system

Abstract

The system according to this embodiment aims to appropriately respond to irregular events during autonomous driving. [Solution] The system according to the embodiment comprises an image generation unit, a dataset addition unit, a prompt input unit, and a response determination unit. The image generation unit generates images for irregular events. The dataset addition unit adds the images generated by the image generation unit to the dataset. The prompt input unit takes the operating situation as a prompt. The response determination unit determines an appropriate response based on the situation input by the prompt input unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the response to irregular events in autonomous driving is not sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to appropriately respond to irregular events during autonomous driving.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an image generation unit, a dataset addition unit, a prompt input unit, and a response determination unit. The image generation unit generates images for irregular events. The dataset addition unit adds the images generated by the image generation unit to the dataset. The prompt input unit takes the operating situation as a prompt. The response determination unit determines an appropriate response based on the situation input by the prompt input unit. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately respond to irregular events during autonomous driving. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The autonomous driving system according to an embodiment of the present invention proposes methods for improving the recognition accuracy of autonomous driving by AI. While autonomous driving systems can handle driving under normal conditions, they lack the accuracy to recognize irregular situations. To solve this problem, we propose two methods utilizing generative AI. First, there is a method for generating images of irregular events. Using generative AI, images are generated that reproduce irregular situations not included in normal datasets. For example, the generative AI reproduces situations such as an animal suddenly jumping out onto the road or driving conditions under abnormal weather conditions. This allows the AI ​​to respond to irregular situations that it has not previously learned. Next, there is a method for inputting situations as prompts during driving. Irregular situations that occur during driving are input to the generative AI as prompts in real time using speech recognition technology. For example, a situation such as "There is an obstacle ahead" is input by voice while driving, and the generative AI determines an appropriate response based on that situation. This allows for a rapid response to irregular situations during driving. By using these methods, the recognition accuracy of autonomous driving by AI can be improved, and a world of autonomous driving with as few misrecognitions as possible can be realized. Specifically, it consists of the following steps. First, a generative AI is used to generate images of irregular events. Next, the generated images are added to a dataset, and the AI ​​is trained. This allows the AI ​​to respond to irregular situations. Furthermore, irregular situations that occur during driving are input to the generative AI as prompts using voice recognition technology, and the generative AI determines the appropriate response based on the situation. For example, if the driver inputs "An animal has suddenly jumped out in front of me" while driving, the generative AI will determine the appropriate response based on the situation and support driving. In addition, by adding images of irregular situations generated by the generative AI to the dataset, the AI ​​can respond to situations it has not previously learned. This mechanism improves the recognition accuracy of AI-driven autonomous driving, making it possible to realize an autonomous driving world with as few misrecognitions as possible.For example, it will be able to handle irregular situations that AI has not been able to cope with before, such as when an animal suddenly jumps out onto the road or when driving under abnormal weather conditions. This will improve the safety of autonomous driving and allow more people to use autonomous driving with peace of mind. As a result, autonomous driving systems will be able to improve the recognition accuracy of AI-based autonomous driving, realizing a world of autonomous driving with as few misrecognitions as possible.

[0029] The autonomous driving system according to this embodiment includes an image generation unit, a dataset addition unit, a prompt input unit, and a response determination unit. The image generation unit generates images for irregular events. The image generation unit generates images that reproduce irregular situations not included in normal datasets, for example, using a generation AI. For example, the image generation unit generates an image that reproduces a situation where an animal suddenly jumps out onto the road. The image generation unit can also generate images that reproduce driving conditions under abnormal weather conditions. For example, the image generation unit generates an image that reproduces driving conditions in heavy rain or snow. Furthermore, the image generation unit can use a generation AI to generate images that reproduce driving conditions at night or in fog. The dataset addition unit adds the images generated by the image generation unit to the dataset. The dataset addition unit integrates the generated images into an existing dataset, for example, to train the AI. For example, the dataset addition unit adds the generated images to the dataset so that the AI ​​can respond to irregular situations. The dataset addition unit can also classify the generated images and add them to appropriate categories. For example, the dataset addition unit classifies the generated images into categories such as traffic accidents, natural disasters, and mechanical failures. The prompt input unit receives the driving situation as a prompt. The prompt input unit uses, for example, speech recognition technology to input irregular situations that occur during driving as prompts to the generating AI in real time. For example, the prompt input unit can input a situation such as "There is an obstacle ahead" by voice while driving. The prompt input unit can also input a situation such as "The road is slippery" by voice while driving. Furthermore, the prompt input unit can also input a situation such as "Visibility is poor" by voice while driving. The response decision unit determines the appropriate response based on the situation input by the prompt input unit. The response decision unit uses, for example, the generating AI to determine the appropriate response based on the input situation. For example, if there is an obstacle ahead, the response decision unit will instruct the driver to take evasive action. The response decision unit can also instruct the driver to slow down if the road is slippery. Furthermore, the response decision unit can instruct the driver to turn on the lights if visibility is poor.As a result, the automated driving system according to this embodiment can generate images in response to irregular events, add to datasets, input prompts, and make decisions on how to respond.

[0030] The image generation unit generates images in response to irregular events. For example, the image generation unit uses a generation AI to generate images that reproduce irregular situations not included in normal datasets. Specifically, the generation AI generates images in response to specific prompts based on a vast dataset that it has previously trained on. For example, when generating an image that reproduces a situation where an animal suddenly jumps out onto the road, the generation AI generates a realistic image considering the type of animal, its movement, and the road conditions. It can also generate images that reproduce driving conditions under abnormal weather conditions. For example, when generating an image that reproduces driving conditions in heavy rain or snow, the generation AI reproduces details such as the way the rain or snow falls, poor visibility, and the slipperiness of the road. Furthermore, when generating an image that reproduces driving conditions at night or in fog, the generation AI generates a realistic image considering light reflection, limited visibility, and fog density. In this way, the image generation unit can generate images that reproduce a variety of irregular situations not included in normal datasets, and these can be used as training data for autonomous driving systems.

[0031] The dataset addition unit adds images generated by the image generation unit to the dataset. For example, the dataset addition unit integrates the generated images into the existing dataset and uses them to train the AI. Specifically, it performs the process of converting the generated images to an appropriate format and adding them to the existing dataset. For example, it classifies the generated images into categories such as traffic accidents, natural disasters, and machine failures, and adds them to the dataset corresponding to each category. The dataset addition unit also manages the metadata of the generated images, recording the date and time of image generation, generation conditions, prompt content, etc. This allows for efficient management of training data so that the AI ​​can handle irregular situations. Furthermore, the dataset addition unit can evaluate the quality of the generated images and filter or correct them as needed. For example, if the generated images are blurry or contain incorrect information, it performs appropriate corrections to ensure quality before adding them to the dataset. This allows the dataset addition unit to effectively integrate the generated images into the dataset and improve the accuracy of the AI's learning.

[0032] The prompt input unit receives driving conditions as prompts. For example, the prompt input unit uses speech recognition technology to input irregular situations that occur during driving as prompts to the generating AI in real time. Specifically, when the driver reports a situation by voice, the speech recognition system converts the content into text and inputs it to the generating AI. For example, if the driver inputs a situation such as "There is an obstacle ahead" by voice while driving, the speech recognition system accurately recognizes the content and provides it to the generating AI as a prompt. Similarly, if the driver inputs a situation such as "The road is slippery" by voice, the speech recognition system can convert the content into text and input it to the generating AI. Furthermore, even if the driver inputs a situation such as "Visibility is poor" by voice, the speech recognition system accurately recognizes the content and provides it to the generating AI as a prompt. In this way, the prompt input unit can input irregular situations during driving to the generating AI in real time and provide information for determining appropriate actions.

[0033] The response decision unit determines the appropriate response based on the situation input by the prompt input unit. For example, the response decision unit uses a generating AI to determine the appropriate response based on the input situation. Specifically, the generating AI refers to past data and learning results based on the input prompt and calculates the optimal response. For example, if there is an obstacle ahead, the generating AI will instruct the system to take evasive action based on past data. Also, if the road is slippery, the generating AI can instruct the system to slow down. Furthermore, if visibility is poor, the generating AI can instruct the system to turn on the lights. This allows the response decision unit to determine the appropriate response in real time based on the information provided by the prompt input unit and issue instructions to the autonomous driving system. In addition, the response decision unit can handle cases where multiple prompts are input simultaneously. For example, if there is an obstacle ahead and the road is slippery, the generating AI will consider both situations to determine the optimal response. The response decision unit can also incorporate past response results as feedback, continuously learn, and improve response accuracy. This allows the response decision unit to always provide highly accurate response decisions based on the latest information, improving the safety and reliability of the autonomous driving system.

