System

Abstract

An object of a system according to an embodiment is to improve the accuracy of AI recognition for irregular situations.SOLUTION: A system according to an embodiment includes an image generation unit, a learning unit, a prompt input unit, and an analysis unit. The image generation unit generates an image of an irregular event. The learning unit uses the image generated by the image generation unit as learning data. The prompt input unit inputs a situation occurring during driving as a prompt. The analysis unit analyzes the prompt input by the prompt input unit and proposes a countermeasure for the situation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacks the accuracy of AI recognition in irregular situations, leaving room for improvement.

[0005] The system according to the embodiment aims to improve the recognition accuracy of AI in irregular situations. [Means for solving the problem]

[0006] The system according to the embodiment includes an image generation unit, a learning unit, a prompt input unit, and an analysis unit. The image generation unit generates images of irregular events. The learning unit uses the images generated by the image generation unit as learning data. The prompt input unit inputs situations that occur while driving as prompts. The analysis unit analyzes the prompts input by the prompt input unit and proposes countermeasures for the situations. [Effects of the Invention]

[0007] The system according to the embodiment can improve the recognition accuracy of the AI ​​for irregular situations. [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) An autonomous driving system according to an embodiment of the present invention is a system for improving the recognition accuracy of irregular events. The system includes an image generation unit that generates images of irregular events, a learning unit that uses the generated images as learning data, a prompt input unit that inputs situations that occur during driving as prompts, and an analysis unit that analyzes the input prompts and proposes countermeasures for the situations. For example, the autonomous driving system uses a generation AI to generate images of irregular events, such as obstacles that suddenly appear on the road or unexpected weather changes. The autonomous driving system then uses the generated images as learning data, and the AI ​​learns from these irregular events. Furthermore, the autonomous driving system inputs situations that occur during driving as prompts, and the generation AI proposes appropriate countermeasures for the situations. This allows the autonomous driving system to improve the recognition accuracy of irregular events. For example, the generation AI generates images of irregular events, and the AI ​​learns from these situations, allowing the system to make appropriate decisions when encountering irregular events during actual driving. Furthermore, the system inputs situations that occur during driving as prompts, and the generation AI proposes countermeasures for the situations, enabling quick and appropriate responses.

[0029] An autonomous driving system according to an embodiment includes an image generation unit, a learning unit, a prompt input unit, and an analysis unit. The image generation unit generates images of irregular events using a generation AI. For example, the image generation unit generates images of irregular events, such as an obstacle that suddenly appears on the road or an unexpected change in weather. The image generation unit can also use the generation AI to generate scenarios that are not seen in normal driving situations. The learning unit uses the generated images as training data. For example, the learning unit trains an AI model using images generated by the generation AI. The learning unit can also preprocess the generated images and perform data augmentation. The prompt input unit inputs situations that occur during driving as prompts. For example, the prompt input unit can input situations during driving using text input or voice input. The prompt input unit can also input situations using gesture input. The analysis unit analyzes the prompt input by the prompt input unit and proposes countermeasures for the situation. For example, the analysis unit analyzes the prompt using a generation AI and proposes appropriate countermeasures. The analysis unit can also analyze the prompt using a data analysis algorithm and propose countermeasures. As a result, the autonomous driving system according to the embodiment can improve the accuracy of recognizing irregular events.

[0030] The image generation unit can generate images of irregular events such as obstacles that suddenly appear on the road or unexpected weather changes. The image generation unit, for example, uses a generation AI to generate images of obstacles that suddenly appear on the road. For example, the generation AI can generate a scenario in which an animal jumps out onto the road. The image generation unit can also use a generation AI to generate images of unexpected weather changes. For example, the generation AI can generate scenarios of sudden rain or snow. This allows the AI's learning data to be enriched by generating images of irregular events. Some or all of the above-mentioned processing in the image generation unit is performed using the generation AI. For example, the image generation unit can input a prompt to the generation AI to generate images of irregular events.

[0031] The learning unit can use the images generated by the image generation unit as training data. The learning unit, for example, trains an AI model using images generated by the generation AI. For example, the learning unit can preprocess the generated images and perform data augmentation. The learning unit can also adjust the parameters of the AI ​​model using the generated images. In this way, the recognition accuracy of the AI ​​can be improved by using the generated images as training data. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit can input the generated images into an AI model and perform training.

[0032] The prompt input unit can input situations that occur while driving as prompts. The prompt input unit can input situations while driving using, for example, text input. For example, the prompt input unit can input situations such as "traffic jam" or "road construction" in text. The prompt input unit can also input situations using voice input. For example, the prompt input unit can input "traffic jam" by voice. The prompt input unit can also input situations using gesture input. For example, the prompt input unit can input situations by performing specific gestures on the smartphone screen. This allows the situation while driving to be input in real time, making it possible for the AI ​​to quickly propose countermeasures. Some or all of the above-mentioned processing in the prompt input unit is performed using AI. For example, the prompt input unit can have AI analyze voice input to identify the situation.

[0033] The analysis unit can analyze the prompt input by the prompt input unit and propose a countermeasure for the situation. The analysis unit, for example, uses a generation AI to analyze the prompt and propose an appropriate countermeasure. For example, the analysis unit can input a prompt for "traffic congestion" to the generation AI and have it propose a detour route. The analysis unit can also analyze the prompt using a data analysis algorithm and propose a countermeasure. For example, the analysis unit can analyze a prompt for "road construction" using a data analysis algorithm and propose an instruction to adjust speed. This makes it possible to quickly respond to irregular situations while driving by analyzing the prompt and proposing an appropriate countermeasure. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input a prompt to the generation AI and have it propose a countermeasure.

[0034] The analysis unit can propose countermeasures for sudden traffic congestion or unexpected road construction situations. The analysis unit, for example, uses the generation AI to propose countermeasures for sudden traffic congestion situations. For example, the analysis unit can input a "traffic congestion" prompt to the generation AI and have it propose a detour route. The analysis unit can also use the generation AI to propose countermeasures for unexpected road construction situations. For example, the analysis unit can input a "road construction" prompt to the generation AI and have it propose a speed adjustment instruction. This makes it possible to propose appropriate countermeasures for irregular situations such as traffic congestion or road construction. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input a prompt to the generation AI and have it propose a countermeasure.

[0035] The image generation unit can include a scenario combining multiple irregular events in the generated image. The image generation unit, for example, uses a generation AI to generate a scenario combining multiple irregular events. For example, the generation AI generates a scenario combining an obstacle suddenly appearing on a road with a sudden change in weather. The generation AI can also generate a scenario in which road construction is being carried out simultaneously with a traffic jam. The generation AI can also generate a scenario in which a pedestrian suddenly runs out into the road with a traffic light malfunction. In this way, by generating a scenario combining multiple irregular events, more realistic training data can be provided. Some or all of the above-mentioned processing in the image generation unit is performed using the generation AI. For example, the image generation unit can input a prompt to the generation AI to generate a scenario combining multiple irregular events.

[0036] The image generation unit can reflect different time periods and seasonal changes in the images it generates. The image generation unit, for example, uses a generation AI to generate images that reflect different time periods and seasonal changes. For example, the generation AI generates a scenario that compares daytime traffic congestion with nighttime traffic congestion. The generation AI can also generate a scenario that compares driving conditions on sunny summer days with driving conditions on snowy winter roads. The generation AI can also generate a scenario that compares driving conditions during morning rush hour with driving conditions during quiet late-night hours. In this way, by generating images that reflect different time periods and seasonal changes, more diverse learning data can be provided. Some or all of the above-mentioned processing in the image generation unit is performed using a generation AI. For example, the image generation unit can input prompts to the generation AI to generate images that reflect different time periods and seasonal changes.

