System

Abstract

An object of a system according to an embodiment is to provide a sense of security to a user by visualizing a future operation of an autonomous driving vehicle.SOLUTION: A system includes an analysis part, a visualization part, a feedback reception part, and an improvement part. The analysis unit analyzes a control content of the autonomous driving. The visualization unit visualizes a future operation based on the information analyzed by the analysis unit. The feedback receiving unit receives feedback from the user. The improvement unit improves the analysis result based on the feedback received by the feedback reception unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult for users to understand the future behavior of self-driving cars, and there is room for improvement in providing a sense of security.

[0005] The system according to the embodiment aims to visualize the future operation of an autonomous vehicle and provide a sense of security to users. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a visualization unit, a feedback receiving unit, and an improvement unit. The analysis unit analyzes the control content of the autonomous driving. The visualization unit visualizes future operations based on the information analyzed by the analysis unit. The feedback receiving unit receives user feedback. The improvement unit improves the analysis results based on the feedback received by the feedback receiving unit. [Effects of the Invention]

[0007] The system according to the embodiment can visualize the future operation of an autonomous vehicle and provide a sense of security to users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention analyzes autonomous driving control content, visualizes future actions in a user-understandable manner, receives user feedback, and improves the analysis results. In this system, when a user gets into an autonomous vehicle, a generating AI analyzes the autonomous driving control content in real time and visualizes future actions in a user-understandable manner based on the analyzed information. For example, the system displays the next action and route as an animation on an in-vehicle display. The generating AI can also receive user feedback and improve the analysis results. This allows users to visually understand the vehicle's future actions and feel more secure. This system not only improves the safety of autonomous vehicles but also the user's sense of security. For example, when a user gets into an autonomous vehicle, they can know in advance what action the vehicle will take next, allowing them to ride without feeling anxious. Furthermore, the generating AI receives user feedback and improves the analysis results, thereby continuously improving the user's sense of security. This is expected to promote the widespread use of autonomous vehicles.

[0029] An autonomous driving system according to an embodiment includes an analysis unit, a visualization unit, a feedback receiving unit, and an improvement unit. The analysis unit analyzes autonomous driving control content. The analysis unit analyzes information such as, for example, what action the vehicle will take next and what route it will take. The analysis unit can also analyze autonomous driving control content in real time using a generation AI. For example, the generation AI predicts the next action and route based on sensor data and map information from the vehicle. The visualization unit visualizes future actions based on the information analyzed by the analysis unit. For example, the visualization unit displays the next action and route as an animation on an in-vehicle display. The visualization unit can also visualize the future actions using the generation AI in a way that is understandable to the user. For example, the generation AI displays the next action and route as an animation based on the analysis results. The feedback receiving unit receives user feedback. For example, the feedback receiving unit accepts feedback input by the user via a touch panel or by voice. The feedback receiving unit can also analyze the user feedback using the generation AI. For example, the generation AI extracts information for improving the analysis results based on user feedback. The improvement unit improves the analysis results based on the feedback received by the feedback receiving unit. The improvement unit, for example, reflects the user feedback in subsequent analyses. The improvement unit can also improve the analysis results using the generation AI. For example, the generation AI adjusts the analysis algorithm based on user feedback. As a result, the autonomous driving system according to the embodiment can visualize future autonomous driving operations and improve the analysis results based on feedback in order to increase the user's sense of security.

[0030] The analysis unit can analyze information about what action the vehicle will take next and what route it will take. For example, the analysis unit analyzes actions such as accelerating, decelerating, and changing direction of the vehicle. The analysis unit can also analyze the shortest route to a destination and a route that takes traffic conditions into consideration. For example, the analysis unit can predict the vehicle's next action based on sensor data. The analysis unit can also calculate the optimal route based on map information. This allows the user to predict future actions by analyzing the vehicle's next action and route. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the vehicle's sensor data into the generation AI and have the generation AI predict the vehicle's next action and route.

[0031] The visualization unit can display the next action or route as an animation on the in-vehicle display based on the analyzed information. The visualization unit can, for example, display actions such as accelerating, decelerating, and changing direction of the vehicle as an animation. The visualization unit can also display the shortest route to a destination or a route that takes traffic conditions into consideration as an animation. For example, the visualization unit can display the next action as an animation on the in-vehicle display. The visualization unit can also display the next route as an animation on the in-vehicle display. In this way, by displaying the next action or route as an animation on the in-vehicle display, the user can visually understand future actions. Some or all of the above-mentioned processing in the visualization unit may be performed using or without the generation AI. For example, the visualization unit can input the analyzed information to the generation AI and cause the generation AI to generate an animation.

