dark places such as at night or inside tunnels, it can adjust the camera's exposure settings according to the lighting conditions to acquire bright images. In addition, if the lighting is insufficient, the acquisition unit can use an infrared camera to acquire images. This makes it possible to accurately understand road conditions even in dark environments.

[0092] The analysis unit can further analyze audio data to supplement information about road congestion. For example, it can analyze vehicle engine sounds and horn sounds to estimate the degree of road congestion. The analysis unit can also analyze ambient sounds to detect accidents and construction work. This allows for a more accurate understanding of road conditions by utilizing audio data.

[0093] The calculation unit can further estimate the user's emotions and calculate the optimal route based on the estimated user emotions. For example, if the user is feeling stressed, it can calculate a route that avoids traffic jams with priority, and if the user is relaxed, it can calculate a route with beautiful scenery with priority. This makes it possible to provide the optimal route according to the user's emotions.

[0094] The guidance unit can further estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions. For example, if the user is nervous, the guidance unit can provide guidance in a calm voice, and if the user is relaxed, the guidance unit can provide guidance in a cheerful voice. Also, if the user is in a hurry, the guidance unit can provide quick and concise guidance. This makes it possible to provide appropriate guidance according to the user's emotions.

[0095] The acquisition unit can further acquire aerial images using a drone. For example, it can use a drone to acquire real-time images of road conditions over a wide area and to acquire detailed information on specific congested areas. It can also use a drone to grasp the overall congestion situation on roads. In this way, by using a drone, it is possible to grasp road conditions over a wide area in real time.

[0096] The acquisition unit can further improve the accuracy of image acquisition by integrating data from other traffic information systems. For example, it can improve the accuracy of image acquisition by integrating traffic congestion and accident information from traffic information systems such as VICS and ITS. It can also improve the accuracy of image acquisition by integrating construction information from other traffic information systems. In this way, the accuracy of image acquisition is improved by integrating data from other traffic information systems.

[0097] The analysis unit can further estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided, and if the user is relaxed, a display method including detailed information can be provided. Also, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide appropriate information according to the user's emotions.

[0098] The calculation unit can further calculate the shortest route by considering public transportation operation information. For example, it can calculate the optimal route by considering the operating schedule and status of public transportation. It can also calculate the shortest route by considering delay information for public transportation. As a result, by considering public transportation operation information, it is possible to provide a more accurate shortest route.

[0099] The guidance unit can further estimate the user's emotions and determine the priority of guidance based on the estimated user's emotions. For example, if the user is feeling stressed, important information can be given priority, and if the user is relaxed, detailed information can be given priority. Also, if the user is in a hurry, information that can be obtained quickly can be given priority. This makes it possible to provide appropriate guidance according to the user's emotions.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The acquisition unit acquires images from cameras. For example, the acquisition unit can acquire images in real time from cameras installed at various points along the road. The acquisition unit can also adjust the camera resolution to acquire the optimal image. For example, the acquisition unit can acquire high-resolution images when the road is congested and low-resolution images when it is not congested. Step 2: The analysis unit analyzes the images acquired by the acquisition unit to identify congested lanes. For example, the analysis unit can measure the density and speed of vehicles in each lane using image recognition technology. The analysis unit can also predict the current congestion situation by referring to past congestion data. Step 3: The calculation unit calculates the shortest lane to the destination based on the information about congested lanes identified by the analysis unit. For example, the calculation unit can prioritize selecting uncongested lanes and calculate the shortest route. The calculation unit can also predict the optimal route by referring to past route data. Step 4: The guidance unit guides the user along the shortest lane calculated by the calculation unit. For example, the guidance unit can transmit information about the calculated shortest lane to a navigation system and provide guidance to the user. The guidance unit can also estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, the 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

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

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

[0149] In the 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.

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

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

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0153] 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.

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

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires an image from a camera; an analysis unit that analyzes the image acquired by the acquisition unit and identifies congested lanes; a calculation unit that calculates the shortest lane to the destination based on the information on the congested lanes identified by the analysis unit; a guidance unit that guides a user to the shortest lane calculated by the calculation unit. A system characterized by:

2. The acquisition unit Acquire images in real time from cameras installed at various points on the road 2. The system of claim 1.

3. The analysis unit Measure the density and speed of vehicles in each lane using image recognition technology 2. The system of claim 1.

4. The calculation unit Prioritize lanes with less traffic congestion and calculate the shortest route 2. The system of claim 1.

5. The guide unit is The calculated shortest lane information is sent to the navigation system to guide the user.

2. The system of claim 1.

6. The acquisition unit The system estimates the user's emotions and adjusts the timing of image acquisition based on the estimated user emotions.

2. The system of claim 1.

7. The acquisition unit Obtaining road weather conditions in real time and adjusting the frequency of image acquisition 2. The system of claim 1.

8. The acquisition unit Dynamically change the resolution of the images you capture depending on the road congestion situation.

2. The system of claim 1.

9. The acquisition unit Estimate the user's emotions and prioritize the images to be acquired based on the estimated user emotions.

2. The system of claim 1.

10. The acquisition unit Using a drone to capture images from the sky 2. The system of claim 1.

Images