[0037] The image generation unit can incorporate different viewpoints and camera angles into the images it generates. The image generation unit generates images incorporating different viewpoints and camera angles, for example, using a generation AI. For example, the generation AI generates a scenario that combines a viewpoint from the driver's seat and a viewpoint from a drone. The generation AI can also generate a scenario that combines a viewpoint from the front and rear of the vehicle. The generation AI can also generate a scenario that combines viewpoints from the inside and outside of the vehicle. In this way, by generating images that incorporate different viewpoints and camera angles, it is possible to provide more multifaceted learning data. Some or all of the above-mentioned processing in the image generation unit is performed using a generation AI. For example, the image generation unit can input a prompt to the generation AI to generate images incorporating different viewpoints and camera angles.

[0038] The image generation unit can reflect different geographical conditions and cultural backgrounds in the images it generates. The image generation unit, for example, uses a generation AI to generate images that reflect different geographical conditions and cultural backgrounds. For example, the generation AI generates a scenario that compares driving conditions in urban and rural areas. The generation AI can also generate scenarios that reflect traffic rules and signs in different countries. The generation AI can also generate scenarios that reflect driving conditions in areas with different cultural backgrounds. This allows for the generation of images that reflect different geographical conditions and cultural backgrounds, thereby providing more diverse learning data. Some or all of the above-mentioned processing in the image generation unit is performed using a generation AI. For example, the image generation unit can input prompts to the generation AI to generate images that reflect different geographical conditions and cultural backgrounds.

[0039] The image generation unit can include different traffic rules and signs in the images it generates. The image generation unit generates images that include different traffic rules and signs, for example, using a generation AI. For example, the generation AI generates scenarios that reflect the traffic rules of different countries. The generation AI can also generate scenarios that reflect signs from different regions. The generation AI can also generate scenarios that emphasize specific traffic rules (e.g., stop signs, priority roads). In this way, by generating images that include different traffic rules and signs, it is possible to provide more diverse learning data. Some or all of the above-mentioned processing in the image generation unit is performed using the generation AI. For example, the image generation unit can input prompts to the generation AI to generate images that include different traffic rules and signs.

[0040] The image generation unit can reflect different vehicle types and traffic conditions in the images it generates. The image generation unit, for example, uses a generation AI to generate images that reflect different vehicle types and traffic conditions. For example, the generation AI generates scenarios that reflect different vehicle types (e.g., trucks, bicycles, motorcycles). The generation AI can also generate scenarios that reflect different traffic conditions (e.g., traffic jams, smooth traffic). The generation AI can also generate scenarios that reflect different vehicle behaviors (e.g., sudden braking, sudden acceleration). This allows for the generation of images that reflect different vehicle types and traffic conditions, thereby providing more diverse learning data. Some or all of the above-described processing in the image generation unit is performed using the generation AI. For example, the image generation unit can input prompts to the generation AI to generate images that reflect different vehicle types and traffic conditions.

[0041] During learning, the learning unit can emphasize particularly important parts of the generated images for learning. For example, the learning unit emphasizes particularly important parts of the images generated using the generation AI for learning. For example, the generation AI can emphasize obstacles that suddenly appear on the road for learning. The generation AI can also emphasize sudden weather changes for learning. The generation AI can also emphasize important parts of traffic congestion and road construction for learning. In this way, by emphasizing important parts for learning, learning accuracy can be improved. Some or all of the above-mentioned processes in the learning unit are performed using AI. For example, the learning unit can input a prompt to the generation AI and emphasize important parts for learning.

[0042] The learning unit can improve learning accuracy by combining different AI models during learning. The learning unit, for example, uses the generation AI to combine and learn different AI models. For example, the generation AI can learn by combining different AI models (e.g., image recognition model, natural language processing model). The generation AI can also learn by combining different data sources (e.g., real-time data, past data). The generation AI can also learn by combining different algorithms (e.g., deep learning, reinforcement learning). In this way, learning accuracy can be improved by combining and learning different AI models. Some or all of the above-mentioned processes in the learning unit are performed using AI. For example, the learning unit can learn by combining different AI models.

[0043] During learning, the learning unit can combine past driving data with generated images for learning. The learning unit, for example, uses a generation AI to combine past driving data with generated images for learning. For example, the generation AI can combine past driving data with generated images of irregular events for learning. The generation AI can also combine past driving data with real-time traffic information for learning. The generation AI can also combine past driving data with images of different weather conditions for learning. In this way, by combining past driving data with generated images for learning, learning accuracy can be improved. Some or all of the above-mentioned processes in the learning unit are performed using AI. For example, the learning unit can make the generation AI learn by combining past driving data with generated images.

[0044] During learning, the learning unit can integrate information from different data sources to expand the learning data. The learning unit, for example, uses a generation AI to integrate information from different data sources to expand the learning data. For example, the generation AI can integrate real-time traffic information and past driving data to learn. The generation AI can also integrate data under different weather conditions to learn. The generation AI can also integrate data under different geographical conditions to learn. In this way, by integrating information from different data sources, the learning data can be expanded and the learning accuracy can be improved. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit can have the generation AI integrate information from different data sources to learn.

[0045] The learning unit can use data that reflects different environmental conditions (weather, time of day, etc.) when learning. The learning unit, for example, uses a generation AI to learn using data that reflects different environmental conditions. For example, the generation AI can learn using data from sunny and rainy days. The generation AI can also learn using data from daytime and nighttime. The generation AI can also learn using data from different seasons. In this way, using data that reflects different environmental conditions can increase the diversity of the learning data and improve learning accuracy. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit can input data from different environmental conditions into the generation AI and have it learn.

[0046] The learning unit can take different driving styles and driver characteristics into consideration during learning. The learning unit, for example, uses a generation AI to take different driving styles and driver characteristics into consideration during learning. For example, the generation AI can learn using data on cautious driving styles and aggressive driving styles. The generation AI can also learn using data on drivers of different age groups. The generation AI can also learn using data on drivers with different experience levels. In this way, by taking different driving styles and driver characteristics into consideration during learning, the diversity of the learning data can be increased and learning accuracy can be improved. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit can input data on different driving styles and driver characteristics into the generation AI and have it learn.

[0047] The prompt input unit can support voice input and gesture input when inputting a prompt. The prompt input unit, for example, inputs a prompt using voice input. For example, the user simply inputs "traffic jam" by voice, and the generation AI analyzes the situation. The prompt input unit can also input a prompt using gesture input. For example, the user can easily input a prompt by performing a specific gesture on the smartphone screen. The prompt input unit can also combine voice input and gesture input to input a prompt more intuitively. In this way, supporting voice input and gesture input enables more intuitive prompt input. Some or all of the above-mentioned processing in the prompt input unit is performed using AI. For example, the prompt input unit can input voice data or gesture data into the generation AI and have it analyze the prompt.

[0048] The prompt input unit can allow multiple situations to be input simultaneously when inputting a prompt. The prompt input unit can, for example, input multiple situations simultaneously. For example, the user can input "traffic jam" and "road construction" simultaneously, and the generation AI can analyze both situations. Alternatively, the user can input "rain" and "traffic light out of order" simultaneously, and the generation AI can analyze both situations. Alternatively, the user can input "animal jumping out" and "sudden change in weather" simultaneously, and the generation AI can analyze both situations. By allowing multiple situations to be input simultaneously, more complex situations can be handled. Some or all of the above-mentioned processing in the prompt input unit is performed using AI. For example, the prompt input unit can input multiple prompts to the generation AI and have it analyze them simultaneously.