[0032] The feedback accepting unit can accept a method in which the user inputs feedback using a touch panel or a method in which the user provides feedback by voice. For example, the feedback accepting unit accepts a method in which the user inputs feedback using a touch panel. The feedback accepting unit can also accept a method in which the user provides feedback by voice using voice recognition technology. For example, the feedback accepting unit adjusts the size and sensitivity of the touch panel to make it easier for the user to input feedback. The feedback accepting unit can also improve the accuracy of voice recognition to make it easier for the user to provide feedback by voice. This makes it easier to collect feedback by accepting a method in which the user provides feedback using a touch panel or voice. Some or all of the above-described processing in the feedback accepting unit may be performed using or without the generation AI. For example, the feedback accepting unit can input the user's voice data to the generation AI and cause the generation AI to perform voice recognition.

[0033] The improvement unit can reflect the user feedback in subsequent analyses based on the user's feedback. For example, the improvement unit adjusts the analysis algorithm based on the user's feedback. The improvement unit can also improve the analysis results based on the user's feedback. For example, the improvement unit analyzes the user's feedback and reflects it in subsequent analyses. The improvement unit can also adjust the analysis algorithm based on the user's feedback using the generation AI. For example, the generation AI adjusts the analysis algorithm based on the user's feedback and improves the analysis results from subsequent analyses. This makes it possible to continuously improve the user's sense of security by improving the analysis results based on the user's feedback. Some or all of the above-mentioned processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input user feedback data into the generation AI and cause the generation AI to adjust the analysis algorithm.

[0034] The analysis unit can include not only the vehicle's behavior but also surrounding traffic conditions and weather information in its analysis. For example, the analysis unit analyzes real-time traffic congestion information and reflects it in the vehicle's behavior. The analysis unit can also analyze current weather information and adjust driving behavior in rainy weather. For example, the analysis unit analyzes the movement of surrounding vehicles and predicts behavior to avoid a collision. The analysis unit can also analyze surrounding traffic conditions and weather information using a generation AI. For example, the generation AI adjusts the vehicle's behavior based on traffic congestion information and weather information. By including surrounding traffic conditions and weather information in the analysis, more accurate predictions of future behavior can be made. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input traffic conditions and weather information into the generation AI and have the generation AI adjust the vehicle's behavior.

[0035] During analysis, the analysis unit can predict future behavior by referring to past driving data. For example, the analysis unit can predict behavior at a specific intersection based on past driving data. The analysis unit can also predict speed adjustments on a specific road by referring to past driving data. For example, the analysis unit can predict behavior under specific weather conditions based on past driving data. The analysis unit can also predict future behavior by referring to past driving data using a generation AI. For example, the generation AI predicts future behavior based on past driving data. In this way, future behavior can be predicted more accurately by referring to past driving data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past driving data into the generation AI and cause the generation AI to predict future behavior.

[0036] The analysis unit can integrate in-vehicle sensor data during analysis to improve the accuracy of the analysis. For example, the analysis unit can integrate in-vehicle camera data into the analysis to take the driver's state into consideration. The analysis unit can also integrate in-vehicle temperature sensor data into the analysis to maintain a comfortable driving environment. For example, the analysis unit can integrate in-vehicle voice data into the analysis to reflect the driver's instructions. The analysis unit can also use a generation AI to integrate in-vehicle sensor data to improve the accuracy of the analysis. For example, the generation AI can improve the accuracy of the analysis based on in-vehicle camera data and temperature sensor data. In this way, the integration of in-vehicle sensor data can improve the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input in-vehicle sensor data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0037] During analysis, the analysis unit can determine the analysis priority by taking into account the user's driving history. For example, the analysis unit prioritizes important analysis items based on the user's past driving history. The analysis unit can also prioritize analysis of specific driving patterns by taking into account the user's driving history. For example, the analysis unit prioritizes analysis of specific road conditions based on the user's driving history. The analysis unit can also determine the analysis priority by taking into account the user's driving history using a generation AI. For example, the generation AI determines the analysis priority based on the user's driving history. This allows important analysis items to be prioritized by taking into account the user's driving history. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's driving history data into the generation AI and have the generation AI determine the analysis priority.