[0034] The system includes a generation AI unit that generates images using generation AI. The generation AI unit generates images that reproduce irregular situations not included in normal datasets, for example, using generation AI. For example, the generation AI unit generates an image that reproduces a situation where an animal suddenly jumps out onto the road. The generation AI unit can also generate images that reproduce driving conditions under abnormal weather conditions. For example, the generation AI unit generates an image that reproduces driving conditions in heavy rain or snow. Furthermore, the generation AI unit can use generation AI to generate images that reproduce driving conditions at night or in fog. This improves the accuracy of image generation by using generation AI. Some or all of the above-described processes in the generation AI unit may be performed using generation AI, for example, or without generation AI. For example, the generation AI unit can add images generated using generation AI to a dataset and train the AI.

[0035] The system includes a voice recognition unit that uses voice recognition technology to input situations. The voice recognition unit inputs irregular situations that occur during driving as prompts in real time using voice recognition technology to the generating AI. For example, the voice recognition unit can input a situation such as "There is an obstacle ahead" by voice while driving. The voice recognition unit can also input a situation such as "The road is slippery" by voice while driving. Furthermore, the voice recognition unit can also input a situation such as "Visibility is poor" by voice while driving. This improves the accuracy of situation input by using voice recognition technology. Some or all of the above processing in the voice recognition unit may be performed using AI, for example, or without AI. For example, the voice recognition unit inputs the situation entered using voice recognition technology to the generating AI, and the generating AI can determine an appropriate response based on that situation.

[0036] The system includes a dataset management unit that adds generated images to a dataset. The dataset management unit can, for example, integrate generated images into an existing dataset and train an AI. For example, the dataset management unit can add generated images to a dataset so that the AI ​​can handle irregular situations. The dataset management unit can also classify generated images and add them to the appropriate category. For example, the dataset management unit can classify generated images into categories such as traffic accidents, natural disasters, and machine failures. This makes dataset management more efficient when using the dataset management unit. Some or all of the above-described processes in the dataset management unit may be performed using AI, or not using AI. For example, the dataset management unit can input generated images into an AI, which can classify the images and add them to a dataset.

[0037] The system includes an AI unit that determines an appropriate response based on the driving situation. The AI ​​unit determines an appropriate response based on the situation input using, for example, a generating AI. For example, if there is an obstacle ahead, the AI ​​unit will instruct the driver to take evasive action. The AI ​​unit can also instruct the driver to slow down if the road is slippery. Furthermore, the AI ​​unit can instruct the driver to turn on the headlights if visibility is poor. As a result, the AI ​​unit enables the driver to quickly determine an appropriate response. Some or all of the above-described processes in the AI ​​unit may be performed using, for example, AI, or without AI. For example, the AI ​​unit can determine an appropriate response based on the situation input using a generating AI and instruct the driver of the result.

[0038] The image generation unit can generate images that reproduce irregular situations not included in a normal dataset. For example, the image generation unit can use a generation AI to generate images that reproduce irregular situations not included in a normal dataset. For example, the image generation unit can generate an image that reproduces a situation where an animal suddenly jumps out onto the road. The image generation unit can also generate images that reproduce driving conditions under abnormal weather conditions. For example, the image generation unit can generate an image that reproduces driving conditions in heavy rain or snow. Furthermore, the image generation unit can use a generation AI to generate an image that reproduces driving conditions at night or in fog. By generating images that reproduce irregular situations, the recognition accuracy of the AI ​​is improved. Some or all of the above processing in the image generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the image generation unit can add images generated using a generation AI to a dataset and train the AI.

[0039] The prompt input unit can input irregular situations that occur during driving as prompts to the generating AI in real time using speech recognition technology. For example, the prompt input unit can input situations such as "There is an obstacle ahead" by voice while driving. It can also input situations such as "The road is slippery" by voice while driving. Furthermore, it can input situations such as "Visibility is poor" by voice while driving. This enables quick responses by inputting prompts in real time. Some or all of the above processing in the prompt input unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the prompt input unit inputs the situation entered using speech recognition technology to the generating AI, and the generating AI can determine an appropriate response based on that situation.

[0040] The response determination unit can determine an appropriate response based on the situation input to the generating AI. For example, the response determination unit uses the generating AI to determine an appropriate response based on the input situation. For example, if there is an obstacle ahead, the response determination unit will instruct the driver to take evasive action. The response determination unit can also instruct the driver to slow down if the road is slippery. Furthermore, the response determination unit can instruct the driver to turn on the lights if visibility is poor. In this way, by using the generating AI, an appropriate response can be determined quickly. Some or all of the above processing in the response determination unit may be performed using the generating AI, or without using the generating AI. For example, the response determination unit can use the generating AI to determine an appropriate response based on the input situation and instruct the driver of the result.

[0041] The image generation unit can reproduce a wider variety of irregular events by including visual information from different viewpoints and angles in the images it generates. For example, the image generation unit uses a generation AI to generate images that include visual information from different viewpoints and angles. For example, the image generation unit can generate an image that reproduces traffic congestion from an aerial view of a road. It can also generate an image that reproduces congestion in a parking lot from a vehicle's side view. Furthermore, it can generate an image that reproduces congestion at a crosswalk from a pedestrian's viewpoint. In this way, a wider variety of irregular events can be reproduced by including visual information from different viewpoints and angles. Some or all of the above processing in the image generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the image generation unit can add images generated using a generation AI to a dataset and train the AI.

[0042] The image generation unit can provide more realistic scenarios by reflecting different time periods and seasonal changes in the images it generates. For example, the image generation unit can use a generation AI to generate images that reflect different time periods and seasonal changes. For example, the image generation unit can generate an image that reproduces nighttime road conditions. It can also generate an image that reproduces the conditions of a snowy road in winter. Furthermore, it can generate an image that reproduces the road conditions with fallen leaves in autumn. In this way, more realistic scenarios are provided by reflecting different time periods and seasonal changes. Some or all of the above processing in the image generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the image generation unit can add images generated using a generation AI to a dataset and train the AI.

[0043] The image generation unit can provide a wider variety of scenarios by reflecting different traffic conditions and road conditions in the images it generates. For example, the image generation unit can use a generation AI to generate images that reflect different traffic conditions and road conditions. For example, the image generation unit can generate an image that reproduces a congested road situation. It can also generate an image that reproduces a road situation under construction. Furthermore, the image generation unit can generate an image that reproduces a road situation at an accident site. In this way, a wider variety of scenarios can be provided by reflecting different traffic conditions and road conditions. Some or all of the above processing in the image generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the image generation unit can add images generated using a generation AI to a dataset and train the AI.

[0044] The image generation unit can provide more realistic scenarios by reflecting different weather conditions in the images it generates. For example, the image generation unit can use a generation AI to generate images that reflect different weather conditions. For example, the image generation unit can generate an image that reproduces road conditions in rainy weather. It can also generate an image that reproduces road conditions on a snowy day. Furthermore, it can generate an image that reproduces road conditions in fog. In this way, more realistic scenarios are provided by reflecting different weather conditions. Some or all of the above processing in the image generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the image generation unit can add images generated using a generation AI to a dataset and train the AI.

[0045] The dataset addition unit can be configured to automatically generate and manage image metadata when adding images to a dataset. For example, the dataset addition unit can automatically generate metadata such as the date and location where an image was taken and add it to the dataset. It can also automatically generate tags related to the image content and add them to the dataset. Furthermore, it can automatically generate metadata such as the image resolution and format information and add it to the dataset. This streamlines dataset management by automatically generating and managing image metadata. Some or all of the above processing in the dataset addition unit may be performed using AI, for example, or without AI. For example, the dataset addition unit can input image metadata into AI, which can then generate that metadata and add it to the dataset.

[0046] The dataset addition unit can be equipped with a function to evaluate the quality of images added to the dataset and automatically exclude low-quality images. For example, the dataset addition unit can automatically exclude images with low resolution. It can also automatically exclude images with a lot of noise. Furthermore, it can automatically exclude images that are blurry. This improves the quality of the dataset by automatically excluding low-quality images. Some or all of the above processing in the dataset addition unit may be performed using AI, for example, or without AI. For example, the dataset addition unit can input the image quality into an AI, which can evaluate the quality and exclude low-quality images.