[0049] The prompt input unit can perform input completion by referring to past input history when entering a prompt. The prompt input unit performs input completion by referring to, for example, past input history. For example, prompts previously entered by the user can be automatically displayed as candidates. The prompt input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The prompt input unit can also predict and suggest prompts to be used in a specific time period based on the user's past input history. This makes it possible to improve input efficiency by performing input completion by referring to the past input history. Some or all of the above-described processing in the prompt input unit is performed using AI. For example, the prompt input unit can input the past input history into a generation AI and have it perform input completion.

[0050] When inputting a prompt, the prompt input unit can prioritize inputting a highly relevant prompt by taking into account the user's geographical location information. The prompt input unit, for example, prioritizes inputting a highly relevant prompt by taking into account the user's geographical location information. For example, if the user is in a specific area, prompts related to that area can be displayed preferentially. Also, if the user is on a specific road, prompts related to that road can be displayed preferentially. Also, if the user is in a specific facility, prompts related to that facility can be displayed preferentially. In this way, by preferentially inputting a highly relevant prompt by taking into account the user's geographical location information, more appropriate prompts can be provided. Some or all of the above-described processing in the prompt input unit is performed using AI. For example, the prompt input unit can input the user's geographical location information to a generation AI and cause it to preferentially input a highly relevant prompt.

[0051] The prompt input unit can analyze the user's social media activity and input relevant prompts when inputting a prompt. The prompt input unit, for example, analyzes the user's social media activity and inputs relevant prompts. For example, it displays prompts related to places the user has checked in to on social media. It can also analyze the content of the user's social media posts and display relevant prompts. It can also display relevant prompts based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity and inputting relevant prompts, it is possible to provide more appropriate prompts. Some or all of the above-described processing in the prompt input unit is performed using AI. For example, the prompt input unit can input the user's social media data into a generation AI and have it input relevant prompts.

[0052] The prompt input unit can customize the input method by reflecting the user's past feedback when entering a prompt. The prompt input unit customizes the input method by reflecting the user's past feedback, for example. For example, the prompt input unit can suggest an optimal input method based on feedback provided by the user in the past. It can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. It can also customize the input interface based on the user's past feedback. In this way, it is possible to provide a more appropriate input method by customizing the input method by reflecting the user's past feedback. Some or all of the above-described processing in the prompt input unit is performed using AI. For example, the prompt input unit can input the user's past feedback data into a generation AI to customize the input method.

[0053] During analysis, the analysis unit can propose multiple countermeasures and select the optimal one from among them. The analysis unit, for example, uses a generation AI to propose multiple countermeasures and select the optimal one from among them. For example, the generation AI can propose multiple routes and select the optimal route from among them. The generation AI can also propose multiple countermeasures (e.g., detour route, waiting) and select the optimal one from among them. The generation AI can also simulate multiple scenarios and select the optimal scenario from among them. In this way, by proposing multiple countermeasures and selecting the optimal one from among them, it is possible to provide a more appropriate countermeasure. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input multiple countermeasures to the generation AI and have it select the optimal one.

[0054] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, uses a generation AI to improve the accuracy of the analysis by referring to past analysis results. For example, the generation AI can improve the analysis accuracy for the current situation based on past analysis results. The generation AI can also compare past analysis results with the current situation and propose optimal countermeasures. The generation AI can also learn from past analysis results and improve the analysis accuracy. In this way, by improving the analysis accuracy by referring to past analysis results, more appropriate countermeasures can be provided. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input past analysis results into the generation AI to improve the analysis accuracy.

[0055] During analysis, the analysis unit can simulate different scenarios and propose optimal countermeasures. The analysis unit, for example, uses a generation AI to simulate different scenarios and propose optimal countermeasures. For example, the generation AI can simulate different traffic conditions and propose optimal countermeasures. The generation AI can also simulate different weather conditions and propose optimal countermeasures. The generation AI can also simulate scenarios for different time periods and propose optimal countermeasures. In this way, by simulating different scenarios and proposing optimal countermeasures, more appropriate countermeasures can be provided. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input different scenarios into the generation AI and have it simulate them.

[0056] During analysis, the analysis unit can propose the optimal countermeasure by taking into account the user's geographical location information. The analysis unit, for example, uses a generation AI to propose the optimal countermeasure by taking into account the user's geographical location information. For example, the generation AI can propose the optimal detour route based on the user's current location. The generation AI can also propose the optimal waiting location based on the user's current location. The generation AI can also propose the optimal means of transportation based on the user's current location. In this way, by proposing the optimal countermeasure by taking into account the user's geographical location information, it is possible to provide a more appropriate countermeasure. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI and have it propose the optimal countermeasure.

[0057] During analysis, the analysis unit can analyze the user's social media activity and suggest relevant countermeasures. The analysis unit, for example, uses a generation AI to analyze the user's social media activity and suggest relevant countermeasures. For example, the generation AI analyzes the content of the user's social media posts and suggests relevant countermeasures. The generation AI can also suggest relevant countermeasures based on the activity of the user's friends on social media. The generation AI can also suggest relevant countermeasures based on the user's social media check-in information. In this way, by analyzing the user's social media activity and suggesting relevant countermeasures, more appropriate countermeasures can be provided. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's social media data into the generation AI and have it suggest relevant countermeasures.

[0058] During analysis, the analysis unit can customize the analysis method by reflecting the user's past feedback. The analysis unit, for example, uses a generation AI to customize the analysis method by reflecting the user's past feedback. For example, the generation AI can suggest the optimal analysis method based on the user's past feedback. The generation AI can also preferentially suggest a specific analysis method (e.g., simulation, real-time analysis) based on the user's past feedback. The generation AI can also customize the analysis interface based on the user's past feedback. In this way, by customizing the analysis method by reflecting the user's past feedback, a more appropriate analysis method can be provided. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's past feedback data into the generation AI to customize the analysis method.

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

[0060] The autonomous driving system may further include a traffic information acquisition unit that acquires real-time traffic information. The traffic information acquisition unit acquires traffic information, for example, via the Internet, and provides it to the analysis unit. This allows the analysis unit to propose more appropriate countermeasures based on the real-time traffic information. For example, the traffic information acquisition unit acquires congestion information and accident information and provides it to the analysis unit, thereby proposing detour routes. The traffic information acquisition unit may also acquire weather information and provide it to the analysis unit, thereby proposing driving instructions according to the weather. Furthermore, the traffic information acquisition unit may acquire the operating status of public transportation and provide it to the analysis unit, thereby proposing travel using public transportation.

[0061] The autonomous driving system may further include a health condition monitoring unit that monitors the user's health condition. The health condition monitoring unit may, for example, measure the user's heart rate and blood pressure and provide the results to the analysis unit. This allows the analysis unit to propose driving instructions that take the user's health condition into consideration. For example, if the user's heart rate is high, the health condition monitoring unit may suggest that the user take a break to relax. Also, if the user's blood pressure is high, the health condition monitoring unit may suggest that the user refrain from driving. Furthermore, the health condition monitoring unit may measure the user's stress level and, if stress is high, may propose driving instructions to reduce stress.

[0062] The autonomous driving system may further include a driving history recording unit that records the user's driving history. The driving history recording unit, for example, records the user's past driving data and provides it to the learning unit. This allows the learning unit to learn the user's driving style and provide more appropriate driving instructions. For example, the driving history recording unit may record what driving situations the user has encountered in the past and provide it to the learning unit, allowing the learning unit to learn countermeasures for similar situations. The driving history recording unit may also analyze the user's driving patterns and provide it to the learning unit to provide driving instructions tailored to the user's driving style. Furthermore, the driving history recording unit may suggest areas for improvement in driving based on the user's driving history.

