system

Abstract

The system according to the embodiment aims to automatically generate explanatory text relating to road traffic conditions and efficiently create learning data. [Solution] A system according to an embodiment includes a collection unit, a generation unit, and a storage unit. The collection unit collects at least one of data on the position or speed of vehicles on roads and the status of traffic signals. The generation unit analyzes the data collected by the collection unit and generates an explanatory text related to the traffic conditions on the roads. The storage unit stores the explanatory text generated by the generation unit as training data.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to generate accurate and efficient descriptions of road traffic conditions, and creating training data requires time and effort.

[0005] The system according to the embodiment aims to automatically generate explanatory text relating to road traffic conditions and efficiently create learning data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, and a storage unit. The collection unit collects at least one of data on the position or speed of vehicles on a road and the status of traffic signals. The generation unit analyzes the data collected by the collection unit and generates an explanatory text related to the traffic conditions on the road. The storage unit stores the explanatory text generated by the generation unit as learning data. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate explanatory text relating to road traffic conditions and efficiently create learning data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A traffic condition explanation generation system according to an embodiment of the present invention is a system that automatically generates explanatory text about road traffic conditions. This traffic condition explanation generation system uses a generation AI to collect and analyze data, such as the position and speed of vehicles on the road and the status of traffic signals, to generate explanatory text. For example, the traffic condition explanation generation system uses cameras and sensors to collect data about the position and speed of vehicles on the road and the status of traffic signals. This data is input into the generation AI. The generation AI then analyzes the collected data and automatically generates explanatory text about road traffic conditions. Based on the collected data, the generation AI generates text that provides detailed explanations of vehicle movements on the road, traffic signal changes, and the like. For example, the AI ​​generates an explanation such as, "Currently, the traffic light at intersection A is red, and a vehicle is stopped. When the light turns green, the vehicle will pass through the intersection." The generated explanatory text is saved as training data. This training data is used in the development of autonomous driving and ADAS. For example, it is used as training data for autonomous vehicle systems to accurately recognize road traffic conditions and perform appropriate driving operations. This system makes it possible to create accurate and efficient training data, allowing the development of autonomous driving and ADAS to proceed smoothly. For example, the creation of traffic situation descriptions, which was previously done manually, can now be automated, significantly improving development efficiency. In addition, the generation AI always generates descriptions that reflect the latest traffic conditions, improving the accuracy of the training data. As a result, the traffic situation explanation generation system makes it possible to create accurate and efficient training data, allowing the development of autonomous driving and ADAS to proceed smoothly.

[0029] A traffic condition explanation generation system according to an embodiment includes a collection unit, a generation unit, and a storage unit. The collection unit collects at least one data item of the position or speed of vehicles on a road and the status of traffic signals. The collection unit collects data such as the position or speed of vehicles on a road and the status of traffic signals using, for example, a camera or a sensor. For example, the camera may be a fixed camera or a dashcam. For example, the sensor may be an ultrasonic sensor or a LiDAR. The generation unit analyzes the data collected by the collection unit and generates an explanation about the traffic conditions on the road. For example, the generation unit uses a generation AI to generate an explanation that details the movement of vehicles on the road and changes in traffic signals based on the collected data. The generation AI generates the explanation using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates an explanation such as, "Currently, the traffic light at intersection A is red, and vehicles are stopped. When the traffic light turns green, vehicles will pass through the intersection." The storage unit stores the explanation generated by the generation unit as training data. The storage unit stores the generated description in, for example, cloud storage. AWS (registered trademark) or Google (registered trademark) Cloud is used as the cloud storage. The storage unit can also encrypt and store the generated description. For example, the description is encrypted and stored using encryption technology such as AES encryption or RSA encryption. This allows the traffic condition description generation system according to the embodiment to automatically generate descriptions related to road traffic conditions and create accurate and efficient learning data.

[0030] The collection unit can collect at least one of data on the position or speed of vehicles on a road and the status of traffic signals using a camera or a sensor. The collection unit collects data such as the position and speed of vehicles on a road and the status of traffic signals using, for example, a camera or a sensor. For example, the camera can be a fixed camera or a drive recorder. For example, the sensor can be an ultrasonic sensor or a LiDAR. The camera can measure the position and speed of vehicles on a road with high accuracy. For example, the camera can acquire the vehicle's position as GPS data and measure the speed using radar. The sensor can accurately grasp the status of traffic signals. For example, the sensor can detect the color and flashing status of traffic signals and collect them as data. In this way, detailed data on roads can be collected using a camera or a sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired from a camera or a sensor into a generation AI and have the generation AI analyze the data.

[0031] The generation unit can generate sentences that provide detailed descriptions of vehicle movements on roads, changes in traffic signals, etc., based on the collected data. For example, the generation unit generates sentences that provide detailed descriptions of vehicle movements on roads, changes in traffic signals, etc., based on the collected data. The generation unit uses a generation AI to analyze the collected data and generate explanatory sentences. The generation AI generates explanatory sentences using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates an explanatory sentence such as, "Currently, the traffic light at intersection A is red, and vehicles are stopped. When the traffic light turns green, vehicles will pass through the intersection." The generation unit can also use the generation AI to adjust the length of the sentence, the language used, etc. For example, the generation unit inputs a prompt to the generation AI, such as, "Please summarize the main points of this sentence," and extracts the main points to create a summary. This generates detailed explanatory sentences based on the collected data, thereby deepening understanding of traffic conditions. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the collected data into a generation AI and cause the generation AI to generate an explanatory text.

[0032] The storage unit can store the generated description as learning data. For example, the storage unit stores the generated description as learning data. The storage unit can store the generated description in cloud storage. AWS, Google Cloud, or the like is used as the cloud storage. The storage unit can also encrypt and store the generated description. For example, the description is encrypted and stored using encryption technology such as AES encryption or RSA encryption. The storage unit can also periodically back up the generated description. For example, the storage unit regularly backs up the generated description every day and stores it in cloud storage. In this way, the generated description can be stored as learning data and used for the development of autonomous driving and ADAS. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the generated description to a generation AI and cause the generation AI to store the data.