[0047] The dataset addition unit can be equipped with a function to evaluate the relevance of images when adding them to the dataset and prioritize adding highly relevant images. For example, the dataset addition unit can prioritize adding images to the dataset if their content is related to the current driving situation. It can also prioritize adding images to the dataset if their content is related to past driving history. Furthermore, it can prioritize adding images to the dataset if their content is related to a specific driving scenario. This improves the quality of the dataset by prioritizing the addition of highly relevant images. Some or all of the above processing in the dataset addition unit may be performed using AI, for example, or without AI. For example, the dataset addition unit can input the relevance of images into an AI, which can evaluate that relevance, select highly relevant images, and add them to the dataset.

[0048] The dataset addition unit can be equipped with a function to evaluate image diversity when adding images to the dataset and prioritize adding images with high diversity. For example, the dataset addition unit can prioritize adding images taken from different viewpoints or angles to the dataset. It can also prioritize adding images that reflect different times of day or seasonal changes to the dataset. Furthermore, it can prioritize adding images that reflect different traffic conditions or road conditions to the dataset. This improves the quality of the dataset by prioritizing the addition of images with high diversity. Some or all of the above processing in the dataset addition unit may be performed using AI, for example, or without AI. For example, the dataset addition unit can input image diversity into AI, which can evaluate that diversity, select images with high diversity, and add them to the dataset.

[0049] The prompt input unit can generate the optimal prompt by referring to the driver's past driving history when a prompt is entered. For example, the prompt input unit may prioritize providing prompts that have been used in similar situations in the past. The prompt input unit can also provide prompts that have been used at specific times or locations based on past driving history. Furthermore, the prompt input unit can analyze past driving history and provide the most effective prompt. In this way, the optimal prompt is provided by referring to past driving history. Some or all of the above processing in the prompt input unit may be performed using AI, for example, or without AI. For example, the prompt input unit can input the driver's past driving history into AI, which can then analyze that history and generate the optimal prompt.

[0050] The prompt input unit can analyze the driver's current driving situation in real time when a prompt is input and generate the optimal prompt. For example, the prompt input unit can analyze the current traffic situation in real time and provide an appropriate prompt. It can also analyze the current weather conditions in real time and provide an appropriate prompt. Furthermore, it can analyze the current road conditions in real time and provide an appropriate prompt. In this way, the optimal prompt is provided by analyzing the current driving situation in real time. Some or all of the above processing in the prompt input unit may be performed using AI, for example, or without AI. For example, the prompt input unit can input the driver's current driving situation to the AI, which can then analyze the situation in real time and generate the optimal prompt.

[0051] The prompt input unit can generate the optimal prompt when a prompt is entered, taking into account the driver's geographical location information. For example, the prompt input unit can provide an appropriate prompt by considering the traffic conditions at the current location. It can also provide an appropriate prompt by considering the weather conditions at the current location. Furthermore, it can provide an appropriate prompt by considering the road conditions at the current location. In this way, the optimal prompt is provided by considering geographical location information. Some or all of the above processing in the prompt input unit may be performed using AI, for example, or without AI. For example, the prompt input unit can input the driver's geographical location information to the AI, and the AI ​​can generate the optimal prompt based on that information.

[0052] The prompt input unit can analyze the driver's social media activity and generate relevant prompts when a prompt is entered. For example, the prompt input unit can provide appropriate prompts based on information shared by the driver on social media. The prompt input unit can also analyze the driver's social media activity history and provide relevant prompts. Furthermore, the prompt input unit can provide appropriate prompts based on information about accounts the driver follows on social media. In this way, relevant prompts are provided by analyzing social media activity. Some or all of the above processing in the prompt input unit may be performed using AI, for example, or without AI. For example, the prompt input unit can input the driver's social media activity into AI, which can then analyze that activity and generate relevant prompts.

[0053] The response decision unit can select the optimal response by referring to past response history when making a response decision. For example, the response decision unit may prioritize selecting responses that were taken in similar situations in the past. The response decision unit can also select responses taken at specific times or locations from past response history. Furthermore, the response decision unit can analyze past response history and select the most effective response. In this way, the optimal response is provided by referring to past response history. Some or all of the above processing in the response decision unit may be performed using AI, for example, or without using AI. For example, the response decision unit can input past response history into AI, which can then analyze that history and select the optimal response.

[0054] The response decision unit can analyze the current driving situation in real time when making a response decision and select the optimal response. For example, the response decision unit can analyze the current traffic situation in real time and select an appropriate response. It can also analyze the current weather conditions in real time and select an appropriate response. Furthermore, it can analyze the current road conditions in real time and select an appropriate response. In this way, the optimal response is provided by analyzing the current driving situation in real time. Some or all of the above processing in the response decision unit may be performed using AI, for example, or without using AI. For example, the response decision unit can input the current driving situation into AI, which can analyze the situation in real time and select the optimal response.

[0055] The response decision unit can select the optimal response by considering the driver's geographical location information when making a response decision. For example, the response decision unit can select an appropriate response by considering the traffic conditions at the current location. It can also select an appropriate response by considering the weather conditions at the current location. Furthermore, it can also select an appropriate response by considering the road conditions at the current location. In this way, the optimal response is provided by considering geographical location information. Some or all of the above processing in the response decision unit may be performed using AI, for example, or without using AI. For example, the response decision unit can input the driver's geographical location information into the AI, and the AI ​​can select the optimal response based on that information.

[0056] The response determination unit can analyze the driver's social media activity and select a relevant response when making a response decision. For example, the response determination unit can select an appropriate response based on information shared by the driver on social media. The response determination unit can also analyze the driver's social media activity history and select a relevant response. Furthermore, the response determination unit can select an appropriate response based on information about accounts the driver follows on social media. In this way, relevant responses are provided by analyzing social media activity. Some or all of the above processing in the response determination unit may be performed using AI, for example, or without AI. For example, the response determination unit can input the driver's social media activity into AI, which can then analyze that activity and generate a relevant response.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] An autonomous driving system can analyze a driver's past driving history and provide optimal driving assistance based on the driver's driving style. For example, a driver who frequently uses sudden braking in the past can receive assistance to prompt deceleration in advance. Similarly, a driver who frequently drives on highways can receive assistance to maintain an optimal following distance on highways. Furthermore, a driver who frequently drives at night can receive assistance to improve nighttime visibility. By providing assistance tailored to the driver's driving style, both driving safety and comfort can be improved. The analysis of driving history may be performed using AI, or it may be performed without AI. For example, driving history data can be input into an AI, which then analyzes the data and generates optimal driving assistance.

[0059] An autonomous driving system can refer to the driver's past driving history and suggest the optimal route based on the driver's driving patterns. For example, it can prioritize suggesting a route to a driver who has frequently used a particular route in the past. It can also suggest a route that avoids congestion to a driver who has tended to avoid it in the past. Furthermore, it can suggest a route that utilizes highways to a driver who has preferred to use highways in the past. By suggesting the optimal route according to the driver's driving patterns, it can improve the efficiency and comfort of driving. Referencing driving history may be done using AI, or it may not require AI. For example, driving history data can be input into an AI, which can then analyze the data and generate the optimal route.

[0060] An autonomous driving system can analyze the driver's current driving situation in real time and provide optimal driving assistance. For example, it can analyze current traffic conditions in real time and provide assistance to avoid congestion. It can also analyze current weather conditions in real time and provide driving assistance in adverse weather conditions. Furthermore, it can analyze current road conditions in real time and provide assistance to avoid road construction and accidents. In this way, optimal driving assistance is provided by analyzing the current driving situation in real time. The real-time analysis of driving conditions may be performed using AI, for example, or without AI. For example, driving condition data can be input into an AI, which can analyze that data in real time and generate optimal driving assistance.

[0061] An autonomous driving system can provide optimal driving assistance by taking into account the driver's geographical location. For example, it can provide assistance to avoid congestion by considering the current traffic conditions. It can also provide driving assistance in adverse weather conditions by considering the current weather conditions. Furthermore, it can provide assistance to avoid road construction or accidents by considering the current road conditions. In this way, optimal driving assistance is provided by taking geographical location into account. This consideration of geographical location may be performed using AI, or it may be performed without using AI. For example, geographical location data can be input into an AI, which can then analyze the data and generate optimal driving assistance.