[0038] The analysis unit can provide analysis results by taking into account vehicle maintenance information during analysis. The analysis unit, for example, adjusts the analysis results based on the vehicle maintenance information. The analysis unit can also analyze the status of specific parts by taking into account the vehicle maintenance information. For example, the analysis unit adjusts driving behavior based on the vehicle maintenance information. The analysis unit can also provide analysis results by taking into account the vehicle maintenance information using a generation AI. For example, the generation AI adjusts the analysis results based on the vehicle maintenance information. This makes it possible to provide more accurate analysis results by taking into account the vehicle maintenance information. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input vehicle maintenance information to the generation AI and have the generation AI adjust the analysis results.

[0039] During analysis, the analysis unit can customize the analysis algorithm based on the user's driving style. For example, the analysis unit analyzes the user's driving style and adjusts the analysis algorithm. The analysis unit can also prioritize analysis of specific driving actions based on the user's driving style. For example, the analysis unit customizes the analysis results taking the user's driving style into consideration. The analysis unit can also customize the analysis algorithm based on the user's driving style using a generation AI. For example, the generation AI adjusts the analysis algorithm based on the user's driving style. This allows customizing the analysis algorithm based on the user's driving style to provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's driving style data into the generation AI and have the generation AI customize the analysis algorithm.

[0040] The visualization unit can display not only future actions but also past action history during visualization. For example, the visualization unit displays past driving actions and compares them with future actions. The visualization unit can also predict future actions based on the past action history. For example, the visualization unit displays the past action history to encourage the user's understanding. The visualization unit can also use the generation AI to display not only future actions but also the past action history. For example, the generation AI displays past driving actions and compares them with future actions. In this way, by also displaying the past action history, the user can better understand future actions. Some or all of the above-mentioned processing in the visualization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input past action history data into the generation AI and cause the generation AI to predict future actions.

[0041] The visualization unit can customize the display style according to the user's visual preferences during visualization. For example, the visualization unit changes the animation style according to the user's preferences. The visualization unit can also adjust the color and design based on the user's visual preferences. For example, the visualization unit suggests an optimal display style based on the user's past selection history. The visualization unit can also customize the display style according to the user's visual preferences using a generation AI. For example, the generation AI changes the animation style according to the user's preferences. This allows the display style to be customized according to the user's visual preferences, thereby providing more attractive information. Some or all of the above-described processing in the visualization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input the user's visual preference data into the generation AI and cause the generation AI to customize the display style.

[0042] When visualizing, the visualization unit can display the future operation not only on the in-car display but also on a smartphone or tablet. For example, the visualization unit can display the future operation on a smartphone in addition to the in-car display. The visualization unit can also display the future operation on a tablet so that the user can check it on multiple devices. For example, the visualization unit synchronizes information between the in-car display and the smartphone and tablet. The visualization unit can also use a generation AI to display not only on the in-car display but also on a smartphone or tablet. For example, the generation AI displays the future operation on a smartphone in addition to the in-car display. This allows the user to check the information on multiple devices by displaying it on a smartphone or tablet. Some or all of the above-mentioned processing in the visualization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input data from a smartphone or tablet to the generation AI and have the generation AI synchronize the display.

[0043] The visualization unit can customize the display content by taking into account the user's driving history when visualizing the data. The visualization unit, for example, adjusts the display content based on the user's past driving history. The visualization unit can also emphasize specific driving actions by taking into account the user's driving history. For example, the visualization unit displays specific road conditions based on the user's driving history. The visualization unit can also customize the display content by taking into account the user's driving history using a generation AI. For example, the generation AI adjusts the display content based on the user's driving history. This makes it possible to provide more appropriate display content by taking into account the user's driving history. Some or all of the above-described processing in the visualization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input the user's driving history data into the generation AI and have the generation AI customize the display content.