[0033] The collection unit can collect at least one of data on weather or road conditions in addition to the position or speed of vehicles on the road and the status of traffic signals. For example, the collection unit collects data such as weather and road conditions in addition to the position or speed of vehicles on the road and the status of traffic signals. The collection unit uses cameras and sensors to collect weather information in addition to the position and speed of vehicles on the road and the status of traffic signals. For example, the camera can measure the position and speed of vehicles on the road with high accuracy. The sensor can accurately grasp the status of traffic signals. The collection unit can acquire weather data through an API to collect weather information. For example, the collection unit acquires weather information such as temperature and precipitation and collects it as data. The collection unit can also detect road conditions with sensors and collect data such as the degree of icing or wetness of the road surface. For example, the collection unit measures the road surface temperature and icing state with sensors and collects it as data. By collecting data such as weather and road conditions, more detailed traffic conditions can be understood. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data on weather and road conditions into the generation AI and have the generation AI analyze the data.

[0034] The collection unit can integrate and analyze data from multiple sensors to improve the accuracy of the collected data. For example, the collection unit integrates and analyzes data from multiple sensors to improve the accuracy of the collected data. The collection unit integrates data from a camera and a radar sensor to analyze the vehicle's position and speed with high accuracy. For example, a camera acquires the vehicle's position as image data, and a radar sensor measures the vehicle's speed. The collection unit can integrate these data and analyze the vehicle's position and speed with high accuracy. The collection unit can also integrate video data from multiple cameras to accurately determine the status of traffic signals. For example, the collection unit analyzes video data from multiple cameras to accurately determine the color and flashing status of traffic signals. Furthermore, the collection unit can combine GPS data and data from sensors to analyze road conditions in detail. For example, the collection unit identifies the vehicle's position using GPS data and analyzes road conditions using data from sensors. In this way, the accuracy of the data is improved by integrating data from multiple sensors. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from multiple sensors into the generation AI and have the generation AI integrate and analyze the data.

[0035] The collection unit can collect data from the sky using a drone. For example, the collection unit collects data from the sky using a drone. The collection unit uses the drone to photograph road traffic conditions from the sky in real time and collect data. For example, the collection unit uses a camera mounted on the drone to photograph road traffic conditions from the sky and collect data. The collection unit can also collect vehicle positions, speeds, and traffic signal statuses from the sky using sensors mounted on the drone. For example, the collection unit uses sensors mounted on the drone to acquire vehicle positions as GPS data and measure speeds with radar. Furthermore, the collection unit can also use a drone to collect road conditions over a wide area at once and efficiently acquire data. For example, the collection unit uses a drone to photograph road conditions over a wide area and collect data. This allows for efficient collection of data over a wide area by using a drone. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from a drone into the generation AI and have the generation AI analyze the data.

[0036] The collection unit can acquire data from other traffic management systems in real time. For example, the collection unit acquires data from other traffic management systems in real time. The collection unit acquires real-time traffic congestion information from other traffic management systems and collects data. For example, the collection unit acquires real-time traffic congestion information from other traffic management systems via an API and collects data. The collection unit can also acquire the operation status of public transportation from other traffic management systems and collect data. For example, the collection unit acquires the operation status of public transportation from other traffic management systems via an API and collects data. Furthermore, the collection unit can also acquire road construction information from other traffic management systems in real time and collect data. For example, the collection unit acquires road construction information from other traffic management systems via an API and collects data. In this way, by acquiring data from other traffic management systems in real time, the latest traffic conditions can be grasped. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from other traffic management systems to a generation AI and have the generation AI analyze the data.

[0037] The generation unit can include predictive information regarding road traffic conditions in the generated explanatory text. For example, the generation unit includes predictive information regarding road traffic conditions in the generated explanatory text. The generation unit uses a generation AI to generate an explanatory text including a future congestion prediction based on current traffic conditions. For example, the generation unit uses a generation AI to generate an explanatory text including a future congestion prediction based on current traffic conditions. The generation unit can also generate an explanatory text including a traffic signal change prediction. For example, the generation unit uses a generation AI to generate an explanatory text including a traffic signal change prediction. Furthermore, the generation unit can also generate an explanatory text including a traffic condition prediction due to weather changes. For example, the generation unit uses a generation AI to generate an explanatory text including a traffic condition prediction due to weather changes. In this way, by including predictive information, future traffic conditions can be predicted. Some or all of the above-described processing in the generation unit may be performed using an AI, for example, or may be performed without using an AI. For example, the generation unit can input current traffic condition data to the generation AI and cause the generation AI to generate predictive information.

[0038] The generation unit can include comparison information with past traffic data in the generated description. For example, the generation unit includes the comparison information with past traffic data in the generated description. The generation unit uses a generation AI to generate a description comparing the current traffic conditions with past traffic conditions at the same time. For example, the generation unit uses a generation AI to generate a description comparing the current traffic conditions with past traffic conditions at the same time. The generation unit can also generate a description comparing past traffic congestion data with the current situation. For example, the generation unit uses a generation AI to generate a description comparing past traffic congestion data with the current situation. Furthermore, the generation unit can also generate a description comparing past weather data with the current situation. For example, the generation unit uses a generation AI to generate a description comparing past weather data with the current situation. In this way, the inclusion of the comparison information with past traffic data allows a more detailed understanding of the current traffic conditions. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI. For example, the generation unit can input past traffic data to the generation AI and cause the generation AI to generate comparison information.

[0039] The generation unit can have a function to generate explanatory text in different languages. For example, the generation unit has a function to generate explanatory text in different languages. The generation unit uses a generation AI to translate explanatory text generated in English into Japanese and provide the translated text. For example, the generation unit uses a generation AI to translate explanatory text generated in English into Japanese and provide the translated text. The generation unit can also translate explanatory text generated in Spanish into English and provide the translated text. For example, the generation unit uses a generation AI to translate explanatory text generated in Spanish into English and provide the translated text. Furthermore, the generation unit can also translate explanatory text generated in French into German and provide the translated text. For example, the generation unit uses a generation AI to translate explanatory text generated in French into German and provide the translated text. This enables multilingual support by generating explanatory text in different languages. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the generated explanatory text into the generation AI and cause the generation AI to translate it into a different language.