[0063] The autonomous driving system may further include a driving style learning unit that learns the user's driving style and provides driving instructions based on the learned driving style. The driving style learning unit, for example, analyzes the user's past driving data to learn the user's driving style. As a result, the driving style learning unit can provide driving instructions that match the user's driving style. For example, the driving style learning unit can provide safer driving instructions if the user has a cautious driving style. Also, the driving style learning unit can provide more efficient driving instructions if the user has an aggressive driving style. Furthermore, the driving style learning unit can suggest driving improvements based on the user's driving style.

[0064] The autonomous driving system may further include a risk assessment unit that assesses driving risk based on the user's driving history. The risk assessment unit, for example, analyzes the user's past driving data to assess the driving risk. This allows the risk assessment unit to provide driving instructions according to the user's driving risk. For example, if the user has had many accidents in the past, the risk assessment unit may provide more cautious driving instructions. Alternatively, if the user has driven safely in the past, the risk assessment unit may provide normal driving instructions. Furthermore, the risk assessment unit may suggest improvements to the user's driving based on the user's driving risk.

[0065] The autonomous driving system may further include a performance evaluation unit that evaluates the user's driving performance based on the user's driving history. The performance evaluation unit, for example, analyzes the user's past driving data and evaluates the user's driving performance. This allows the performance evaluation unit to provide driving instructions according to the user's driving performance. For example, if the user has demonstrated high driving performance in the past, the performance evaluation unit may provide more efficient driving instructions. Furthermore, if the user has demonstrated low driving performance in the past, the performance evaluation unit may also suggest areas for improvement in driving. Furthermore, the performance evaluation unit may suggest a driving training program based on the user's driving performance.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The image generation unit uses generative AI to generate images of irregular events, such as obstacles that suddenly appear on the road or unexpected weather changes. It can also generate scenarios that would not be seen in normal driving situations. Step 2: The learning unit uses the generated images as training data. For example, the AI ​​model is trained using images generated by the generation AI. The generated images can also be preprocessed and data augmented. Step 3: The prompt input unit inputs a situation occurring during driving as a prompt. For example, the situation occurring during driving can be input using text input or voice input. The situation can also be input using gesture input. Step 4: The analysis unit analyzes the prompt entered by the prompt input unit and proposes a response to the situation. For example, the prompt can be analyzed using a generation AI and an appropriate response can be proposed. Alternatively, the prompt can be analyzed using a data analysis algorithm and a response can be proposed.

[0068] (Example 2) An autonomous driving system according to an embodiment of the present invention is a system for improving the recognition accuracy of irregular events. The system includes an image generation unit that generates images of irregular events, a learning unit that uses the generated images as learning data, a prompt input unit that inputs situations that occur during driving as prompts, and an analysis unit that analyzes the input prompts and proposes countermeasures for the situations. For example, the autonomous driving system uses a generation AI to generate images of irregular events, such as obstacles that suddenly appear on the road or unexpected weather changes. The autonomous driving system then uses the generated images as learning data, and the AI ​​learns from these irregular events. Furthermore, the autonomous driving system inputs situations that occur during driving as prompts, and the generation AI proposes appropriate countermeasures for the situations. This allows the autonomous driving system to improve the recognition accuracy of irregular events. For example, the generation AI generates images of irregular events, and the AI ​​learns from these situations, allowing the system to make appropriate decisions when encountering irregular events during actual driving. Furthermore, the system inputs situations that occur during driving as prompts, and the generation AI proposes countermeasures for the situations, enabling quick and appropriate responses.

[0069] An autonomous driving system according to an embodiment includes an image generation unit, a learning unit, a prompt input unit, and an analysis unit. The image generation unit generates images of irregular events using a generation AI. For example, the image generation unit generates images of irregular events, such as an obstacle that suddenly appears on the road or an unexpected change in weather. The image generation unit can also use the generation AI to generate scenarios that are not seen in normal driving situations. The learning unit uses the generated images as training data. For example, the learning unit trains an AI model using images generated by the generation AI. The learning unit can also preprocess the generated images and perform data augmentation. The prompt input unit inputs situations that occur during driving as prompts. For example, the prompt input unit can input situations during driving using text input or voice input. The prompt input unit can also input situations using gesture input. The analysis unit analyzes the prompt input by the prompt input unit and proposes countermeasures for the situation. For example, the analysis unit analyzes the prompt using a generation AI and proposes appropriate countermeasures. The analysis unit can also analyze the prompt using a data analysis algorithm and propose countermeasures. As a result, the autonomous driving system according to the embodiment can improve the accuracy of recognizing irregular events.

[0070] The image generation unit can generate images of irregular events such as obstacles that suddenly appear on the road or unexpected weather changes. The image generation unit, for example, uses a generation AI to generate images of obstacles that suddenly appear on the road. For example, the generation AI can generate a scenario in which an animal jumps out onto the road. The image generation unit can also use a generation AI to generate images of unexpected weather changes. For example, the generation AI can generate scenarios of sudden rain or snow. This allows the AI's learning data to be enriched by generating images of irregular events. Some or all of the above-mentioned processing in the image generation unit is performed using the generation AI. For example, the image generation unit can input a prompt to the generation AI to generate images of irregular events.

[0071] The learning unit can use the images generated by the image generation unit as training data. The learning unit, for example, trains an AI model using images generated by the generation AI. For example, the learning unit can preprocess the generated images and perform data augmentation. The learning unit can also adjust the parameters of the AI ​​model using the generated images. In this way, the recognition accuracy of the AI ​​can be improved by using the generated images as training data. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit can input the generated images into an AI model and perform training.

[0072] The prompt input unit can input situations that occur while driving as prompts. The prompt input unit can input situations while driving using, for example, text input. For example, the prompt input unit can input situations such as "traffic jam" or "road construction" in text. The prompt input unit can also input situations using voice input. For example, the prompt input unit can input "traffic jam" by voice. The prompt input unit can also input situations using gesture input. For example, the prompt input unit can input situations by performing specific gestures on the smartphone screen. This allows the situation while driving to be input in real time, making it possible for the AI ​​to quickly propose countermeasures. Some or all of the above-mentioned processing in the prompt input unit is performed using AI. For example, the prompt input unit can have AI analyze voice input to identify the situation.

[0073] The analysis unit can analyze the prompt input by the prompt input unit and propose a countermeasure for the situation. The analysis unit, for example, uses a generation AI to analyze the prompt and propose an appropriate countermeasure. For example, the analysis unit can input a prompt for "traffic congestion" to the generation AI and have it propose a detour route. The analysis unit can also analyze the prompt using a data analysis algorithm and propose a countermeasure. For example, the analysis unit can analyze a prompt for "road construction" using a data analysis algorithm and propose an instruction to adjust speed. This makes it possible to quickly respond to irregular situations while driving by analyzing the prompt and proposing an appropriate countermeasure. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input a prompt to the generation AI and have it propose a countermeasure.

[0074] The analysis unit can propose countermeasures for sudden traffic congestion or unexpected road construction situations. The analysis unit, for example, uses the generation AI to propose countermeasures for sudden traffic congestion situations. For example, the analysis unit can input a "traffic congestion" prompt to the generation AI and have it propose a detour route. The analysis unit can also use the generation AI to propose countermeasures for unexpected road construction situations. For example, the analysis unit can input a "road construction" prompt to the generation AI and have it propose a speed adjustment instruction. This makes it possible to propose appropriate countermeasures for irregular situations such as traffic congestion or road construction. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input a prompt to the generation AI and have it propose a countermeasure.