[0062] Autonomous driving systems can analyze a driver's social media activity and provide optimal driving assistance based on the driver's interests. For example, they can suggest routes to places of interest based on information the driver has shared on social media. They can also analyze the driver's social media activity history and provide relevant driving assistance. Furthermore, they can suggest routes to events and places of interest based on information about accounts the driver follows on social media. In this way, by analyzing social media activity, driving assistance is provided based on the driver's interests. The analysis of social media activity may be performed using AI or not. For example, social media activity data can be input into an AI, which can analyze the data and generate optimal driving assistance.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The image generation unit generates images for irregular events. For example, it uses a generation AI to generate images that reproduce irregular situations not included in normal datasets. Specifically, it generates images that reproduce situations such as animals suddenly jumping out onto the road or driving conditions under abnormal weather conditions (driving in heavy rain or snow, at night or in fog). Step 2: The dataset addition unit adds the images generated by the image generation unit to the dataset. For example, it integrates the generated images into an existing dataset to train the AI. It also classifies the generated images and adds them to appropriate categories such as traffic accidents, natural disasters, and machine failures. Step 3: The prompt input section takes the driving situation as a prompt. For example, it uses speech recognition technology to input irregular situations that occur while driving as prompts to the AI ​​in real time. Specifically, it inputs situations such as "There is an obstacle ahead," "The road is slippery," or "Visibility is poor" by voice. Step 4: The response decision unit determines the appropriate response based on the situation input by the prompt input unit. For example, it uses a generated AI to determine the appropriate response based on the input situation and instructs the vehicle to take evasive action if there is an obstacle ahead, to slow down if the road is slippery, and to turn on the lights if visibility is poor.

[0065] (Example of form 2) The autonomous driving system according to an embodiment of the present invention proposes methods for improving the recognition accuracy of autonomous driving by AI. While autonomous driving systems can handle driving under normal conditions, they lack the accuracy to recognize irregular situations. To solve this problem, we propose two methods utilizing generative AI. First, there is a method for generating images of irregular events. Using generative AI, images are generated that reproduce irregular situations not included in normal datasets. For example, the generative AI reproduces situations such as an animal suddenly jumping out onto the road or driving conditions under abnormal weather conditions. This allows the AI ​​to respond to irregular situations that it has not previously learned. Next, there is a method for inputting situations as prompts during driving. Irregular situations that occur during driving are input to the generative AI as prompts in real time using speech recognition technology. For example, a situation such as "There is an obstacle ahead" is input by voice while driving, and the generative AI determines an appropriate response based on that situation. This allows for a rapid response to irregular situations during driving. By using these methods, the recognition accuracy of autonomous driving by AI can be improved, and a world of autonomous driving with as few misrecognitions as possible can be realized. Specifically, it consists of the following steps. First, a generative AI is used to generate images of irregular events. Next, the generated images are added to a dataset, and the AI ​​is trained. This allows the AI ​​to respond to irregular situations. Furthermore, irregular situations that occur during driving are input to the generative AI as prompts using voice recognition technology, and the generative AI determines the appropriate response based on the situation. For example, if the driver inputs "An animal has suddenly jumped out in front of me" while driving, the generative AI will determine the appropriate response based on the situation and support driving. In addition, by adding images of irregular situations generated by the generative AI to the dataset, the AI ​​can respond to situations it has not previously learned. This mechanism improves the recognition accuracy of AI-driven autonomous driving, making it possible to realize an autonomous driving world with as few misrecognitions as possible.For example, it will be able to handle irregular situations that AI has not been able to cope with before, such as when an animal suddenly jumps out onto the road or when driving under abnormal weather conditions. This will improve the safety of autonomous driving and allow more people to use autonomous driving with peace of mind. As a result, autonomous driving systems will be able to improve the recognition accuracy of AI-based autonomous driving, realizing a world of autonomous driving with as few misrecognitions as possible.

[0066] The autonomous driving system according to this embodiment includes an image generation unit, a dataset addition unit, a prompt input unit, and a response determination unit. The image generation unit generates images for irregular events. The image generation unit generates images that reproduce irregular situations not included in normal datasets, for example, using a generation AI. For example, the image generation unit generates an image that reproduces a situation where an animal suddenly jumps out onto the road. The image generation unit can also generate images that reproduce driving conditions under abnormal weather conditions. For example, the image generation unit generates an image that reproduces driving conditions in heavy rain or snow. Furthermore, the image generation unit can use a generation AI to generate images that reproduce driving conditions at night or in fog. The dataset addition unit adds the images generated by the image generation unit to the dataset. The dataset addition unit integrates the generated images into an existing dataset, for example, to train the AI. For example, the dataset addition unit adds the generated images to the dataset so that the AI ​​can respond to irregular situations. The dataset addition unit can also classify the generated images and add them to appropriate categories. For example, the dataset addition unit classifies the generated images into categories such as traffic accidents, natural disasters, and mechanical failures. The prompt input unit receives the driving situation as a prompt. The prompt input unit uses, for example, speech recognition technology to input irregular situations that occur during driving as prompts to the generating AI in real time. For example, the prompt input unit can input a situation such as "There is an obstacle ahead" by voice while driving. The prompt input unit can also input a situation such as "The road is slippery" by voice while driving. Furthermore, the prompt input unit can also input a situation such as "Visibility is poor" by voice while driving. The response decision unit determines the appropriate response based on the situation input by the prompt input unit. The response decision unit uses, for example, the generating AI to determine the appropriate response based on the input situation. For example, if there is an obstacle ahead, the response decision unit will instruct the driver to take evasive action. The response decision unit can also instruct the driver to slow down if the road is slippery. Furthermore, the response decision unit can instruct the driver to turn on the lights if visibility is poor.As a result, the automated driving system according to this embodiment can generate images in response to irregular events, add to datasets, input prompts, and make decisions on how to respond.

[0067] The image generation unit generates images in response to irregular events. For example, the image generation unit uses a generation AI to generate images that reproduce irregular situations not included in normal datasets. Specifically, the generation AI generates images in response to specific prompts based on a vast dataset that it has previously trained on. For example, when generating an image that reproduces a situation where an animal suddenly jumps out onto the road, the generation AI generates a realistic image considering the type of animal, its movement, and the road conditions. It can also generate images that reproduce driving conditions under abnormal weather conditions. For example, when generating an image that reproduces driving conditions in heavy rain or snow, the generation AI reproduces details such as the way the rain or snow falls, poor visibility, and the slipperiness of the road. Furthermore, when generating an image that reproduces driving conditions at night or in fog, the generation AI generates a realistic image considering light reflection, limited visibility, and fog density. In this way, the image generation unit can generate images that reproduce a variety of irregular situations not included in normal datasets, and these can be used as training data for autonomous driving systems.

[0068] The dataset addition unit adds images generated by the image generation unit to the dataset. For example, the dataset addition unit integrates the generated images into the existing dataset and uses them to train the AI. Specifically, it performs the process of converting the generated images to an appropriate format and adding them to the existing dataset. For example, it classifies the generated images into categories such as traffic accidents, natural disasters, and machine failures, and adds them to the dataset corresponding to each category. The dataset addition unit also manages the metadata of the generated images, recording the date and time of image generation, generation conditions, prompt content, etc. This allows for efficient management of training data so that the AI ​​can handle irregular situations. Furthermore, the dataset addition unit can evaluate the quality of the generated images and filter or correct them as needed. For example, if the generated images are blurry or contain incorrect information, it performs appropriate corrections to ensure quality before adding them to the dataset. This allows the dataset addition unit to effectively integrate the generated images into the dataset and improve the accuracy of the AI's learning.

[0069] The prompt input unit receives driving conditions as prompts. For example, the prompt input unit uses speech recognition technology to input irregular situations that occur during driving as prompts to the generating AI in real time. Specifically, when the driver reports a situation by voice, the speech recognition system converts the content into text and inputs it to the generating AI. For example, if the driver inputs a situation such as "There is an obstacle ahead" by voice while driving, the speech recognition system accurately recognizes the content and provides it to the generating AI as a prompt. Similarly, if the driver inputs a situation such as "The road is slippery" by voice, the speech recognition system can convert the content into text and input it to the generating AI. Furthermore, even if the driver inputs a situation such as "Visibility is poor" by voice, the speech recognition system accurately recognizes the content and provides it to the generating AI as a prompt. In this way, the prompt input unit can input irregular situations during driving to the generating AI in real time and provide information for determining appropriate actions.