[0040] The generation unit can generate an explanatory text by voice and save it as audio data. For example, the generation unit generates an explanatory text by voice and saves it as audio data. The generation unit uses a generation AI to generate the generated explanatory text as audio data using voice synthesis technology. For example, the generation unit uses a generation AI to generate the generated explanatory text as audio data using voice synthesis technology. The generation unit can also save the generated audio data so that it can be played back later. For example, the generation unit saves the generated audio data so that it can be played back later. Furthermore, the generation unit can also use the generated audio data in cooperation with another system. For example, the generation unit uses the generated audio data in cooperation with another system. This makes it possible to provide visual information by generating an explanatory text by voice. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the generated explanatory text to a generation AI and cause the generation AI to generate audio data.

[0041] The storage unit can store the generated description in cloud storage. For example, the storage unit stores the generated description in the cloud storage. The storage unit automatically uploads the generated description to the cloud storage. For example, the storage unit automatically uploads the generated description to the cloud storage. The storage unit can also make the description stored in the cloud storage accessible from other devices. For example, the storage unit makes the description stored in the cloud storage accessible from other devices. Furthermore, the storage unit can also periodically back up the description stored in the cloud storage. For example, the storage unit periodically backs up the description stored in the cloud storage. As a result, by storing the description in the cloud storage, it becomes accessible from other devices. Some or all of the above-mentioned processing in the storage unit may be performed using AI, or may be performed without using AI, for example. For example, the storage unit can input the generated description to a generation AI and cause the generation AI to store the description in the cloud storage.

[0042] The storage unit can encrypt and store the generated description. For example, the storage unit encrypts and stores the generated description. The storage unit encrypts the generated description using AES encryption technology. For example, the storage unit encrypts the generated description using AES encryption technology. The storage unit can also store the encrypted description in secure storage. For example, the storage unit stores the encrypted description in secure storage. Furthermore, the storage unit can decrypt the encrypted description to make it accessible. For example, the storage unit decrypts the encrypted description to make it accessible. Thus, by encrypting and storing the description, data security is improved. Some or all of the above-mentioned processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input the generated description to a generation AI and cause the generation AI to perform encryption and storage.

[0043] The storage unit may have a function of periodically backing up the generated description. The storage unit has a function of periodically backing up the generated description, for example. The storage unit periodically backs up the generated description every day. For example, the storage unit periodically backs up the generated description every day. The storage unit can also back up the generated description on a weekly basis and store it for a long period of time. For example, the storage unit can back up the generated description on a weekly basis and store it for a long period of time. Furthermore, the storage unit can also back up the generated description on a monthly basis and store it in cloud storage. For example, the storage unit can back up the generated description on a monthly basis and store it in cloud storage. This makes it possible to prevent data loss by performing regular backups. Some or all of the above-described processing in the storage unit may be performed using AI, or may be performed without using AI. For example, the storage unit can input the generated description to a generation AI and have the generation AI perform a backup.

[0044] The storage unit may have a function of sharing the generated explanatory text with other learning systems. For example, the storage unit has a function of sharing the generated explanatory text with other learning systems. The storage unit shares the generated explanatory text with other autonomous driving systems and uses it as learning data. For example, the storage unit shares the generated explanatory text with other autonomous driving systems and uses it as learning data. The storage unit may also share the generated explanatory text with an ADAS system and use it to improve driving assistance functions. For example, the storage unit shares the generated explanatory text with an ADAS system and uses it to improve driving assistance functions. Furthermore, the storage unit may also share the generated explanatory text with a research institute and use it for research on autonomous driving technology. For example, the storage unit shares the generated explanatory text with a research institute and uses it for research on autonomous driving technology. This enables effective use of the learning data by sharing it with other learning systems. Some or all of the above-described processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the generated explanatory text into the generation AI and cause the generation AI to share it with other learning systems.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The collection unit can also collect surrounding audio data in addition to the position and speed of vehicles on the road and the status of traffic signals. For example, the collection unit uses a microphone to collect engine sounds, horn sounds, and pedestrian voices from vehicles on the road. This enables a detailed understanding of traffic conditions. The collection unit can also analyze the audio data and detect abnormal sounds. For example, the collection unit detects abnormal engine sounds or tire slipping sounds and reports the abnormality. Furthermore, the collection unit can use the audio data to detect the occurrence of a traffic accident early. For example, the collection unit detects the sound of a collision and immediately issues an alarm. In this way, collecting audio data enables a more detailed understanding of traffic conditions and early detection of abnormalities.

[0047] The generation unit can include visual information in addition to a description of the traffic situation based on the collected data. For example, the generation unit generates animations showing the movement of vehicles on the road and changes in traffic signals based on the collected data. This makes it easier to visually understand the traffic situation. The generation unit can also generate a 3D model of the traffic situation based on the collected data. For example, the generation unit generates a 3D model based on the position and speed of vehicles on the road and visually displays it. Furthermore, the generation unit can simulate the traffic situation based on the collected data. For example, the generation unit simulates changes in traffic signals and vehicle movement to predict future traffic situations. In this way, the inclusion of visual information deepens understanding of the traffic situation and enables future predictions.

[0048] The storage unit can store the collected raw data in addition to the generated description. For example, the storage unit stores image data and sensor data collected from a camera or sensor as is. This allows for later detailed analysis. The storage unit can also compress the collected raw data before storing it. For example, the storage unit compresses image data in JPEG format to save storage space. Furthermore, the storage unit can regularly back up the collected raw data. For example, the storage unit backs up the collected raw data on a daily basis to prevent data loss. This allows for later detailed analysis and data preservation by storing the collected raw data.

[0049] The collection unit can also collect environmental data such as ambient temperature and humidity, in addition to the position and speed of vehicles on the road and the status of traffic signals. For example, the collection unit uses a temperature sensor and a humidity sensor to measure the temperature and humidity on the road and collect the data. This makes it possible to understand environmental factors that affect traffic conditions. The collection unit can also analyze the environmental data and detect abnormal weather conditions. For example, the collection unit can detect sudden temperature changes or high humidity and report the abnormality. Furthermore, the collection unit can use the environmental data to predict icy or slippery roads. For example, the collection unit can detect a combination of low temperatures and high humidity and predict icy roads. In this way, by collecting environmental data, factors that affect traffic conditions can be understood in detail and abnormalities can be detected early.