[0075] The autonomous driving system further includes an image generation unit that estimates the user's emotions and adjusts the type of irregular event to be generated based on the estimated user emotions. The image generation unit estimates the user's emotions using a generation AI and adjusts the type of irregular event to be generated based on the estimated user emotions. For example, if the user is nervous, the generation AI can generate a relatively minor irregular event (a small obstacle or a mild weather change). Alternatively, if the user is relaxed, the generation AI can generate a complex irregular event (a large-scale traffic accident or a sudden weather change). Alternatively, if the user is excited, the generation AI can generate a visually stimulating irregular event (a sudden animal jumping out or a large-scale event). This allows for more appropriate training data to be provided by adjusting the type of irregular event to be generated based on the user's emotions. 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 image generation unit is performed using the generation AI. For example, the image generation unit can input user emotional data into the generation AI and adjust the type of irregular event.

[0076] The image generation unit can include a scenario combining multiple irregular events in the generated image. The image generation unit, for example, uses a generation AI to generate a scenario combining multiple irregular events. For example, the generation AI generates a scenario combining an obstacle suddenly appearing on a road with a sudden change in weather. The generation AI can also generate a scenario in which road construction is being carried out simultaneously with a traffic jam. The generation AI can also generate a scenario in which a pedestrian suddenly runs out into the road with a traffic light malfunction. In this way, by generating a scenario combining multiple irregular events, more realistic training data can be provided. Some or all of the above-mentioned processing in the image generation unit is performed using the generation AI. For example, the image generation unit can input a prompt to the generation AI to generate a scenario combining multiple irregular events.

[0077] The image generation unit can reflect different time periods and seasonal changes in the images it generates. The image generation unit, for example, uses a generation AI to generate images that reflect different time periods and seasonal changes. For example, the generation AI generates a scenario that compares daytime traffic congestion with nighttime traffic congestion. The generation AI can also generate a scenario that compares driving conditions on sunny summer days with driving conditions on snowy winter roads. The generation AI can also generate a scenario that compares driving conditions during morning rush hour with driving conditions during quiet late-night hours. In this way, by generating images that reflect different time periods and seasonal changes, more diverse learning data can be provided. Some or all of the above-mentioned processing in the image generation unit is performed using a generation AI. For example, the image generation unit can input prompts to the generation AI to generate images that reflect different time periods and seasonal changes.

[0078] The image generation unit can incorporate different viewpoints and camera angles into the images it generates. The image generation unit generates images incorporating different viewpoints and camera angles, for example, using a generation AI. For example, the generation AI generates a scenario that combines a viewpoint from the driver's seat and a viewpoint from a drone. The generation AI can also generate a scenario that combines a viewpoint from the front and rear of the vehicle. The generation AI can also generate a scenario that combines viewpoints from the inside and outside of the vehicle. In this way, by generating images that incorporate different viewpoints and camera angles, it is possible to provide more multifaceted learning data. Some or all of the above-mentioned processing in the image generation unit is performed using a generation AI. For example, the image generation unit can input a prompt to the generation AI to generate images incorporating different viewpoints and camera angles.

[0079] The autonomous driving system further includes an image generation unit that estimates the user's emotions and adjusts the level of detail of the generated image based on the estimated user's emotions. The image generation unit estimates the user's emotions using a generation AI and adjusts the level of detail of the generated image based on the estimated user's emotions. For example, if the user is nervous, the generation AI can generate a simple image with low detail. Alternatively, if the user is relaxed, the generation AI can generate a complex image with high detail. Alternatively, if the user is excited, the generation AI can generate a visually stimulating image with high detail. This allows for adjusting the level of detail of the generated image based on the user's emotions, thereby providing more appropriate training 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 image generation unit is performed using the generation AI. For example, the image generation unit can input user emotion data into the generation AI and adjust the level of detail of the image.

[0080] The image generation unit can reflect different geographical conditions and cultural backgrounds in the images it generates. The image generation unit, for example, uses a generation AI to generate images that reflect different geographical conditions and cultural backgrounds. For example, the generation AI generates a scenario that compares driving conditions in urban and rural areas. The generation AI can also generate scenarios that reflect traffic rules and signs in different countries. The generation AI can also generate scenarios that reflect driving conditions in areas with different cultural backgrounds. This allows for the generation of images that reflect different geographical conditions and cultural backgrounds, thereby providing more diverse learning data. Some or all of the above-mentioned processing in the image generation unit is performed using a generation AI. For example, the image generation unit can input prompts to the generation AI to generate images that reflect different geographical conditions and cultural backgrounds.

[0081] The image generation unit can include different traffic rules and signs in the images it generates. The image generation unit generates images that include different traffic rules and signs, for example, using a generation AI. For example, the generation AI generates scenarios that reflect the traffic rules of different countries. The generation AI can also generate scenarios that reflect signs from different regions. The generation AI can also generate scenarios that emphasize specific traffic rules (e.g., stop signs, priority roads). In this way, by generating images that include different traffic rules and signs, it is possible to provide more diverse learning data. Some or all of the above-mentioned processing in the image generation unit is performed using the generation AI. For example, the image generation unit can input prompts to the generation AI to generate images that include different traffic rules and signs.

[0082] The image generation unit can reflect different vehicle types and traffic conditions in the images it generates. The image generation unit, for example, uses a generation AI to generate images that reflect different vehicle types and traffic conditions. For example, the generation AI generates scenarios that reflect different vehicle types (e.g., trucks, bicycles, motorcycles). The generation AI can also generate scenarios that reflect different traffic conditions (e.g., traffic jams, smooth traffic). The generation AI can also generate scenarios that reflect different vehicle behaviors (e.g., sudden braking, sudden acceleration). This allows for the generation of images that reflect different vehicle types and traffic conditions, thereby providing more diverse learning data. Some or all of the above-described processing in the image generation unit is performed using the generation AI. For example, the image generation unit can input prompts to the generation AI to generate images that reflect different vehicle types and traffic conditions.

[0083] The autonomous driving system further includes a learning unit that estimates the user's emotions and selects training data based on the estimated user emotions. The learning unit estimates the user's emotions using AI and selects training data based on the estimated user emotions. For example, if the user is nervous, the generation AI selects images of relatively minor irregular events as training data. Alternatively, if the user is relaxed, the generation AI can select images of complex irregular events as training data. Alternatively, if the user is excited, the generation AI can select images of visually stimulating irregular events as training data. This allows for more appropriate training data to be provided by selecting training data based on the user's emotions. The 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 learning unit is performed using AI. For example, the learning unit can input user emotion data into the generation AI and have it select training data.

[0084] During learning, the learning unit can emphasize particularly important parts of the generated images for learning. For example, the learning unit emphasizes particularly important parts of the images generated using the generation AI for learning. For example, the generation AI can emphasize obstacles that suddenly appear on the road for learning. The generation AI can also emphasize sudden weather changes for learning. The generation AI can also emphasize important parts of traffic congestion and road construction for learning. In this way, by emphasizing important parts for learning, learning accuracy can be improved. Some or all of the above-mentioned processes in the learning unit are performed using AI. For example, the learning unit can input a prompt to the generation AI and emphasize important parts for learning.