[0044] The visualization unit can include car maintenance information in the display when visualizing. For example, the visualization unit displays car maintenance information to inform the user of the car's condition. The visualization unit can also display the condition of specific parts based on the car maintenance information. For example, the visualization unit displays car maintenance information to prompt the user to confirm driving safety. The visualization unit can also include car maintenance information in the display using a generation AI. For example, the generation AI displays car maintenance information to inform the user of the car's condition. In this way, by including the car maintenance information in the display, the user can be informed of the car's condition. Some or all of the above-mentioned processing in the visualization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input car maintenance information to the generation AI and cause the generation AI to generate display content.

[0045] The visualization unit can adjust the display content based on the user's driving style during visualization. For example, the visualization unit analyzes the user's driving style and adjusts the display content. The visualization unit can also emphasize specific driving actions based on the user's driving style. For example, the visualization unit customizes the display content taking the user's driving style into consideration. The visualization unit can also adjust the display content based on the user's driving style using a generation AI. For example, the generation AI adjusts the display content based on the user's driving style. In this way, by adjusting the display content based on the user's driving style, more appropriate information can be provided. Some or all of the above-described processing in the visualization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input the user's driving style data to the generation AI and cause the generation AI to adjust the display content.

[0046] When receiving feedback, the feedback receiving unit can select the optimal receiving method by referring to the user's past feedback history. For example, the feedback receiving unit can suggest the optimal receiving method based on the user's past feedback history. The feedback receiving unit can also prioritize a specific feedback method by referring to the user's feedback history. For example, the feedback receiving unit can emphasize a specific feedback item based on the user's past feedback history. The feedback receiving unit can also select the optimal receiving method by referring to the user's past feedback history using a generation AI. For example, the generation AI can suggest the optimal receiving method based on the user's past feedback history. In this way, the optimal feedback receiving method can be provided by referring to the user's past feedback history. Some or all of the above-described processing in the feedback receiving unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback receiving unit can input the user's feedback history data into the generation AI and cause the generation AI to select the optimal receiving method.

[0047] The feedback receiving unit can customize the feedback receiving method according to the user's current situation when receiving feedback. For example, the feedback receiving unit analyzes the user's current situation and provides the optimal feedback method. The feedback receiving unit can also prioritize a specific feedback method taking the user's current situation into consideration. For example, the feedback receiving unit customizes feedback items based on the user's current situation. The feedback receiving unit can also customize the feedback receiving method according to the user's current situation using a generation AI. For example, the generation AI analyzes the user's current situation and provides the optimal feedback method. This allows more appropriate feedback to be collected by customizing the feedback receiving method according to the user's current situation. Some or all of the above-described processing in the feedback receiving unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback receiving unit can input data on the user's current situation into the generation AI and cause the generation AI to customize the feedback receiving method.

[0048] When receiving feedback, the feedback receiving unit can select the optimal receiving method by taking into account the user's geographical location information. The feedback receiving unit, for example, suggests the optimal feedback method based on the user's current location. The feedback receiving unit can also prioritize a specific feedback method by taking into account the user's geographical location information. For example, the feedback receiving unit customizes feedback items based on the user's current location. The feedback receiving unit can also select the optimal receiving method by using a generation AI by taking into account the user's geographical location information. For example, the generation AI suggests the optimal feedback method based on the user's current location. In this way, the optimal feedback receiving method can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback receiving unit may be performed by using the generation AI, or may be performed without using the generation AI. For example, the feedback receiving unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal receiving method.

[0049] When receiving feedback, the feedback receiving unit can analyze the user's social media activity to obtain relevant feedback. For example, the feedback receiving unit analyzes the user's social media activity and suggests relevant feedback items. The feedback receiving unit can also prioritize specific feedback items based on the content of the user's social media posts. For example, the feedback receiving unit can suggest relevant feedback items based on the activity of the user's friends on social media. The feedback receiving unit can also analyze the user's social media activity to obtain relevant feedback using a generation AI. For example, the generation AI analyzes the user's social media activity and suggests relevant feedback items. In this way, relevant feedback can be collected by analyzing the user's social media activity. Some or all of the above-described processing in the feedback receiving unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback receiving unit can input the user's social media data into the generation AI and cause the generation AI to obtain relevant feedback.