[0070] The response decision unit determines the appropriate response based on the situation input by the prompt input unit. For example, the response decision unit uses a generating AI to determine the appropriate response based on the input situation. Specifically, the generating AI refers to past data and learning results based on the input prompt and calculates the optimal response. For example, if there is an obstacle ahead, the generating AI will instruct the system to take evasive action based on past data. Also, if the road is slippery, the generating AI can instruct the system to slow down. Furthermore, if visibility is poor, the generating AI can instruct the system to turn on the lights. This allows the response decision unit to determine the appropriate response in real time based on the information provided by the prompt input unit and issue instructions to the autonomous driving system. In addition, the response decision unit can handle cases where multiple prompts are input simultaneously. For example, if there is an obstacle ahead and the road is slippery, the generating AI will consider both situations to determine the optimal response. The response decision unit can also incorporate past response results as feedback, continuously learn, and improve response accuracy. This allows the response decision unit to always provide highly accurate response decisions based on the latest information, improving the safety and reliability of the autonomous driving system.

[0071] The system includes a generation AI unit that generates images using generation AI. The generation AI unit generates images that reproduce irregular situations not included in normal datasets, for example, using generation AI. For example, the generation AI unit generates an image that reproduces a situation where an animal suddenly jumps out onto the road. The generation AI unit can also generate images that reproduce driving conditions under abnormal weather conditions. For example, the generation AI unit generates an image that reproduces driving conditions in heavy rain or snow. Furthermore, the generation AI unit can use generation AI to generate images that reproduce driving conditions at night or in fog. This improves the accuracy of image generation by using generation AI. Some or all of the above-described processes in the generation AI unit may be performed using generation AI, for example, or without generation AI. For example, the generation AI unit can add images generated using generation AI to a dataset and train the AI.

[0072] The system includes a voice recognition unit that uses voice recognition technology to input situations. The voice recognition unit inputs irregular situations that occur during driving as prompts in real time using voice recognition technology to the generating AI. For example, the voice recognition unit can input a situation such as "There is an obstacle ahead" by voice while driving. The voice recognition unit can also input a situation such as "The road is slippery" by voice while driving. Furthermore, the voice recognition unit can also input a situation such as "Visibility is poor" by voice while driving. This improves the accuracy of situation input by using voice recognition technology. Some or all of the above processing in the voice recognition unit may be performed using AI, for example, or without AI. For example, the voice recognition unit inputs the situation entered using voice recognition technology to the generating AI, and the generating AI can determine an appropriate response based on that situation.

[0073] The system includes a dataset management unit that adds generated images to a dataset. The dataset management unit can, for example, integrate generated images into an existing dataset and train an AI. For example, the dataset management unit can add generated images to a dataset so that the AI ​​can handle irregular situations. The dataset management unit can also classify generated images and add them to the appropriate category. For example, the dataset management unit can classify generated images into categories such as traffic accidents, natural disasters, and machine failures. This makes dataset management more efficient when using the dataset management unit. Some or all of the above-described processes in the dataset management unit may be performed using AI, or not using AI. For example, the dataset management unit can input generated images into an AI, which can classify the images and add them to a dataset.

[0074] The system includes an AI unit that determines an appropriate response based on the driving situation. The AI ​​unit determines an appropriate response based on the situation input using, for example, a generating AI. For example, if there is an obstacle ahead, the AI ​​unit will instruct the driver to take evasive action. The AI ​​unit can also instruct the driver to slow down if the road is slippery. Furthermore, the AI ​​unit can instruct the driver to turn on the headlights if visibility is poor. As a result, the AI ​​unit enables the driver to quickly determine an appropriate response. Some or all of the above-described processes in the AI ​​unit may be performed using, for example, AI, or without AI. For example, the AI ​​unit can determine an appropriate response based on the situation input using a generating AI and instruct the driver of the result.

[0075] The image generation unit can generate images that reproduce irregular situations not included in a normal dataset. For example, the image generation unit can use a generation AI to generate images that reproduce irregular situations not included in a normal dataset. For example, the image generation unit can generate an image that reproduces a situation where an animal suddenly jumps out onto the road. The image generation unit can also generate images that reproduce driving conditions under abnormal weather conditions. For example, the image generation unit can generate an image that reproduces driving conditions in heavy rain or snow. Furthermore, the image generation unit can use a generation AI to generate an image that reproduces driving conditions at night or in fog. By generating images that reproduce irregular situations, the recognition accuracy of the AI ​​is improved. Some or all of the above processing in the image generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the image generation unit can add images generated using a generation AI to a dataset and train the AI.

[0076] The prompt input unit can input irregular situations that occur during driving as prompts to the generating AI in real time using speech recognition technology. For example, the prompt input unit can input situations such as "There is an obstacle ahead" by voice while driving. It can also input situations such as "The road is slippery" by voice while driving. Furthermore, it can input situations such as "Visibility is poor" by voice while driving. This enables quick responses by inputting prompts in real time. Some or all of the above processing in the prompt input unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the prompt input unit inputs the situation entered using speech recognition technology to the generating AI, and the generating AI can determine an appropriate response based on that situation.

[0077] The response determination unit can determine an appropriate response based on the situation input to the generating AI. For example, the response determination unit uses the generating AI to determine an appropriate response based on the input situation. For example, if there is an obstacle ahead, the response determination unit will instruct the driver to take evasive action. The response determination unit can also instruct the driver to slow down if the road is slippery. Furthermore, the response determination unit can instruct the driver to turn on the lights if visibility is poor. In this way, by using the generating AI, an appropriate response can be determined quickly. Some or all of the above processing in the response determination unit may be performed using the generating AI, or without using the generating AI. For example, the response determination unit can use the generating AI to determine an appropriate response based on the input situation and instruct the driver of the result.

[0078] The image generation unit can estimate the driver's emotions and adjust the content of the generated image based on the estimated emotions. For example, if the driver is tense, the generating AI can generate a calm landscape image to alleviate the tension. The image generation unit can also generate a refreshing natural landscape image if the driver is tired. Furthermore, if the driver is excited, the generating AI can generate a tranquil night view image to help them regain their composure. This provides an image appropriate to the driver's state by generating images that match their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the image generation unit may be performed using a generating AI, or not. For example, the image generation unit can input driver emotion data into a generating AI, which can then generate an appropriate image based on those emotions.

[0079] The image generation unit can reproduce a wider variety of irregular events by including visual information from different viewpoints and angles in the images it generates. For example, the image generation unit uses a generation AI to generate images that include visual information from different viewpoints and angles. For example, the image generation unit can generate an image that reproduces traffic congestion from an aerial view of a road. It can also generate an image that reproduces congestion in a parking lot from a vehicle's side view. Furthermore, it can generate an image that reproduces congestion at a crosswalk from a pedestrian's viewpoint. In this way, a wider variety of irregular events can be reproduced by including visual information from different viewpoints and angles. Some or all of the above processing in the image generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the image generation unit can add images generated using a generation AI to a dataset and train the AI.

[0080] The image generation unit can provide more realistic scenarios by reflecting different time periods and seasonal changes in the images it generates. For example, the image generation unit can use a generation AI to generate images that reflect different time periods and seasonal changes. For example, the image generation unit can generate an image that reproduces nighttime road conditions. It can also generate an image that reproduces the conditions of a snowy road in winter. Furthermore, it can generate an image that reproduces the road conditions with fallen leaves in autumn. In this way, more realistic scenarios are provided by reflecting different time periods and seasonal changes. Some or all of the above processing in the image generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the image generation unit can add images generated using a generation AI to a dataset and train the AI.

[0081] The image generation unit can estimate the driver's emotions and determine the priority of images to generate based on the estimated emotions. For example, if the driver is tense, the generating AI will prioritize generating images with a relaxing effect. Similarly, if the driver is tired, the generating AI can prioritize generating images with a refreshing effect. Furthermore, if the driver is excited, the generating AI can prioritize generating images that help the driver regain composure. This ensures that images appropriate to the driver's state are provided by prioritizing images according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the image generation unit may be performed using a generating AI, or not. For example, the image generation unit can input driver emotion data into a generating AI, which can then generate an appropriate image based on those emotions.

[0082] The image generation unit can provide a wider variety of scenarios by reflecting different traffic conditions and road conditions in the images it generates. For example, the image generation unit can use a generation AI to generate images that reflect different traffic conditions and road conditions. For example, the image generation unit can generate an image that reproduces a congested road situation. It can also generate an image that reproduces a road situation under construction. Furthermore, the image generation unit can generate an image that reproduces a road situation at an accident site. In this way, a wider variety of scenarios can be provided by reflecting different traffic conditions and road conditions. Some or all of the above processing in the image generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the image generation unit can add images generated using a generation AI to a dataset and train the AI.