[0085] The learning unit can improve learning accuracy by combining different AI models during learning. The learning unit, for example, uses the generation AI to combine and learn different AI models. For example, the generation AI can learn by combining different AI models (e.g., image recognition model, natural language processing model). The generation AI can also learn by combining different data sources (e.g., real-time data, past data). The generation AI can also learn by combining different algorithms (e.g., deep learning, reinforcement learning). In this way, learning accuracy can be improved by combining and learning different AI models. Some or all of the above-mentioned processes in the learning unit are performed using AI. For example, the learning unit can learn by combining different AI models.

[0086] During learning, the learning unit can combine past driving data with generated images for learning. The learning unit, for example, uses a generation AI to combine past driving data with generated images for learning. For example, the generation AI can combine past driving data with generated images of irregular events for learning. The generation AI can also combine past driving data with real-time traffic information for learning. The generation AI can also combine past driving data with images of different weather conditions for learning. In this way, by combining past driving data with generated images for learning, learning accuracy can be improved. Some or all of the above-mentioned processes in the learning unit are performed using AI. For example, the learning unit can make the generation AI learn by combining past driving data with generated images.

[0087] The autonomous driving system further includes a learning unit that estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The learning unit estimates the user's emotions using AI and adjusts the learning frequency based on the estimated user emotions. For example, if the user is nervous, the generation AI sets the learning frequency low. Alternatively, if the user is relaxed, the generation AI can set the learning frequency high. Alternatively, if the user is excited, the generation AI can set the learning frequency to medium. This allows for adjusting the learning frequency according to the user's emotions, thereby providing more appropriate learning data. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or 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 learning unit is performed using AI. For example, the learning unit can input user emotion data into the generation AI and adjust the learning frequency.

[0088] During learning, the learning unit can integrate information from different data sources to expand the learning data. The learning unit, for example, uses a generation AI to integrate information from different data sources to expand the learning data. For example, the generation AI can integrate real-time traffic information and past driving data to learn. The generation AI can also integrate data under different weather conditions to learn. The generation AI can also integrate data under different geographical conditions to learn. In this way, by integrating information from different data sources, the learning data can be expanded and the learning accuracy can be improved. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit can have the generation AI integrate information from different data sources to learn.

[0089] The learning unit can use data that reflects different environmental conditions (weather, time of day, etc.) when learning. The learning unit, for example, uses a generation AI to learn using data that reflects different environmental conditions. For example, the generation AI can learn using data from sunny and rainy days. The generation AI can also learn using data from daytime and nighttime. The generation AI can also learn using data from different seasons. In this way, using data that reflects different environmental conditions can increase the diversity of the learning data and improve learning accuracy. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit can input data from different environmental conditions into the generation AI and have it learn.

[0090] The learning unit can take different driving styles and driver characteristics into consideration during learning. The learning unit, for example, uses a generation AI to take different driving styles and driver characteristics into consideration during learning. For example, the generation AI can learn using data on cautious driving styles and aggressive driving styles. The generation AI can also learn using data on drivers of different age groups. The generation AI can also learn using data on drivers with different experience levels. In this way, by taking different driving styles and driver characteristics into consideration during learning, the diversity of the learning data can be increased and learning accuracy can be improved. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit can input data on different driving styles and driver characteristics into the generation AI and have it learn.

[0091] The autonomous driving system further includes a prompt input unit that estimates a user's emotions and adjusts the prompt input method based on the estimated user emotions. The prompt input unit estimates the user's emotions using AI and adjusts the prompt input method based on the estimated user emotions. For example, if the user is nervous, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Alternatively, if the user is in a hurry, voice input can be prioritized to allow prompt input to be completed quickly. This allows the prompt input method to be adjusted according to the user's emotions, thereby providing a more appropriate input method. 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 prompt input unit is performed using AI. For example, the prompt input unit can input user emotion data into the generation AI and adjust the prompt input method.

[0092] The prompt input unit can support voice input and gesture input when inputting a prompt. The prompt input unit, for example, inputs a prompt using voice input. For example, the user simply inputs "traffic jam" by voice, and the generation AI analyzes the situation. The prompt input unit can also input a prompt using gesture input. For example, the user can easily input a prompt by performing a specific gesture on the smartphone screen. The prompt input unit can also combine voice input and gesture input to input a prompt more intuitively. In this way, supporting voice input and gesture input enables more intuitive prompt input. Some or all of the above-mentioned processing in the prompt input unit is performed using AI. For example, the prompt input unit can input voice data or gesture data into the generation AI and have it analyze the prompt.

[0093] The prompt input unit can allow multiple situations to be input simultaneously when inputting a prompt. The prompt input unit can, for example, input multiple situations simultaneously. For example, the user can input "traffic jam" and "road construction" simultaneously, and the generation AI can analyze both situations. Alternatively, the user can input "rain" and "traffic light out of order" simultaneously, and the generation AI can analyze both situations. Alternatively, the user can input "animal jumping out" and "sudden change in weather" simultaneously, and the generation AI can analyze both situations. By allowing multiple situations to be input simultaneously, more complex situations can be handled. Some or all of the above-mentioned processing in the prompt input unit is performed using AI. For example, the prompt input unit can input multiple prompts to the generation AI and have it analyze them simultaneously.

[0094] The prompt input unit can perform input completion by referring to past input history when entering a prompt. The prompt input unit performs input completion by referring to, for example, past input history. For example, prompts previously entered by the user can be automatically displayed as candidates. The prompt input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The prompt input unit can also predict and suggest prompts to be used in a specific time period based on the user's past input history. This makes it possible to improve input efficiency by performing input completion by referring to the past input history. Some or all of the above-described processing in the prompt input unit is performed using AI. For example, the prompt input unit can input the past input history into a generation AI and have it perform input completion.

[0095] The autonomous driving system further includes a prompt input unit that estimates a user's emotions and prioritizes prompts based on the estimated user emotions. The prompt input unit estimates the user's emotions using AI and prioritizes prompts based on the estimated user emotions. For example, if the user is nervous, the generation AI can prioritize displaying prompts with high importance. Also, if the user is relaxed, the generation AI can prioritize displaying detailed prompts. Also, if the user is in a hurry, the generation AI can prioritize displaying prompts that can be quickly responded to. This prioritizes prompts based on the user's emotions, allowing for more appropriate prompts to be provided. The 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 prompt input unit is performed using AI. For example, the prompt input unit can input user emotion data to the generation AI to determine the priority of prompts.

[0096] When inputting a prompt, the prompt input unit can prioritize inputting a highly relevant prompt by taking into account the user's geographical location information. The prompt input unit, for example, prioritizes inputting a highly relevant prompt by taking into account the user's geographical location information. For example, if the user is in a specific area, prompts related to that area can be displayed preferentially. Also, if the user is on a specific road, prompts related to that road can be displayed preferentially. Also, if the user is in a specific facility, prompts related to that facility can be displayed preferentially. In this way, by preferentially inputting a highly relevant prompt by taking into account the user's geographical location information, more appropriate prompts can be provided. Some or all of the above-described processing in the prompt input unit is performed using AI. For example, the prompt input unit can input the user's geographical location information to a generation AI and cause it to preferentially input a highly relevant prompt.

[0097] The prompt input unit can analyze the user's social media activity and input relevant prompts when inputting a prompt. The prompt input unit, for example, analyzes the user's social media activity and inputs relevant prompts. For example, it displays prompts related to places the user has checked in to on social media. It can also analyze the content of the user's social media posts and display relevant prompts. It can also display relevant prompts based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity and inputting relevant prompts, it is possible to provide more appropriate prompts. Some or all of the above-described processing in the prompt input unit is performed using AI. For example, the prompt input unit can input the user's social media data into a generation AI and have it input relevant prompts.