[0050] When making an improvement, the improvement unit can select the optimal improvement method by referring to the user's past feedback history. For example, the improvement unit can suggest the optimal improvement method based on the user's past feedback history. The improvement unit can also prioritize a specific improvement method by referring to the user's feedback history. For example, the improvement unit can emphasize a specific improvement item based on the user's past feedback history. The improvement unit can also select the optimal improvement method by referring to the user's past feedback history using the generation AI. For example, the generation AI can suggest the optimal improvement method based on the user's past feedback history. In this way, the optimal improvement method can be provided by referring to the user's past feedback history. Some or all of the above-described processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's feedback history data into the generation AI and have the generation AI select the optimal improvement method.

[0051] During improvement, the improvement unit can customize the improvement method according to the user's current situation. For example, the improvement unit analyzes the user's current situation and provides the optimal improvement method. The improvement unit can also prioritize a specific improvement method by taking the user's current situation into consideration. For example, the improvement unit customizes improvement items based on the user's current situation. The improvement unit can also customize the improvement method according to the user's current situation using a generation AI. For example, the generation AI analyzes the user's current situation and provides the optimal improvement method. This allows for more appropriate improvement by customizing the improvement method according to the user's current situation. Some or all of the above-described processing in the improvement unit may be performed using or without the generation AI. For example, the improvement unit can input data on the user's current situation into the generation AI and have the generation AI customize the improvement method.

[0052] During improvement, the improvement unit can adjust the analysis algorithm by reflecting user feedback. For example, the improvement unit adjusts the analysis algorithm based on user feedback. The improvement unit can also reflect user feedback and prioritize specific analysis items. For example, the improvement unit customizes the analysis results based on user feedback. The improvement unit can also adjust the analysis algorithm by reflecting user feedback using a generation AI. For example, the generation AI adjusts the analysis algorithm based on user feedback. This allows the analysis algorithm to be more appropriately adjusted by reflecting user feedback. Some or all of the above-mentioned processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input user feedback data into the generation AI and have the generation AI adjust the analysis algorithm.

[0053] When making an improvement, the improvement unit can select the optimal improvement method by taking into account the user's geographical location information. For example, the improvement unit can suggest the optimal improvement method based on the user's current location. The improvement unit can also prioritize a specific improvement method by taking into account the user's geographical location information. For example, the improvement unit customizes improvement items based on the user's current location. The improvement unit can also use a generation AI to select the optimal improvement method by taking into account the user's geographical location information. For example, the generation AI suggests the optimal improvement method based on the user's current location. This makes it possible to provide the optimal improvement method by taking into account the user's geographical location information. Some or all of the above-described processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal improvement method.

[0054] When making an improvement, the improvement unit can analyze the user's social media activity and suggest relevant improvement methods. For example, the improvement unit analyzes the user's social media activity and suggests relevant improvement items. The improvement unit can also prioritize specific improvement items based on the content of the user's social media posts. For example, the improvement unit can suggest relevant improvement items based on the activity of the user's friends on social media. The improvement unit can also analyze the user's social media activity and suggest relevant improvement methods using a generation AI. For example, the generation AI analyzes the user's social media activity and suggests relevant improvement items. In this way, relevant improvement methods can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's social media data into the generation AI and have the generation AI execute the suggestion of relevant improvement methods.

[0055] When making an improvement, the improvement unit can customize the improvement method based on the user's driving style. For example, the improvement unit analyzes the user's driving style and adjusts the improvement method. The improvement unit can also prioritize improving specific driving actions based on the user's driving style. For example, the improvement unit customizes the improvement method taking the user's driving style into consideration. The improvement unit can also customize the improvement method based on the user's driving style using a generation AI. For example, the generation AI adjusts the improvement method based on the user's driving style. This allows for more appropriate improvement by customizing the improvement method based on the user's driving style. Some or all of the above-described processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's driving style data into the generation AI and have the generation AI customize the improvement method.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The analysis unit can analyze the user's driving style and customize the analysis results. For example, the analysis unit can analyze the user's past driving style and provide analysis results based on that style the next time the user drives. The analysis unit can also emphasize specific driving actions depending on the user's driving style. For example, for a user who frequently brakes suddenly, detailed information about the timing and strength of braking can be provided. This allows for more appropriate driving assistance by providing analysis results that are tailored to the user's driving style.

[0058] The feedback receiving unit can analyze user feedback in real time and immediately make suggestions for improvement. For example, if a user feels anxious while driving, the feedback can be immediately analyzed and suggestions can be made to reduce that anxiety the next time the user drives. Also, if a user is dissatisfied with a particular function, the feedback can be used to suggest improvements to the function. Furthermore, if a user experiences difficulty in a particular driving situation, driving assistance appropriate to that situation can be suggested. This allows for faster response by analyzing user feedback in real time and immediately making suggestions for improvement.