[0083] The image generation unit can provide more realistic scenarios by reflecting different weather conditions in the images it generates. For example, the image generation unit can use a generation AI to generate images that reflect different weather conditions. For example, the image generation unit can generate an image that reproduces road conditions in rainy weather. It can also generate an image that reproduces road conditions on a snowy day. Furthermore, it can generate an image that reproduces road conditions in fog. In this way, more realistic scenarios are provided by reflecting different weather conditions. Some or all of the above processing in the image generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the image generation unit can add images generated using a generation AI to a dataset and train the AI.

[0084] The dataset addition unit can estimate the driver's emotions and select images to add to the dataset based on the estimated emotions. For example, if the driver is tense, the dataset addition unit can add images with a relaxing effect to the dataset. It can also add images with a refreshing effect if the driver is tired. Furthermore, if the driver is excited, the dataset addition unit can add images to help them regain their composure. This improves the quality of the dataset by adding images that correspond to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dataset addition unit may be performed using AI or not. For example, the dataset addition unit can input driver emotion data into an AI, which can then select appropriate images based on those emotions and add them to the dataset.

[0085] The dataset addition unit can be configured to automatically generate and manage image metadata when adding images to a dataset. For example, the dataset addition unit can automatically generate metadata such as the date and location where an image was taken and add it to the dataset. It can also automatically generate tags related to the image content and add them to the dataset. Furthermore, it can automatically generate metadata such as the image resolution and format information and add it to the dataset. This streamlines dataset management by automatically generating and managing image metadata. Some or all of the above processing in the dataset addition unit may be performed using AI, for example, or without AI. For example, the dataset addition unit can input image metadata into AI, which can then generate that metadata and add it to the dataset.

[0086] The dataset addition unit can be equipped with a function to evaluate the quality of images added to the dataset and automatically exclude low-quality images. For example, the dataset addition unit can automatically exclude images with low resolution. It can also automatically exclude images with a lot of noise. Furthermore, it can automatically exclude images that are blurry. This improves the quality of the dataset by automatically excluding low-quality images. Some or all of the above processing in the dataset addition unit may be performed using AI, for example, or without AI. For example, the dataset addition unit can input the image quality into an AI, which can evaluate the quality and exclude low-quality images.

[0087] The dataset appending unit can estimate the driver's emotions and determine the priority of images to add to the dataset based on the estimated emotions. For example, if the driver is tense, the dataset appending unit can prioritize adding images with a relaxing effect to the dataset. Similarly, if the driver is tired, it can prioritize adding images with a refreshing effect. Furthermore, if the driver is excited, it can prioritize adding images that help the driver regain calmness. This improves the quality of the dataset by prioritizing images according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dataset appending unit may be performed using AI or not. For example, the dataset appending unit can input driver emotion data into an AI, which can then select appropriate images based on those emotions and add them to the dataset.

[0088] The dataset addition unit can be equipped with a function to evaluate the relevance of images when adding them to the dataset and prioritize adding highly relevant images. For example, the dataset addition unit can prioritize adding images to the dataset if their content is related to the current driving situation. It can also prioritize adding images to the dataset if their content is related to past driving history. Furthermore, it can prioritize adding images to the dataset if their content is related to a specific driving scenario. This improves the quality of the dataset by prioritizing the addition of highly relevant images. Some or all of the above processing in the dataset addition unit may be performed using AI, for example, or without AI. For example, the dataset addition unit can input the relevance of images into an AI, which can evaluate that relevance, select highly relevant images, and add them to the dataset.

[0089] The dataset addition unit can be equipped with a function to evaluate image diversity when adding images to the dataset and prioritize adding images with high diversity. For example, the dataset addition unit can prioritize adding images taken from different viewpoints or angles to the dataset. It can also prioritize adding images that reflect different times of day or seasonal changes to the dataset. Furthermore, it can prioritize adding images that reflect different traffic conditions or road conditions to the dataset. This improves the quality of the dataset by prioritizing the addition of images with high diversity. Some or all of the above processing in the dataset addition unit may be performed using AI, for example, or without AI. For example, the dataset addition unit can input image diversity into AI, which can evaluate that diversity, select images with high diversity, and add them to the dataset.

[0090] The prompt input unit can estimate the driver's emotions and adjust the content of the prompt based on the estimated emotions. For example, if the driver is tense, the prompt input unit can provide a simple and clear prompt. If the driver is relaxed, the prompt input unit can also provide a prompt with more detailed information. Furthermore, if the driver is in a hurry, the prompt input unit can provide a prompt that allows for a quick response. By providing prompts that are appropriate to the driver's emotions, prompts suitable for the driver's state are provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prompt input unit may be performed using AI, or not using AI. For example, the prompt input unit can input driver emotion data into an AI, and the AI ​​can generate an appropriate prompt based on that emotion.

[0091] The prompt input unit can generate the optimal prompt by referring to the driver's past driving history when a prompt is entered. For example, the prompt input unit may prioritize providing prompts that have been used in similar situations in the past. The prompt input unit can also provide prompts that have been used at specific times or locations based on past driving history. Furthermore, the prompt input unit can analyze past driving history and provide the most effective prompt. In this way, the optimal prompt is provided by referring to past driving history. Some or all of the above processing in the prompt input unit may be performed using AI, for example, or without AI. For example, the prompt input unit can input the driver's past driving history into AI, which can then analyze that history and generate the optimal prompt.

[0092] The prompt input unit can analyze the driver's current driving situation in real time when a prompt is input and generate the optimal prompt. For example, the prompt input unit can analyze the current traffic situation in real time and provide an appropriate prompt. It can also analyze the current weather conditions in real time and provide an appropriate prompt. Furthermore, it can analyze the current road conditions in real time and provide an appropriate prompt. In this way, the optimal prompt is provided by analyzing the current driving situation in real time. Some or all of the above processing in the prompt input unit may be performed using AI, for example, or without AI. For example, the prompt input unit can input the driver's current driving situation to the AI, which can then analyze the situation in real time and generate the optimal prompt.

[0093] The prompt input unit can estimate the driver's emotions and determine the priority of prompts based on the estimated emotions. For example, if the driver is tense, the prompt input unit may prioritize providing prompts that promote relaxation. It may also prioritize providing prompts that promote refreshment if the driver is tired. Furthermore, if the driver is agitated, it may prioritize providing prompts that help the driver regain composure. By prioritizing prompts according to the driver's emotions, prompts appropriate to the driver's state are provided preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the prompt input unit may be performed using AI, or not. For example, the prompt input unit can input driver emotion data into an AI, which can then generate appropriate prompts based on those emotions.

[0094] The prompt input unit can generate the optimal prompt when a prompt is entered, taking into account the driver's geographical location information. For example, the prompt input unit can provide an appropriate prompt by considering the traffic conditions at the current location. It can also provide an appropriate prompt by considering the weather conditions at the current location. Furthermore, it can provide an appropriate prompt by considering the road conditions at the current location. In this way, the optimal prompt is provided by considering geographical location information. Some or all of the above processing in the prompt input unit may be performed using AI, for example, or without AI. For example, the prompt input unit can input the driver's geographical location information to the AI, and the AI ​​can generate the optimal prompt based on that information.

[0095] The prompt input unit can analyze the driver's social media activity and generate relevant prompts when a prompt is entered. For example, the prompt input unit can provide appropriate prompts based on information shared by the driver on social media. The prompt input unit can also analyze the driver's social media activity history and provide relevant prompts. Furthermore, the prompt input unit can provide appropriate prompts based on information about accounts the driver follows on social media. In this way, relevant prompts are provided by analyzing social media activity. Some or all of the above processing in the prompt input unit may be performed using AI, for example, or without AI. For example, the prompt input unit can input the driver's social media activity into AI, which can then analyze that activity and generate relevant prompts.

[0096] The response determination unit can estimate the driver's emotions and adjust the content of the response based on the estimated emotions of the driver. For example, if the driver is tense, the response determination unit can provide a response that has a relaxing effect. It can also provide a response that has a refreshing effect if the driver is tired. Furthermore, if the driver is excited, the response determination unit can provide a response that helps the driver regain composure. In this way, by providing a response that corresponds to the driver's emotions, a response appropriate to the driver's state is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the response determination unit may be performed using AI, for example, or without using AI. For example, the response determination unit can input driver emotion data into an AI, and the AI ​​can generate an appropriate response based on that emotion.