[0098] The prompt input unit can customize the input method by reflecting the user's past feedback when entering a prompt. The prompt input unit customizes the input method by reflecting the user's past feedback, for example. For example, the prompt input unit can suggest an optimal input method based on feedback provided by the user in the past. It can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. It can also customize the input interface based on the user's past feedback. In this way, it is possible to provide a more appropriate input method by customizing the input method by reflecting the user's past feedback. Some or all of the above-described processing in the prompt input unit is performed using AI. For example, the prompt input unit can input the user's past feedback data into a generation AI to customize the input method.

[0099] The autonomous driving system further includes an analysis unit that estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using a generation AI and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This allows for adjusting the display method of the analysis results according to the user's emotions to provide a more appropriate display method. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input user emotion data into the generation AI and adjust the display method of the analysis results.

[0100] During analysis, the analysis unit can propose multiple countermeasures and select the optimal one from among them. The analysis unit, for example, uses a generation AI to propose multiple countermeasures and select the optimal one from among them. For example, the generation AI can propose multiple routes and select the optimal route from among them. The generation AI can also propose multiple countermeasures (e.g., detour route, waiting) and select the optimal one from among them. The generation AI can also simulate multiple scenarios and select the optimal scenario from among them. In this way, by proposing multiple countermeasures and selecting the optimal one from among them, it is possible to provide a more appropriate countermeasure. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input multiple countermeasures to the generation AI and have it select the optimal one.

[0101] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, uses a generation AI to improve the accuracy of the analysis by referring to past analysis results. For example, the generation AI can improve the analysis accuracy for the current situation based on past analysis results. The generation AI can also compare past analysis results with the current situation and propose optimal countermeasures. The generation AI can also learn from past analysis results and improve the analysis accuracy. In this way, by improving the analysis accuracy by referring to past analysis results, more appropriate countermeasures can be provided. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input past analysis results into the generation AI to improve the analysis accuracy.

[0102] During analysis, the analysis unit can simulate different scenarios and propose optimal countermeasures. The analysis unit, for example, uses a generation AI to simulate different scenarios and propose optimal countermeasures. For example, the generation AI can simulate different traffic conditions and propose optimal countermeasures. The generation AI can also simulate different weather conditions and propose optimal countermeasures. The generation AI can also simulate scenarios for different time periods and propose optimal countermeasures. In this way, by simulating different scenarios and proposing optimal countermeasures, more appropriate countermeasures can be provided. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input different scenarios into the generation AI and have it simulate them.

[0103] The autonomous driving system further includes an analysis unit that estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using a generation AI and prioritizes the analysis results based on the estimated user emotions. For example, if the user is nervous, the generation AI can prioritize displaying analysis results with high importance. Also, if the user is relaxed, the generation AI can prioritize displaying detailed analysis results. Also, if the user is in a hurry, the generation AI can prioritize displaying analysis results that can be addressed quickly. This prioritizes the analysis results based on the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input user emotion data into the generation AI and have it prioritize the analysis results.

[0104] During analysis, the analysis unit can propose the optimal countermeasure by taking into account the user's geographical location information. The analysis unit, for example, uses a generation AI to propose the optimal countermeasure by taking into account the user's geographical location information. For example, the generation AI can propose the optimal detour route based on the user's current location. The generation AI can also propose the optimal waiting location based on the user's current location. The generation AI can also propose the optimal means of transportation based on the user's current location. In this way, by proposing the optimal countermeasure by taking into account the user's geographical location information, it is possible to provide a more appropriate countermeasure. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI and have it propose the optimal countermeasure.

[0105] During analysis, the analysis unit can analyze the user's social media activity and suggest relevant countermeasures. The analysis unit, for example, uses a generation AI to analyze the user's social media activity and suggest relevant countermeasures. For example, the generation AI analyzes the content of the user's social media posts and suggests relevant countermeasures. The generation AI can also suggest relevant countermeasures based on the activity of the user's friends on social media. The generation AI can also suggest relevant countermeasures based on the user's social media check-in information. In this way, by analyzing the user's social media activity and suggesting relevant countermeasures, more appropriate countermeasures can be provided. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's social media data into the generation AI and have it suggest relevant countermeasures.

[0106] During analysis, the analysis unit can customize the analysis method by reflecting the user's past feedback. The analysis unit, for example, uses a generation AI to customize the analysis method by reflecting the user's past feedback. For example, the generation AI can suggest the optimal analysis method based on the user's past feedback. The generation AI can also preferentially suggest a specific analysis method (e.g., simulation, real-time analysis) based on the user's past feedback. The generation AI can also customize the analysis interface based on the user's past feedback. In this way, by customizing the analysis method by reflecting the user's past feedback, a more appropriate analysis method can be provided. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's past feedback data into the generation AI to customize the analysis method. === Hard Collateral 1-1 === Each of the multiple elements including the image generation unit, learning unit, prompt input unit, and analysis 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 image generation unit generates an image of an irregular event using the camera 42 of the smart device 14, and executes a generation AI using the specific processing unit 290 of the data processing device 12. The learning unit uses the generated image as learning data using the specific processing unit 290 of the data processing device 12. The prompt input unit inputs the driving situation using the reception device 38 of the smart device 14, and the input is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the prompt using the specific processing unit 290 of the data processing device 12 and proposes an appropriate countermeasure. === Hard Collateral 1-2 === Each of the multiple elements including the image generation unit, learning unit, prompt input unit, and analysis unit described above is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the image generation unit generates an image of an irregular event using the camera 42 of the smart glasses 214, and executes a generation AI using the specific processing unit 290 of the data processing device 12. The learning unit uses the generated image as learning data using the specific processing unit 290 of the data processing device 12. The prompt input unit inputs a driving situation using the microphone 238 of the smart glasses 214, which is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the prompt using the specific processing unit 290 of the data processing device 12 and proposes an appropriate countermeasure. === Hard Collateral 1-3 === Each of the multiple elements including the image generation unit, learning unit, prompt input unit, and analysis 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 image generation unit generates an image of an irregular event using the camera 42 of the headset type terminal 314, and executes a generation AI by the specific processing unit 290 of the data processing device 12. The learning unit uses the generated image as learning data by the specific processing unit 290 of the data processing device 12. The prompt input unit inputs the situation during driving using the microphone 238 of the headset type terminal 314, and the input is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the prompt by the specific processing unit 290 of the data processing device 12 and proposes an appropriate countermeasure. === Hard Collateral 1-4 === Each of the multiple elements including the image generation unit, learning unit, prompt input unit, and analysis unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the image generation unit generates an image of an irregular event using the camera 42 of the robot 414, and the specific processing unit 290 of the data processing device 12 executes a generation AI. The learning unit uses the generated image as learning data by the specific processing unit 290 of the data processing device 12. The prompt input unit inputs the situation during driving using the microphone 238 of the robot 414, and the input is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the prompt by the specific processing unit 290 of the data processing device 12 and proposes an appropriate countermeasure.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The autonomous driving system may further include a traffic information acquisition unit that acquires real-time traffic information. The traffic information acquisition unit acquires traffic information, for example, via the Internet, and provides it to the analysis unit. This allows the analysis unit to propose more appropriate countermeasures based on the real-time traffic information. For example, the traffic information acquisition unit acquires congestion information and accident information and provides it to the analysis unit, thereby proposing detour routes. The traffic information acquisition unit may also acquire weather information and provide it to the analysis unit, thereby proposing driving instructions according to the weather. Furthermore, the traffic information acquisition unit may acquire the operating status of public transportation and provide it to the analysis unit, thereby proposing travel using public transportation.