[0059] The analysis unit can integrate in-vehicle sensor data to improve the accuracy of the analysis. For example, in-vehicle camera data can be integrated into the analysis to take the driver's condition into account. In-vehicle temperature sensor data can also be integrated into the analysis to maintain a comfortable driving environment. Furthermore, in-vehicle voice data can be integrated into the analysis to reflect the driver's instructions. In this way, by integrating in-vehicle sensor data, the accuracy of the analysis can be improved.

[0060] The feedback receiving unit can reflect the user's feedback in subsequent analyses. For example, if the user is dissatisfied with a particular driving behavior, the analysis algorithm can be adjusted based on that feedback. Also, if the user gives a high rating to a particular function, an analysis can be performed to enhance that function. Furthermore, if the user experiences difficulty in a particular driving situation, an analysis can be performed that is tailored to that situation. This allows for more appropriate driving assistance by improving the analysis results based on user feedback.

[0061] The analysis unit can predict future behavior by referring to past driving data. For example, it can predict behavior at a specific intersection based on past driving data. It can also predict speed adjustments on a specific road based on past driving data. It can also predict behavior under specific weather conditions based on past driving data. Thus, by referring to past driving data, it is possible to more accurately predict future behavior.

[0062] The analysis unit can include surrounding traffic conditions and weather information in the analysis. For example, it can analyze real-time traffic congestion information and reflect it in the vehicle's behavior. It can also analyze current weather information and adjust driving behavior in rainy weather. It can also analyze the movements of surrounding vehicles and predict actions to avoid collisions. By including surrounding traffic conditions and weather information in the analysis, it is possible to predict future behavior more accurately.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The analysis unit analyzes the autonomous driving control content. For example, the analysis unit analyzes information such as what action the vehicle will take next and what route it will take. The analysis unit can also use the generation AI to analyze the autonomous driving control content in real time. For example, the generation AI predicts the next action and route based on the vehicle's sensor data and map information. Step 2: The visualization unit visualizes future actions based on the information analyzed by the analysis unit. For example, the visualization unit displays the next action or route as an animation on an in-car display. The visualization unit can also use a generative AI to visualize future actions in a way that is understandable to the user. For example, the generative AI displays the next action or route as an animation based on the analysis results. Step 3: The feedback receiving unit receives the user's feedback. For example, the feedback receiving unit receives feedback by the user inputting it on a touch panel or by providing it by voice. The feedback receiving unit can also analyze the user's feedback using a generation AI. For example, the generation AI extracts information to improve the analysis results based on the user's feedback. Step 4: The improvement unit improves the analysis results based on the feedback received by the feedback receiving unit. For example, the improvement unit reflects the user's feedback in subsequent analyses. The improvement unit can also use the generation AI to improve the analysis results. For example, the generation AI adjusts the analysis algorithm based on the user's feedback.

[0065] (Example 2) A system according to an embodiment of the present invention analyzes autonomous driving control content, visualizes future actions in a user-understandable manner, receives user feedback, and improves the analysis results. In this system, when a user gets into an autonomous vehicle, a generating AI analyzes the autonomous driving control content in real time and visualizes future actions in a user-understandable manner based on the analyzed information. For example, the system displays the next action and route as an animation on an in-vehicle display. The generating AI can also receive user feedback and improve the analysis results. This allows users to visually understand the vehicle's future actions and feel more secure. This system not only improves the safety of autonomous vehicles but also the user's sense of security. For example, when a user gets into an autonomous vehicle, they can know in advance what action the vehicle will take next, allowing them to ride without feeling anxious. Furthermore, the generating AI receives user feedback and improves the analysis results, thereby continuously improving the user's sense of security. This is expected to promote the widespread use of autonomous vehicles.