[0050] The generation unit can generate audio descriptions in addition to text descriptions of traffic conditions based on the collected data. For example, the generation unit provides audio descriptions of vehicle movements on the road and changes in traffic signals based on the collected data. This makes it possible to provide audio information in addition to visual information. The generation unit can also generate audio guidance for traffic conditions based on the collected data. For example, the generation unit generates audio guidance based on the position and speed of vehicles on the road and provides it to the driver. Furthermore, the generation unit can also generate audio alerts for traffic conditions based on the collected data. For example, the generation unit provides audio alerts for changes in traffic signals and vehicle abnormalities to alert the driver. This makes it possible to provide audio information in addition to visual information by generating audio descriptions, thereby attracting the driver's attention.

[0051] The generation unit can provide real-time traffic forecast information in addition to the traffic situation description based on the collected data. For example, the generation unit can predict future traffic congestion based on the collected data and include the prediction in the description. This allows the driver to predict future traffic conditions and take appropriate action. The generation unit can also predict changes in traffic signals based on the collected data. For example, the generation unit can predict changes in traffic signals and notify the driver. Furthermore, the generation unit can predict traffic conditions due to weather changes based on the collected data. For example, the generation unit can predict weather changes and alert the driver. This allows the driver to predict future traffic conditions and take appropriate action by providing real-time traffic forecast information.

[0052] The storage unit can store the collected raw data in cloud storage in addition to the generated description. For example, the storage unit stores image data and sensor data collected from a camera or sensor in cloud storage. This enables later detailed analysis. The storage unit can also encrypt the collected raw data and store it in cloud storage. For example, the storage unit encrypts image data using AES encryption technology and stores it in cloud storage. Furthermore, the storage unit can also periodically back up the collected raw data. For example, the storage unit backs up the collected raw data regularly every day and stores it in cloud storage. This allows later detailed analysis and data preservation by storing the collected raw data in cloud storage.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection unit collects at least one of data on the position or speed of vehicles on a road and the status of traffic signals. The collection unit uses, for example, a camera or a sensor to collect data such as the position or speed of vehicles on a road and the status of traffic signals. The camera may be a fixed camera or a drive recorder, and the sensor may be an ultrasonic sensor or LiDAR. Step 2: The generation unit analyzes the data collected by the collection unit and generates explanatory text about road traffic conditions. For example, the generation unit uses a generation AI to generate text that provides detailed explanations of vehicle movements on the road and changes in traffic signals based on the collected data. The generation AI generates explanatory text using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, it generates an explanatory text such as, "Currently, the traffic light at intersection A is red and vehicles are stopped. When the light turns green, vehicles will pass through the intersection." Step 3: The storage unit stores the description generated by the generation unit as learning data. For example, the storage unit stores the generated description in cloud storage. Cloud storage such as AWS or Google Cloud is used. The storage unit can also encrypt and store the generated description. For example, the description is encrypted and stored using encryption technology such as AES encryption or RSA encryption.

[0055] (Example 2) A traffic condition explanation generation system according to an embodiment of the present invention is a system that automatically generates explanatory text about road traffic conditions. This traffic condition explanation generation system uses a generation AI to collect and analyze data, such as the position and speed of vehicles on the road and the status of traffic signals, to generate explanatory text. For example, the traffic condition explanation generation system uses cameras and sensors to collect data about the position and speed of vehicles on the road and the status of traffic signals. This data is input into the generation AI. The generation AI then analyzes the collected data and automatically generates explanatory text about road traffic conditions. Based on the collected data, the generation AI generates text that provides detailed explanations of vehicle movements on the road, traffic signal changes, and the like. For example, the AI ​​generates an explanation such as, "Currently, the traffic light at intersection A is red, and a vehicle is stopped. When the light turns green, the vehicle will pass through the intersection." The generated explanatory text is saved as training data. This training data is used in the development of autonomous driving and ADAS. For example, it is used as training data for autonomous vehicle systems to accurately recognize road traffic conditions and perform appropriate driving operations. This system makes it possible to create accurate and efficient training data, allowing the development of autonomous driving and ADAS to proceed smoothly. For example, the creation of traffic situation descriptions, which was previously done manually, can now be automated, significantly improving development efficiency. In addition, the generation AI always generates descriptions that reflect the latest traffic conditions, improving the accuracy of the training data. As a result, the traffic situation explanation generation system makes it possible to create accurate and efficient training data, allowing the development of autonomous driving and ADAS to proceed smoothly.

[0056] A traffic condition explanation generation system according to an embodiment includes a collection unit, a generation unit, and a storage unit. The collection unit collects at least one data item of the position or speed of vehicles on a road and the status of traffic signals. The collection unit collects data such as the position or speed of vehicles on a road and the status of traffic signals using, for example, a camera or a sensor. For example, the camera may be a fixed camera or a dashcam. For example, the sensor may be an ultrasonic sensor or a LiDAR. The generation unit analyzes the data collected by the collection unit and generates an explanation about the traffic conditions on the road. For example, the generation unit uses a generation AI to generate an explanation that details the movement of vehicles on the road and changes in traffic signals based on the collected data. The generation AI generates the explanation using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates an explanation such as, "Currently, the traffic light at intersection A is red, and vehicles are stopped. When the traffic light turns green, vehicles will pass through the intersection." The storage unit stores the explanation generated by the generation unit as training data. The storage unit stores the generated description in, for example, cloud storage. AWS, Google Cloud, or the like is used as the cloud storage. The storage unit can also encrypt and store the generated description. For example, the description is encrypted and stored using encryption technology such as AES encryption or RSA encryption. This enables the traffic condition description generation system according to the embodiment to automatically generate descriptions related to road traffic conditions and create accurate and efficient learning data.