[0097] The response decision unit can select the optimal response by referring to past response history when making a response decision. For example, the response decision unit may prioritize selecting responses that were taken in similar situations in the past. The response decision unit can also select responses taken at specific times or locations from past response history. Furthermore, the response decision unit can analyze past response history and select the most effective response. In this way, the optimal response is provided by referring to past response history. Some or all of the above processing in the response decision unit may be performed using AI, for example, or without using AI. For example, the response decision unit can input past response history into AI, which can then analyze that history and select the optimal response.

[0098] The response decision unit can analyze the current driving situation in real time when making a response decision and select the optimal response. For example, the response decision unit can analyze the current traffic situation in real time and select an appropriate response. It can also analyze the current weather conditions in real time and select an appropriate response. Furthermore, it can analyze the current road conditions in real time and select an appropriate response. In this way, the optimal response is provided by analyzing the current driving situation in real time. Some or all of the above processing in the response decision unit may be performed using AI, for example, or without using AI. For example, the response decision unit can input the current driving situation into AI, which can analyze the situation in real time and select the optimal response.

[0099] The response determination unit can estimate the driver's emotions and determine the priority of responses based on the estimated emotions. For example, if the driver is tense, the response determination unit may prioritize providing responses that have a relaxing effect. It may also prioritize providing responses that have a refreshing effect if the driver is tired. Furthermore, if the driver is agitated, it may prioritize providing responses that help the driver regain composure. By determining the priority of responses according to the driver's emotions, responses appropriate to the driver's state are provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response determination unit may be performed using AI, or not. For example, the response determination unit can input driver emotion data into an AI, which can then generate an appropriate response based on those emotions.

[0100] The response decision unit can select the optimal response by considering the driver's geographical location information when making a response decision. For example, the response decision unit can select an appropriate response by considering the traffic conditions at the current location. It can also select an appropriate response by considering the weather conditions at the current location. Furthermore, it can also select an appropriate response by considering the road conditions at the current location. In this way, the optimal response is provided by considering geographical location information. Some or all of the above processing in the response decision unit may be performed using AI, for example, or without using AI. For example, the response decision unit can input the driver's geographical location information into the AI, and the AI ​​can select the optimal response based on that information.

[0101] The response determination unit can analyze the driver's social media activity and select a relevant response when making a response decision. For example, the response determination unit can select an appropriate response based on information shared by the driver on social media. The response determination unit can also analyze the driver's social media activity history and select a relevant response. Furthermore, the response determination unit can select an appropriate response based on information about accounts the driver follows on social media. In this way, relevant responses are provided by analyzing social media activity. Some or all of the above processing in the response determination unit may be performed using AI, for example, or without AI. For example, the response determination unit can input the driver's social media activity into AI, which can then analyze that activity and generate a relevant response.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] An autonomous driving system can estimate the driver's emotions and provide feedback to the driver based on the estimated emotions. For example, if the driver is tense, the system can suggest breathing exercises or music to help them relax. If the driver is tired, the system can encourage them to take a break. Furthermore, if the driver is agitated, the system can provide advice to help them calm down. By providing feedback tailored to the driver's emotions, the system can optimize the driver's state and support safe driving. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback provision unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provision unit can input driver emotion data into a generative AI, which can then generate appropriate feedback based on those emotions.

[0104] An autonomous driving system can analyze a driver's past driving history and provide optimal driving assistance based on the driver's driving style. For example, a driver who frequently uses sudden braking in the past can receive assistance to prompt deceleration in advance. Similarly, a driver who frequently drives on highways can receive assistance to maintain an optimal following distance on highways. Furthermore, a driver who frequently drives at night can receive assistance to improve nighttime visibility. By providing assistance tailored to the driver's driving style, both driving safety and comfort can be improved. The analysis of driving history may be performed using AI, or it may be performed without AI. For example, driving history data can be input into an AI, which then analyzes the data and generates optimal driving assistance.

[0105] The autonomous driving system can estimate the driver's emotions and monitor the driver's stress level based on the estimated emotions. For example, if the driver is experiencing high stress, the system can prompt the driver to take a break. Conversely, if the driver is at a low stress level, the system can recommend that the driver continue driving. Furthermore, if the driver's stress level changes rapidly, the system can identify the cause and suggest appropriate countermeasures. In this way, monitoring the driver's stress level can support the driver's health and safety. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the stress monitoring unit may be performed using, for example, generative AI, or not using generative AI. For example, the stress monitoring unit can input the driver's emotion data into the generative AI, which can then perform appropriate monitoring based on those emotions.

[0106] An autonomous driving system can estimate the driver's emotions and evaluate the driver's concentration based on the estimated emotions. For example, if the driver is focused, the system can recommend that the driver continue driving. If the driver is not focused, the system can prompt the driver to take a break. Furthermore, if the driver's concentration drops sharply, the system can identify the cause and suggest appropriate countermeasures. In this way, evaluating the driver's concentration can improve driving safety. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the concentration evaluation unit may be performed using a generative AI, or not using a generative AI. For example, the concentration evaluation unit can input the driver's emotion data into a generative AI, which can then perform an appropriate evaluation based on that emotion.

[0107] An autonomous driving system can estimate the driver's emotions and adjust the driver's driving style based on those emotions. For example, if the driver is tense, the system can adjust the driving to be calmer. If the driver is relaxed, the system can make the driving more dynamic. Furthermore, if the driver is tired, the system can reduce speed to make driving safer. This improves driving safety and comfort by providing a driving style that responds to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the driving style adjustment unit may be performed using a generative AI, or not. For example, the driving style adjustment unit can input driver emotion data into a generative AI, which can then generate an appropriate driving style based on those emotions.

[0108] An autonomous driving system can refer to the driver's past driving history and suggest the optimal route based on the driver's driving patterns. For example, it can prioritize suggesting a route to a driver who has frequently used a particular route in the past. It can also suggest a route that avoids congestion to a driver who has tended to avoid it in the past. Furthermore, it can suggest a route that utilizes highways to a driver who has preferred to use highways in the past. By suggesting the optimal route according to the driver's driving patterns, it can improve the efficiency and comfort of driving. Referencing driving history may be done using AI, or it may not require AI. For example, driving history data can be input into an AI, which can then analyze the data and generate the optimal route.

[0109] An autonomous driving system can analyze the driver's current driving situation in real time and provide optimal driving assistance. For example, it can analyze current traffic conditions in real time and provide assistance to avoid congestion. It can also analyze current weather conditions in real time and provide driving assistance in adverse weather conditions. Furthermore, it can analyze current road conditions in real time and provide assistance to avoid road construction and accidents. In this way, optimal driving assistance is provided by analyzing the current driving situation in real time. The real-time analysis of driving conditions may be performed using AI, for example, or without AI. For example, driving condition data can be input into an AI, which can analyze that data in real time and generate optimal driving assistance.

[0110] An autonomous driving system can provide optimal driving assistance by taking into account the driver's geographical location. For example, it can provide assistance to avoid congestion by considering the current traffic conditions. It can also provide driving assistance in adverse weather conditions by considering the current weather conditions. Furthermore, it can provide assistance to avoid road construction or accidents by considering the current road conditions. In this way, optimal driving assistance is provided by taking geographical location into account. This consideration of geographical location may be performed using AI, or it may be performed without using AI. For example, geographical location data can be input into an AI, which can then analyze the data and generate optimal driving assistance.

[0111] Autonomous driving systems can analyze a driver's social media activity and provide optimal driving assistance based on the driver's interests. For example, they can suggest routes to places of interest based on information the driver has shared on social media. They can also analyze the driver's social media activity history and provide relevant driving assistance. Furthermore, they can suggest routes to events and places of interest based on information about accounts the driver follows on social media. In this way, by analyzing social media activity, driving assistance is provided based on the driver's interests. The analysis of social media activity may be performed using AI or not. For example, social media activity data can be input into an AI, which can analyze the data and generate optimal driving assistance.