[0066] An autonomous driving system according to an embodiment includes an analysis unit, a visualization unit, a feedback receiving unit, and an improvement unit. The analysis unit analyzes autonomous driving control content. The analysis unit analyzes information such as, for example, what action the vehicle will take next and what route it will take. The analysis unit can also analyze autonomous driving control content in real time using a generation AI. For example, the generation AI predicts the next action and route based on sensor data and map information from the vehicle. The visualization unit visualizes future actions based on the information analyzed by the analysis unit. For example, the visualization unit displays the next action and route as an animation on an in-vehicle display. The visualization unit can also visualize the future actions using the generation AI in a way that is understandable to the user. For example, the generation AI displays the next action and route as an animation based on the analysis results. The feedback receiving unit receives user feedback. For example, the feedback receiving unit accepts feedback input by the user via a touch panel or by voice. The feedback receiving unit can also analyze the user feedback using the generation AI. For example, the generation AI extracts information for improving the analysis results based on user feedback. The improvement unit improves the analysis results based on the feedback received by the feedback receiving unit. The improvement unit, for example, reflects the user feedback in subsequent analyses. The improvement unit can also improve the analysis results using the generation AI. For example, the generation AI adjusts the analysis algorithm based on user feedback. As a result, the autonomous driving system according to the embodiment can visualize future autonomous driving operations and improve the analysis results based on feedback in order to increase the user's sense of security.

[0067] The analysis unit can analyze information about what action the vehicle will take next and what route it will take. For example, the analysis unit analyzes actions such as accelerating, decelerating, and changing direction of the vehicle. The analysis unit can also analyze the shortest route to a destination and a route that takes traffic conditions into consideration. For example, the analysis unit can predict the vehicle's next action based on sensor data. The analysis unit can also calculate the optimal route based on map information. This allows the user to predict future actions by analyzing the vehicle's next action and route. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the vehicle's sensor data into the generation AI and have the generation AI predict the vehicle's next action and route.

[0068] The visualization unit can display the next action or route as an animation on the in-vehicle display based on the analyzed information. The visualization unit can, for example, display actions such as accelerating, decelerating, and changing direction of the vehicle as an animation. The visualization unit can also display the shortest route to a destination or a route that takes traffic conditions into consideration as an animation. For example, the visualization unit can display the next action as an animation on the in-vehicle display. The visualization unit can also display the next route as an animation on the in-vehicle display. In this way, by displaying the next action or route as an animation on the in-vehicle display, the user can visually understand future actions. Some or all of the above-mentioned processing in the visualization unit may be performed using or without the generation AI. For example, the visualization unit can input the analyzed information to the generation AI and cause the generation AI to generate an animation.

[0069] The feedback accepting unit can accept a method in which the user inputs feedback using a touch panel or a method in which the user provides feedback by voice. For example, the feedback accepting unit accepts a method in which the user inputs feedback using a touch panel. The feedback accepting unit can also accept a method in which the user provides feedback by voice using voice recognition technology. For example, the feedback accepting unit adjusts the size and sensitivity of the touch panel to make it easier for the user to input feedback. The feedback accepting unit can also improve the accuracy of voice recognition to make it easier for the user to provide feedback by voice. This makes it easier to collect feedback by accepting a method in which the user provides feedback using a touch panel or voice. Some or all of the above-described processing in the feedback accepting unit may be performed using or without the generation AI. For example, the feedback accepting unit can input the user's voice data to the generation AI and cause the generation AI to perform voice recognition.

[0070] The improvement unit can reflect the user feedback in subsequent analyses based on the user's feedback. For example, the improvement unit adjusts the analysis algorithm based on the user's feedback. The improvement unit can also improve the analysis results based on the user's feedback. For example, the improvement unit analyzes the user's feedback and reflects it in subsequent analyses. The improvement unit can also adjust the analysis algorithm based on the user's feedback using the generation AI. For example, the generation AI adjusts the analysis algorithm based on the user's feedback and improves the analysis results from subsequent analyses. This makes it possible to continuously improve the user's sense of security by improving the analysis results based on the user's feedback. Some or all of the above-mentioned processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input user feedback data into the generation AI and cause the generation AI to adjust the analysis algorithm.

[0071] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the accuracy of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the accuracy of the analysis.

[0072] The analysis unit can include not only the vehicle's behavior but also surrounding traffic conditions and weather information in its analysis. For example, the analysis unit analyzes real-time traffic congestion information and reflects it in the vehicle's behavior. The analysis unit can also analyze current weather information and adjust driving behavior in rainy weather. For example, the analysis unit analyzes the movement of surrounding vehicles and predicts behavior to avoid a collision. The analysis unit can also analyze surrounding traffic conditions and weather information using a generation AI. For example, the generation AI adjusts the vehicle's behavior based on traffic congestion information and weather information. By including surrounding traffic conditions and weather information in the analysis, more accurate predictions of future behavior can be made. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input traffic conditions and weather information into the generation AI and have the generation AI adjust the vehicle's behavior.