[0057] The collection unit can collect at least one of data on the position or speed of vehicles on a road and the status of traffic signals using a camera or a sensor. The collection unit collects data such as the position and speed of vehicles on a road and the status of traffic signals using, for example, a camera or a sensor. For example, the camera can be a fixed camera or a drive recorder. For example, the sensor can be an ultrasonic sensor or a LiDAR. The camera can measure the position and speed of vehicles on a road with high accuracy. For example, the camera can acquire the vehicle's position as GPS data and measure the speed using radar. The sensor can accurately grasp the status of traffic signals. For example, the sensor can detect the color and flashing status of traffic signals and collect them as data. In this way, detailed data on roads can be collected using a camera or a sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired from a camera or a sensor into a generation AI and have the generation AI analyze the data.

[0058] The generation unit can generate sentences that provide detailed descriptions of vehicle movements on roads, changes in traffic signals, etc., based on the collected data. For example, the generation unit generates sentences that provide detailed descriptions of vehicle movements on roads, changes in traffic signals, etc., based on the collected data. The generation unit uses a generation AI to analyze the collected data and generate explanatory sentences. The generation AI generates explanatory sentences using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates an explanatory sentence such as, "Currently, the traffic light at intersection A is red, and vehicles are stopped. When the traffic light turns green, vehicles will pass through the intersection." The generation unit can also use the generation AI to adjust the length of the sentence, the language used, etc. For example, the generation unit inputs a prompt to the generation AI, such as, "Please summarize the main points of this sentence," and extracts the main points to create a summary. This generates detailed explanatory sentences based on the collected data, thereby deepening understanding of traffic conditions. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the collected data into a generation AI and cause the generation AI to generate an explanatory text.

[0059] The storage unit can store the generated description as learning data. For example, the storage unit stores the generated description as learning data. The storage unit can store the generated description in cloud storage. AWS, Google Cloud, or the like is used as the cloud storage. The storage unit can also encrypt and store the generated description. For example, the description is encrypted and stored using encryption technology such as AES encryption or RSA encryption. The storage unit can also periodically back up the generated description. For example, the storage unit regularly backs up the generated description every day and stores it in cloud storage. In this way, the generated description can be stored as learning data and used for the development of autonomous driving and ADAS. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the generated description to a generation AI and cause the generation AI to store the data.

[0060] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. The collection unit estimates the user's emotions using an emotion estimation algorithm. For example, the collection unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The collection unit can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the load on the system. Also, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the collection unit can prioritize collecting only important data and process it quickly. This allows the timing of data collection to be adjusted according to the user's emotions, thereby reducing the load on the system. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and cause the generation AI to adjust the timing of data collection.

[0061] The collection unit can collect at least one of data on weather or road conditions in addition to the position or speed of vehicles on the road and the status of traffic signals. For example, the collection unit collects data such as weather and road conditions in addition to the position or speed of vehicles on the road and the status of traffic signals. The collection unit uses cameras and sensors to collect weather information in addition to the position and speed of vehicles on the road and the status of traffic signals. For example, the camera can measure the position and speed of vehicles on the road with high accuracy. The sensor can accurately grasp the status of traffic signals. The collection unit can acquire weather data through an API to collect weather information. For example, the collection unit acquires weather information such as temperature and precipitation and collects it as data. The collection unit can also detect road conditions with sensors and collect data such as the degree of icing or wetness of the road surface. For example, the collection unit measures the road surface temperature and icing state with sensors and collects it as data. By collecting data such as weather and road conditions, more detailed traffic conditions can be understood. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data on weather and road conditions into the generation AI and have the generation AI analyze the data.

[0062] The collection unit can integrate and analyze data from multiple sensors to improve the accuracy of the collected data. For example, the collection unit integrates and analyzes data from multiple sensors to improve the accuracy of the collected data. The collection unit integrates data from a camera and a radar sensor to analyze the vehicle's position and speed with high accuracy. For example, a camera acquires the vehicle's position as image data, and a radar sensor measures the vehicle's speed. The collection unit can integrate these data and analyze the vehicle's position and speed with high accuracy. The collection unit can also integrate video data from multiple cameras to accurately determine the status of traffic signals. For example, the collection unit analyzes video data from multiple cameras to accurately determine the color and flashing status of traffic signals. Furthermore, the collection unit can combine GPS data and data from sensors to analyze road conditions in detail. For example, the collection unit identifies the vehicle's position using GPS data and analyzes road conditions using data from sensors. In this way, the accuracy of the data is improved by integrating data from multiple sensors. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from multiple sensors into the generation AI and have the generation AI integrate and analyze the data.

[0063] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, the collection unit estimates the user's emotions and prioritizes the data to be collected based on the estimated user emotions. The collection unit estimates the user's emotions using an emotion estimation algorithm. For example, the collection unit can analyze the user's facial expressions and estimate emotions using facial expression recognition technology. The collection unit can also estimate emotions by analyzing the tone and speed of the user's voice using voice analysis technology. For example, if the user is feeling stressed, the collection unit can prioritize collecting only important data to reduce the load on the system. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed data to improve accuracy. For example, if the user is in a hurry, the collection unit prioritizes collecting data that requires quick processing. Thus, by prioritizing data according to the user's emotions, important data can be prioritized and collected. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and have the generation AI determine the priority of the data.

[0064] The collection unit can collect data from the sky using a drone. For example, the collection unit collects data from the sky using a drone. The collection unit uses the drone to photograph road traffic conditions from the sky in real time and collect data. For example, the collection unit uses a camera mounted on the drone to photograph road traffic conditions from the sky and collect data. The collection unit can also collect vehicle positions, speeds, and traffic signal statuses from the sky using sensors mounted on the drone. For example, the collection unit uses sensors mounted on the drone to acquire vehicle positions as GPS data and measure speeds with radar. Furthermore, the collection unit can also use a drone to collect road conditions over a wide area at once and efficiently acquire data. For example, the collection unit uses a drone to photograph road conditions over a wide area and collect data. This allows for efficient collection of data over a wide area by using a drone. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from a drone into the generation AI and have the generation AI analyze the data.