[0109] The autonomous driving system may further include a health condition monitoring unit that monitors the user's health condition. The health condition monitoring unit may, for example, measure the user's heart rate and blood pressure and provide the results to the analysis unit. This allows the analysis unit to propose driving instructions that take the user's health condition into consideration. For example, if the user's heart rate is high, the health condition monitoring unit may suggest that the user take a break to relax. Also, if the user's blood pressure is high, the health condition monitoring unit may suggest that the user refrain from driving. Furthermore, the health condition monitoring unit may measure the user's stress level and, if stress is high, may propose driving instructions to reduce stress.

[0110] The autonomous driving system may further include a driving history recording unit that records the user's driving history. The driving history recording unit, for example, records the user's past driving data and provides it to the learning unit. This allows the learning unit to learn the user's driving style and provide more appropriate driving instructions. For example, the driving history recording unit may record what driving situations the user has encountered in the past and provide it to the learning unit, allowing the learning unit to learn countermeasures for similar situations. The driving history recording unit may also analyze the user's driving patterns and provide it to the learning unit to provide driving instructions tailored to the user's driving style. Furthermore, the driving history recording unit may suggest areas for improvement in driving based on the user's driving history.

[0111] The autonomous driving system may further include a driving mode switching unit that estimates the user's emotions and switches the driving mode based on the estimated user's emotions. For example, the driving mode switching unit may switch to a relaxed mode if the user is nervous. Alternatively, the driving mode switching unit may switch to a normal mode if the user is relaxed. Furthermore, the driving mode switching unit may switch to a cautious mode if the user is excited. This allows for safer and more comfortable driving by switching the driving mode according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. Some or all of the above-described processing in the driving mode switching unit is performed using the generation AI. For example, the driving mode switching unit may input user emotion data into the generation AI and switch the driving mode.

[0112] The autonomous driving system may further include a voice assistant adjustment unit that estimates the user's emotions and adjusts the voice assistant's response content based on the estimated user emotions. For example, if the user is nervous, the voice assistant adjustment unit may respond in a calm tone. If the user is relaxed, the voice assistant adjustment unit may respond in a friendly tone. If the user is excited, the voice assistant adjustment unit may respond in a calm tone. This allows the voice assistant's response content to be adjusted according to the user's emotions, thereby providing more appropriate communication. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Some or all of the above-mentioned processing in the voice assistant adjustment unit is performed using the generation AI. For example, the voice assistant adjustment unit may input user emotion data into the generation AI and have it adjust the response content.

[0113] The autonomous driving system may further include an environment adjustment unit that estimates the user's emotions and adjusts the in-car environment based on the estimated user emotions. For example, if the user is nervous, the environment adjustment unit can dim the interior lights and play relaxing music. Alternatively, if the user is relaxed, the environment adjustment unit can provide normal lighting and music. Furthermore, if the user is excited, the environment adjustment unit can lower the temperature inside the car and provide an environment that helps the user stay calm. This allows the in-car environment to be adjusted according to the user's emotions, providing a more comfortable driving environment. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Some or all of the above-mentioned processing in the environment adjustment unit is performed using the generation AI. For example, the environment adjustment unit can input user emotion data into the generation AI and have it adjust the in-car environment.

[0114] The autonomous driving system may further include a driving assistance adjustment unit that estimates the user's emotions and adjusts the driving assistance function based on the estimated user emotions. For example, if the user is nervous, the driving assistance adjustment unit may strengthen the driving assistance function to provide more assistance. Alternatively, if the user is relaxed, the driving assistance function may be set to a normal level. Furthermore, if the user is excited, the driving assistance function may be carefully set to enhance safety. This allows for safer and more comfortable driving by adjusting the driving assistance function according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Some or all of the above-mentioned processing in the driving assistance adjustment unit is performed using a generation AI. For example, the driving assistance adjustment unit may input user emotion data into the generation AI and adjust the driving assistance function.

[0115] The autonomous driving system may further include a driving style learning unit that learns the user's driving style and provides driving instructions based on the learned driving style. The driving style learning unit, for example, analyzes the user's past driving data to learn the user's driving style. As a result, the driving style learning unit can provide driving instructions that match the user's driving style. For example, the driving style learning unit can provide safer driving instructions if the user has a cautious driving style. Also, the driving style learning unit can provide more efficient driving instructions if the user has an aggressive driving style. Furthermore, the driving style learning unit can suggest driving improvements based on the user's driving style.

[0116] The autonomous driving system may further include a risk assessment unit that assesses driving risk based on the user's driving history. The risk assessment unit, for example, analyzes the user's past driving data to assess the driving risk. This allows the risk assessment unit to provide driving instructions according to the user's driving risk. For example, if the user has had many accidents in the past, the risk assessment unit may provide more cautious driving instructions. Alternatively, if the user has driven safely in the past, the risk assessment unit may provide normal driving instructions. Furthermore, the risk assessment unit may suggest improvements to the user's driving based on the user's driving risk.

[0117] The autonomous driving system may further include a performance evaluation unit that evaluates the user's driving performance based on the user's driving history. The performance evaluation unit, for example, analyzes the user's past driving data and evaluates the user's driving performance. This allows the performance evaluation unit to provide driving instructions according to the user's driving performance. For example, if the user has demonstrated high driving performance in the past, the performance evaluation unit may provide more efficient driving instructions. Furthermore, if the user has demonstrated low driving performance in the past, the performance evaluation unit may also suggest areas for improvement in driving. Furthermore, the performance evaluation unit may suggest a driving training program based on the user's driving performance.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The image generation unit uses generative AI to generate images of irregular events, such as obstacles that suddenly appear on the road or unexpected weather changes. It can also generate scenarios that would not be seen in normal driving situations. Step 2: The learning unit uses the generated images as training data. For example, the AI ​​model is trained using images generated by the generation AI. The generated images can also be preprocessed and data augmented. Step 3: The prompt input unit inputs a situation occurring during driving as a prompt. For example, the situation occurring during driving can be input using text input or voice input. The situation can also be input using gesture input. Step 4: The analysis unit analyzes the prompt entered by the prompt input unit and proposes a response to the situation. For example, the prompt can be analyzed using a generation AI and an appropriate response can be proposed. Alternatively, the prompt can be analyzed using a data analysis algorithm and a response can be proposed.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image generator for generating an image of an irregular event; a learning unit that uses the images generated by the image generation unit as learning data; a prompt input unit for inputting a situation occurring during driving as a prompt; an analysis unit that analyzes the prompt input by the prompt input unit and proposes a countermeasure for the situation. A system characterized by:

2. The image generation unit Generate images of irregular events such as obstacles suddenly appearing on the road or unexpected weather changes 2. The system of claim 1.

3. The learning unit The images generated by the image generating unit are used as learning data.

2. The system of claim 1.

4. The prompt input unit Enter situations that occur while driving as prompts 2. The system of claim 1.

5. The analysis unit Analyzing the prompt input by the prompt input unit and proposing a countermeasure for the situation 2. The system of claim 1.

6. The analysis unit Propose solutions for sudden traffic jams or unexpected road construction situations 2. The system of claim 1.

7. The image generation unit Estimate the user's emotions and adjust the types of irregular events to be generated based on the estimated user emotions.

2. The system of claim 1.

8. The image generation unit Generated images include scenarios that combine multiple irregular events 2. The system of claim 1.

9. The image generation unit Generate images that reflect different times of day and seasonal changes 2. The system of claim 1.

Images