[0112] An autonomous driving system can estimate the driver's emotions and evaluate the driver's driving performance based on the estimated emotions. For example, if the driver is tense, the system can evaluate that the driving performance is degraded. Conversely, if the driver is relaxed, the system can evaluate that the driving performance is improved. Furthermore, if the driver is tired, the system can also evaluate that the driving performance is degraded. This allows for improved driving safety and efficiency by evaluating driving performance in accordance with the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the driving performance evaluation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the driving performance evaluation unit can input driver emotion data into a generative AI, which can then perform an appropriate evaluation based on that emotion.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The image generation unit generates images for irregular events. For example, it uses a generation AI to generate images that reproduce irregular situations not included in normal datasets. Specifically, it generates images that reproduce situations such as animals suddenly jumping out onto the road or driving conditions under abnormal weather conditions (driving in heavy rain or snow, at night or in fog). Step 2: The dataset addition unit adds the images generated by the image generation unit to the dataset. For example, it integrates the generated images into an existing dataset to train the AI. It also classifies the generated images and adds them to appropriate categories such as traffic accidents, natural disasters, and machine failures. Step 3: The prompt input section takes the driving situation as a prompt. For example, it uses speech recognition technology to input irregular situations that occur while driving as prompts to the AI ​​in real time. Specifically, it inputs situations such as "There is an obstacle ahead," "The road is slippery," or "Visibility is poor" by voice. Step 4: The response decision unit determines the appropriate response based on the situation input by the prompt input unit. For example, it uses a generated AI to determine the appropriate response based on the input situation and instructs the vehicle to take evasive action if there is an obstacle ahead, to slow down if the road is slippery, and to turn on the lights if visibility is poor.

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0118] Each of the multiple elements described above, including the image generation unit, dataset addition unit, prompt input unit, and correspondence determination unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the image generation unit is implemented by the control unit 46A of the smart device 14 and generates an image that reproduces an irregular situation using generation AI. The dataset addition unit is implemented by the identification processing unit 290 of the data processing device 12 and adds the generated image to the dataset. The prompt input unit is implemented by the control unit 46A of the smart device 14 and inputs the situation during operation as a prompt using speech recognition technology. The correspondence determination unit is implemented by the identification processing unit 290 of the data processing device 12 and determines an appropriate response based on the input situation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 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.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the image generation unit, dataset addition unit, prompt input unit, and correspondence determination unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the image generation unit is implemented by the control unit 46A of the smart glasses 214 and generates an image that reproduces an irregular situation using generation AI. The dataset addition unit is implemented by the identification processing unit 290 of the data processing device 12 and adds the generated image to the dataset. The prompt input unit is implemented by the control unit 46A of the smart glasses 214 and inputs the driving situation as a prompt using voice recognition technology. The correspondence determination unit is implemented by the identification processing unit 290 of the data processing device 12 and determines an appropriate response based on the input situation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the image generation unit, dataset addition unit, prompt input unit, and response determination unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the image generation unit is implemented by the control unit 46A of the headset terminal 314 and generates an image that reproduces an irregular situation using generation AI. The dataset addition unit is implemented by the identification processing unit 290 of the data processing device 12 and adds the generated image to the dataset. The prompt input unit is implemented by the control unit 46A of the headset terminal 314 and inputs the driving situation as a prompt using speech recognition technology. The response determination unit is implemented by the identification processing unit 290 of the data processing device 12 and determines an appropriate response based on the input situation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 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.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the image generation unit, dataset addition unit, prompt input unit, and correspondence determination unit, is implemented in at least one of the robot 414 and the data processing device 12. For example, the image generation unit is implemented by the control unit 46A of the robot 414 and generates an image that reproduces an irregular situation using generation AI. The dataset addition unit is implemented by the identification processing unit 290 of the data processing device 12 and adds the generated image to the dataset. The prompt input unit is implemented by the control unit 46A of the robot 414 and inputs the situation during operation as a prompt using speech recognition technology. The correspondence determination unit is implemented by the identification processing unit 290 of the data processing device 12 and determines an appropriate response based on the input situation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) An image generation unit that generates images for irregular events, A dataset addition unit adds images generated by the image generation unit to a dataset, A prompt input section for inputting the driving situation as prompts, The system includes a response determination unit that determines an appropriate response based on the situation entered by the prompt input unit. A system characterized by the following features. (Note 2) It includes a generation AI unit that generates images using generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a voice recognition unit that uses voice recognition technology to input the situation. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a dataset management unit that adds the generated images to the dataset. The system described in Appendix 1, characterized by the features described herein. (Note 5) It is equipped with an AI unit that determines the appropriate response based on the driving situation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The image generation unit, Generate images that reproduce irregular situations not included in normal datasets. The system described in Appendix 1, characterized by the features described herein. (Note 7) The prompt input unit is, Irregular situations that occur while driving are input into the AI ​​in real time as prompts generated using voice recognition technology. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned correspondence determination unit, The generating AI determines the appropriate response based on the input situation. The system described in Appendix 1, characterized by the features described herein. (Note 9) The image generation unit, It estimates the driver's emotions and adjusts the content of the generated images based on the estimated emotions of the driver. The system described in Appendix 1, characterized by the features described herein. (Note 10) The image generation unit, By including visual information from different viewpoints and angles in the generated images, a wider variety of irregular events can be reproduced. The system described in Appendix 1, characterized by the features described herein. (Note 11) The image generation unit, By reflecting different time periods and seasonal changes in the generated images, a more realistic scenario can be provided. The system described in Appendix 1, characterized by the features described herein. (Note 12) The image generation unit, The system estimates the driver's emotions and determines the priority of images to generate based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The image generation unit, By reflecting different traffic conditions and road conditions in the generated images, a wider variety of scenarios can be provided. The system described in Appendix 1, characterized by the features described herein. (Note 14) The image generation unit, By reflecting different weather conditions in the generated images, we can provide more realistic scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned dataset addition unit, The system estimates the driver's emotions and selects images to add to the dataset based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned dataset addition unit, Add a feature to automatically generate and manage image metadata when adding images to a dataset. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned dataset addition unit, Add a feature to evaluate the quality of images added to the dataset and automatically exclude low-quality images. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned dataset addition unit, The system estimates the driver's emotions and determines the priority of images to add to the dataset based on the estimated driver's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned dataset addition unit, Add a feature to evaluate image relevance when adding images to a dataset and prioritize adding images with high relevance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned dataset addition unit, Add a feature to evaluate image diversity when adding to a dataset and prioritize adding images with high diversity. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prompt input unit is, The system estimates the driver's emotions and adjusts the content of the prompts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prompt input unit is, When prompting, the system generates the most appropriate prompt by referencing the driver's past driving history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prompt input unit is, When prompt input is received, the system analyzes the driver's current driving situation in real time and generates the optimal prompt. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prompt input unit is, The system estimates the driver's emotions and prioritizes prompts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prompt input unit is, When prompting, the system generates the optimal prompt considering the driver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prompt input unit is, When prompting, the system analyzes the driver's social media activity and generates relevant prompts. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned correspondence determination unit, The system estimates the driver's emotions and adjusts the response based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned correspondence determination unit, When deciding on a course of action, the system will refer to past response history to select the most appropriate course of action. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned correspondence determination unit, When making a decision on how to respond, the system analyzes the current driving situation in real time and selects the optimal response. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned correspondence determination unit, The system estimates the driver's emotions and determines the priority of responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned correspondence determination unit, When making a decision on how to respond, the driver's geographical location information will be taken into consideration to select the most appropriate response. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned correspondence determination unit, When deciding on a course of action, the driver's social media activity will be analyzed, and the appropriate response will be selected. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An image generation unit that generates images for irregular events, A dataset addition unit adds images generated by the image generation unit to a dataset, A prompt input section for inputting the driving situation as prompts, The system includes a response determination unit that determines an appropriate response based on the situation entered by the prompt input unit. A system characterized by the following features.

2. It includes a generation AI unit that generates images using generation AI. The system according to feature 1.

3. It is equipped with a voice recognition unit that uses voice recognition technology to input the situation. The system according to feature 1.

4. It includes a dataset management unit that adds the generated images to the dataset. The system according to feature 1.

5. It is equipped with an AI unit that determines the appropriate response based on the driving situation. The system according to feature 1.

6. The image generation unit, Generate images that reproduce irregular situations not included in normal datasets. The system according to feature 1.

7. The prompt input unit is, Irregular situations that occur while driving are generated in real time as prompts using voice recognition technology and input into the AI. The system according to feature 1.

8. The aforementioned correspondence determination unit, The generating AI determines the appropriate response based on the input situation. The system according to feature 1.

Images