[0065] The collection unit can acquire data from other traffic management systems in real time. For example, the collection unit acquires data from other traffic management systems in real time. The collection unit acquires real-time traffic congestion information from other traffic management systems and collects data. For example, the collection unit acquires real-time traffic congestion information from other traffic management systems via an API and collects data. The collection unit can also acquire the operation status of public transportation from other traffic management systems and collect data. For example, the collection unit acquires the operation status of public transportation from other traffic management systems via an API and collects data. Furthermore, the collection unit can also acquire road construction information from other traffic management systems in real time and collect data. For example, the collection unit acquires road construction information from other traffic management systems via an API and collects data. In this way, by acquiring data from other traffic management systems in real time, the latest traffic conditions can be grasped. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from other traffic management systems to a generation AI and have the generation AI analyze the data.

[0066] The generation unit can estimate the user's emotion and adjust the expression method of the description based on the estimated user's emotion. For example, the generation unit estimates the user's emotion and adjusts the expression method of the description based on the estimated user's emotion. The generation unit estimates the user's emotion using an emotion estimation algorithm. For example, the generation unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The generation unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. For example, the generation unit can generate a detailed and thorough description if the user is relaxed. Also, the generation unit can generate a concise and to-the-point description if the user is in a hurry. For example, the generation unit can generate a description that includes visually stimulating expressions if the user is excited. This allows the generation of a more appropriate description by adjusting the expression method of the description according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the expression method of the explanatory text.

[0067] The generation unit can include predictive information regarding road traffic conditions in the generated explanatory text. For example, the generation unit includes predictive information regarding road traffic conditions in the generated explanatory text. The generation unit uses a generation AI to generate an explanatory text including a future congestion prediction based on current traffic conditions. For example, the generation unit uses a generation AI to generate an explanatory text including a future congestion prediction based on current traffic conditions. The generation unit can also generate an explanatory text including a traffic signal change prediction. For example, the generation unit uses a generation AI to generate an explanatory text including a traffic signal change prediction. Furthermore, the generation unit can also generate an explanatory text including a traffic condition prediction due to weather changes. For example, the generation unit uses a generation AI to generate an explanatory text including a traffic condition prediction due to weather changes. In this way, by including predictive information, future traffic conditions can be predicted. Some or all of the above-described processing in the generation unit may be performed using an AI, for example, or may be performed without using an AI. For example, the generation unit can input current traffic condition data to the generation AI and cause the generation AI to generate predictive information.

[0068] The generation unit can include comparison information with past traffic data in the generated description. For example, the generation unit includes the comparison information with past traffic data in the generated description. The generation unit uses a generation AI to generate a description comparing the current traffic conditions with past traffic conditions at the same time. For example, the generation unit uses a generation AI to generate a description comparing the current traffic conditions with past traffic conditions at the same time. The generation unit can also generate a description comparing past traffic congestion data with the current situation. For example, the generation unit uses a generation AI to generate a description comparing past traffic congestion data with the current situation. Furthermore, the generation unit can also generate a description comparing past weather data with the current situation. For example, the generation unit uses a generation AI to generate a description comparing past weather data with the current situation. In this way, the inclusion of the comparison information with past traffic data allows a more detailed understanding of the current traffic conditions. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI. For example, the generation unit can input past traffic data to the generation AI and cause the generation AI to generate comparison information.

[0069] The generation unit can estimate the user's emotion and adjust the length of the description based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the length of the description based on the estimated user's emotion. The generation unit estimates the user's emotion using an emotion estimation algorithm. For example, the generation unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The generation unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. For example, the generation unit can generate a short and to-the-point description if the user is in a hurry. The generation unit can also generate a longer description including detailed explanations if the user is relaxed. For example, the generation unit can generate a description including visually stimulating expressions if the user is excited. This allows the generation of more appropriate descriptions by adjusting the length of the description according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the length of the explanatory text.

[0070] The generation unit can have a function to generate explanatory text in different languages. For example, the generation unit has a function to generate explanatory text in different languages. The generation unit uses a generation AI to translate explanatory text generated in English into Japanese and provide the translated text. For example, the generation unit uses a generation AI to translate explanatory text generated in English into Japanese and provide the translated text. The generation unit can also translate explanatory text generated in Spanish into English and provide the translated text. For example, the generation unit uses a generation AI to translate explanatory text generated in Spanish into English and provide the translated text. Furthermore, the generation unit can also translate explanatory text generated in French into German and provide the translated text. For example, the generation unit uses a generation AI to translate explanatory text generated in French into German and provide the translated text. This enables multilingual support by generating explanatory text in different languages. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the generated explanatory text into the generation AI and cause the generation AI to translate it into a different language.

[0071] The generation unit can generate an explanatory text by voice and save it as audio data. For example, the generation unit generates an explanatory text by voice and saves it as audio data. The generation unit uses a generation AI to generate the generated explanatory text as audio data using voice synthesis technology. For example, the generation unit uses a generation AI to generate the generated explanatory text as audio data using voice synthesis technology. The generation unit can also save the generated audio data so that it can be played back later. For example, the generation unit saves the generated audio data so that it can be played back later. Furthermore, the generation unit can also use the generated audio data in cooperation with another system. For example, the generation unit uses the generated audio data in cooperation with another system. This makes it possible to provide visual information by generating an explanatory text by voice. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the generated explanatory text to a generation AI and cause the generation AI to generate audio data.

[0072] The storage unit can estimate the user's emotions and determine the priority of data to be saved based on the estimated user emotions. For example, the storage unit can estimate the user's emotions and determine the priority of data to be saved based on the estimated user emotions. The storage unit estimates the user's emotions using an emotion estimation algorithm. For example, the storage unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The storage unit can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. For example, if the user is stressed, the storage unit can prioritize saving only important data. Also, if the user is relaxed, the storage unit can prioritize saving detailed data. For example, if the user is in a hurry, the storage unit prioritizes saving data that requires quick processing. In this way, by determining the priority of data according to the user's emotions, important data can be prioritized and saved. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may input user emotion data to the generation AI and have the generation AI determine the priority of the data.

[0073] The storage unit can store the generated description in cloud storage. For example, the storage unit stores the generated description in the cloud storage. The storage unit automatically uploads the generated description to the cloud storage. For example, the storage unit automatically uploads the generated description to the cloud storage. The storage unit can also make the description stored in the cloud storage accessible from other devices. For example, the storage unit makes the description stored in the cloud storage accessible from other devices. Furthermore, the storage unit can also periodically back up the description stored in the cloud storage. For example, the storage unit periodically backs up the description stored in the cloud storage. As a result, by storing the description in the cloud storage, it becomes accessible from other devices. Some or all of the above-mentioned processing in the storage unit may be performed using AI, or may be performed without using AI, for example. For example, the storage unit can input the generated description to a generation AI and cause the generation AI to store the description in the cloud storage.

[0074] The storage unit can encrypt and store the generated description. For example, the storage unit encrypts and stores the generated description. The storage unit encrypts the generated description using AES encryption technology. For example, the storage unit encrypts the generated description using AES encryption technology. The storage unit can also store the encrypted description in secure storage. For example, the storage unit stores the encrypted description in secure storage. Furthermore, the storage unit can decrypt the encrypted description to make it accessible. For example, the storage unit decrypts the encrypted description to make it accessible. Thus, by encrypting and storing the description, data security is improved. Some or all of the above-mentioned processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input the generated description to a generation AI and cause the generation AI to perform encryption and storage.

[0075] The storage unit can estimate a user's emotion and adjust access permissions for the stored data based on the estimated user emotion. For example, the storage unit estimates a user's emotion and adjusts access permissions for the stored data based on the estimated user emotion. The storage unit estimates the user's emotion using an emotion estimation algorithm. For example, the storage unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The storage unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. For example, if the user is stressed, the storage unit can limit access permissions and display only important data. The storage unit can also grant access permissions to detailed data if the user is relaxed. For example, if the user is in a hurry, the storage unit can adjust permissions to allow quick access. This facilitates data management by adjusting access permissions according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may input user emotion data to the generation AI and have the generation AI adjust the access authority.

[0076] The storage unit may have a function of periodically backing up the generated description. The storage unit has a function of periodically backing up the generated description, for example. The storage unit periodically backs up the generated description every day. For example, the storage unit periodically backs up the generated description every day. The storage unit can also back up the generated description on a weekly basis and store it for a long period of time. For example, the storage unit can back up the generated description on a weekly basis and store it for a long period of time. Furthermore, the storage unit can also back up the generated description on a monthly basis and store it in cloud storage. For example, the storage unit can back up the generated description on a monthly basis and store it in cloud storage. This makes it possible to prevent data loss by performing regular backups. Some or all of the above-described processing in the storage unit may be performed using AI, or may be performed without using AI. For example, the storage unit can input the generated description to a generation AI and have the generation AI perform a backup.

[0077] The storage unit may have a function of sharing the generated explanatory text with other learning systems. For example, the storage unit has a function of sharing the generated explanatory text with other learning systems. The storage unit shares the generated explanatory text with other autonomous driving systems and uses it as learning data. For example, the storage unit shares the generated explanatory text with other autonomous driving systems and uses it as learning data. The storage unit may also share the generated explanatory text with an ADAS system and use it to improve driving assistance functions. For example, the storage unit shares the generated explanatory text with an ADAS system and uses it to improve driving assistance functions. Furthermore, the storage unit may also share the generated explanatory text with a research institute and use it for research on autonomous driving technology. For example, the storage unit shares the generated explanatory text with a research institute and uses it for research on autonomous driving technology. This enables effective use of the learning data by sharing it with other learning systems. Some or all of the above-described processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the generated explanatory text into the generation AI and cause the generation AI to share it with other learning systems. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, generation unit, and storage 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 collection unit collects data such as the position and speed of vehicles on roads and the status of traffic signals using the camera 42 and sensors of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and generates an explanatory text using a generation AI. The storage unit stores the generated explanatory text in, for example, the storage 32 of the data processing device 12. The storage unit can also store the explanatory text in, for example, the storage 50 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, generation unit, and storage unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data such as the position and speed of vehicles on roads and the status of traffic signals using the camera 42 and sensors of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and generates an explanatory text using a generation AI. The storage unit stores the generated explanatory text in, for example, the storage 32 of the data processing device 12. The storage unit can also store the explanatory text in, for example, the storage 50 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, generation unit, and storage unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data such as the position and speed of vehicles on roads and the status of traffic lights using the camera 42 and sensors of the headset type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and generates explanatory text using a generation AI. The storage unit stores the generated explanatory text in, for example, the storage 32 of the data processing device 12. The storage unit can also store the explanatory text in, for example, the storage 50 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, and storage unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses the camera 42 and sensors of the robot 414 to collect data such as the position and speed of vehicles on roads and the status of traffic lights. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and generates an explanatory text using a generation AI. The storage unit stores the generated explanatory text in, for example, the storage 32 of the data processing device 12. The storage unit can also store the explanatory text in, for example, the storage 50 of the robot 414.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The collection unit can also collect surrounding audio data in addition to the position and speed of vehicles on the road and the status of traffic signals. For example, the collection unit uses a microphone to collect engine sounds, horn sounds, and pedestrian voices from vehicles on the road. This enables a detailed understanding of traffic conditions. The collection unit can also analyze the audio data and detect abnormal sounds. For example, the collection unit detects abnormal engine sounds or tire slipping sounds and reports the abnormality. Furthermore, the collection unit can use the audio data to detect the occurrence of a traffic accident early. For example, the collection unit detects the sound of a collision and immediately issues an alarm. In this way, collecting audio data enables a more detailed understanding of traffic conditions and early detection of abnormalities.

[0080] The generation unit can include visual information in addition to a description of the traffic situation based on the collected data. For example, the generation unit generates animations showing the movement of vehicles on the road and changes in traffic signals based on the collected data. This makes it easier to visually understand the traffic situation. The generation unit can also generate a 3D model of the traffic situation based on the collected data. For example, the generation unit generates a 3D model based on the position and speed of vehicles on the road and visually displays it. Furthermore, the generation unit can simulate the traffic situation based on the collected data. For example, the generation unit simulates changes in traffic signals and vehicle movement to predict future traffic situations. In this way, the inclusion of visual information deepens understanding of the traffic situation and enables future predictions.

[0081] The storage unit can store the collected raw data in addition to the generated description. For example, the storage unit stores image data and sensor data collected from a camera or sensor as is. This allows for later detailed analysis. The storage unit can also compress the collected raw data before storing it. For example, the storage unit compresses image data in JPEG format to save storage space. Furthermore, the storage unit can regularly back up the collected raw data. For example, the storage unit backs up the collected raw data on a daily basis to prevent data loss. This allows for later detailed analysis and data preservation by storing the collected raw data.

[0082] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects only important data to reduce the load on the system. Furthermore, if the user is relaxed, the collection unit can collect detailed data to improve accuracy. For example, if the user is in a hurry, the collection unit prioritizes collecting data that requires rapid processing. This adjusts the type of data according to the user's emotions, thereby reducing the load on the system and prioritizing the collection of important data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the type of data.

[0083] The generation unit can estimate the user's emotions and adjust the tone of the explanatory text based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate explanatory text with a calm and soothing tone. Also, if the user is excited, the generation unit can generate explanatory text with a lively tone. For example, if the user is stressed, the generation unit can generate explanatory text with a reassuring tone. This allows for adjusting the tone of the explanatory text according to the user's emotions, thereby generating more appropriate explanatory text. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the tone of the explanatory text.

[0084] The collection unit can also collect environmental data such as ambient temperature and humidity, in addition to the position and speed of vehicles on the road and the status of traffic signals. For example, the collection unit uses a temperature sensor and a humidity sensor to measure the temperature and humidity on the road and collect the data. This makes it possible to understand environmental factors that affect traffic conditions. The collection unit can also analyze the environmental data and detect abnormal weather conditions. For example, the collection unit can detect sudden temperature changes or high humidity and report the abnormality. Furthermore, the collection unit can use the environmental data to predict icy or slippery roads. For example, the collection unit can detect a combination of low temperatures and high humidity and predict icy roads. In this way, by collecting environmental data, factors that affect traffic conditions can be understood in detail and abnormalities can be detected early.

[0085] The generation unit can generate audio descriptions in addition to text descriptions of traffic conditions based on the collected data. For example, the generation unit provides audio descriptions of vehicle movements on the road and changes in traffic signals based on the collected data. This makes it possible to provide audio information in addition to visual information. The generation unit can also generate audio guidance for traffic conditions based on the collected data. For example, the generation unit generates audio guidance based on the position and speed of vehicles on the road and provides it to the driver. Furthermore, the generation unit can also generate audio alerts for traffic conditions based on the collected data. For example, the generation unit provides audio alerts for changes in traffic signals and vehicle abnormalities to alert the driver. This makes it possible to provide audio information in addition to visual information by generating audio descriptions, thereby attracting the driver's attention.

[0086] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the load on the system. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the collection unit can prioritize collecting only important data and process it quickly. This allows the frequency of data collection to be adjusted according to the user's emotions, reducing the load on the system and prioritizing the collection of important data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the frequency of data collection.

[0087] The generation unit can provide real-time traffic forecast information in addition to the traffic situation description based on the collected data. For example, the generation unit can predict future traffic congestion based on the collected data and include the prediction in the description. This allows the driver to predict future traffic conditions and take appropriate action. The generation unit can also predict changes in traffic signals based on the collected data. For example, the generation unit can predict changes in traffic signals and notify the driver. Furthermore, the generation unit can predict traffic conditions due to weather changes based on the collected data. For example, the generation unit can predict weather changes and alert the driver. This allows the driver to predict future traffic conditions and take appropriate action by providing real-time traffic forecast information.

[0088] The storage unit can store the collected raw data in cloud storage in addition to the generated description. For example, the storage unit stores image data and sensor data collected from a camera or sensor in cloud storage. This enables later detailed analysis. The storage unit can also encrypt the collected raw data and store it in cloud storage. For example, the storage unit encrypts image data using AES encryption technology and stores it in cloud storage. Furthermore, the storage unit can also periodically back up the collected raw data. For example, the storage unit backs up the collected raw data regularly every day and stores it in cloud storage. This allows later detailed analysis and data preservation by storing the collected raw data in cloud storage.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The collection unit collects at least one of data on the position or speed of vehicles on a road and the status of traffic signals. The collection unit uses, for example, a camera or a sensor to collect data such as the position or speed of vehicles on a road and the status of traffic signals. The camera may be a fixed camera or a drive recorder, and the sensor may be an ultrasonic sensor or LiDAR. Step 2: The generation unit analyzes the data collected by the collection unit and generates explanatory text about road traffic conditions. For example, the generation unit uses a generation AI to generate text that provides detailed explanations of vehicle movements on the road and changes in traffic signals based on the collected data. The generation AI generates explanatory text using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, it generates an explanatory text such as, "Currently, the traffic light at intersection A is red and vehicles are stopped. When the light turns green, vehicles will pass through the intersection." Step 3: The storage unit stores the description generated by the generation unit as learning data. For example, the storage unit stores the generated description in cloud storage. Cloud storage such as AWS or Google Cloud is used. The storage unit can also encrypt and store the generated description. For example, the description is encrypted and stored using encryption technology such as AES encryption or RSA encryption.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

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

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

[0138] In the 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.

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

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

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

[0142] The data processing system 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.

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

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a collection unit that collects at least one data item of the position or speed of vehicles on a road and the status of traffic signals; a generation unit that analyzes the data collected by the collection unit and generates explanatory text about the traffic conditions on the road; and a storage unit that stores the explanatory text generated by the generation unit as learning data.

2. The system according to claim 1 , wherein the collection unit collects at least one of data on the position or speed of a vehicle on a road and the status of a traffic signal using a camera or a sensor.

3. The generation unit Based on the collected data, it generates sentences that explain in detail the movement of vehicles on the road, changes in traffic signals, etc.

2. The system of claim 1.

4. The storage unit Save the generated explanations as training data 2. The system of claim 1.

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

6. 2. The system according to claim 1, wherein the collection unit collects data on at least one of weather and road conditions in addition to the position or speed of vehicles on a road and the status of traffic signals.

7. The collecting unit Integrate and analyze data from multiple sensors to improve the accuracy of collected data 2. The system of claim 1.

8. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

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