[0073] During analysis, the analysis unit can predict future behavior by referring to past driving data. For example, the analysis unit can predict behavior at a specific intersection based on past driving data. The analysis unit can also predict speed adjustments on a specific road by referring to past driving data. For example, the analysis unit can predict behavior under specific weather conditions based on past driving data. The analysis unit can also predict future behavior by referring to past driving data using a generation AI. For example, the generation AI predicts future behavior based on past driving data. In this way, future behavior can be predicted more accurately by referring to past driving data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past driving data into the generation AI and cause the generation AI to predict future behavior.

[0074] The analysis unit can integrate in-vehicle sensor data during analysis to improve the accuracy of the analysis. For example, the analysis unit can integrate in-vehicle camera data into the analysis to take the driver's state into consideration. The analysis unit can also integrate in-vehicle temperature sensor data into the analysis to maintain a comfortable driving environment. For example, the analysis unit can integrate in-vehicle voice data into the analysis to reflect the driver's instructions. The analysis unit can also use a generation AI to integrate in-vehicle sensor data to improve the accuracy of the analysis. For example, the generation AI can improve the accuracy of the analysis based on in-vehicle camera data and temperature sensor data. In this way, the integration of in-vehicle sensor data can improve the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input in-vehicle sensor data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0076] During analysis, the analysis unit can determine the analysis priority by taking into account the user's driving history. For example, the analysis unit prioritizes important analysis items based on the user's past driving history. The analysis unit can also prioritize analysis of specific driving patterns by taking into account the user's driving history. For example, the analysis unit prioritizes analysis of specific road conditions based on the user's driving history. The analysis unit can also determine the analysis priority by taking into account the user's driving history using a generation AI. For example, the generation AI determines the analysis priority based on the user's driving history. This allows important analysis items to be prioritized by taking into account the user's driving history. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's driving history data into the generation AI and have the generation AI determine the analysis priority.

[0077] The analysis unit can provide analysis results by taking into account vehicle maintenance information during analysis. The analysis unit, for example, adjusts the analysis results based on the vehicle maintenance information. The analysis unit can also analyze the status of specific parts by taking into account the vehicle maintenance information. For example, the analysis unit adjusts driving behavior based on the vehicle maintenance information. The analysis unit can also provide analysis results by taking into account the vehicle maintenance information using a generation AI. For example, the generation AI adjusts the analysis results based on the vehicle maintenance information. This makes it possible to provide more accurate analysis results by taking into account the vehicle maintenance information. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input vehicle maintenance information to the generation AI and have the generation AI adjust the analysis results.

[0078] During analysis, the analysis unit can customize the analysis algorithm based on the user's driving style. For example, the analysis unit analyzes the user's driving style and adjusts the analysis algorithm. The analysis unit can also prioritize analysis of specific driving actions based on the user's driving style. For example, the analysis unit customizes the analysis results taking the user's driving style into consideration. The analysis unit can also customize the analysis algorithm based on the user's driving style using a generation AI. For example, the generation AI adjusts the analysis algorithm based on the user's driving style. This allows customizing the analysis algorithm based on the user's driving style to provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's driving style data into the generation AI and have the generation AI customize the analysis algorithm.

[0079] The visualization unit can estimate the user's emotion and adjust the visualization expression method based on the estimated user's emotion. For example, the visualization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the visualization unit calculates an emotion score based on changes in facial expression. The visualization unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the visualization unit analyzes the tone and speed of the voice and calculates an emotion score. The visualization unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the visualization unit calculates an emotion score based on heart rate fluctuations. This allows the visualization expression method to be adjusted according to the user's emotion, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the visualization unit may be performed using or without the generation AI. For example, the visualization unit may input user emotion data into the generation AI and have the generation AI adjust the visualization expression method.

[0080] The visualization unit can display not only future actions but also past action history during visualization. For example, the visualization unit displays past driving actions and compares them with future actions. The visualization unit can also predict future actions based on the past action history. For example, the visualization unit displays the past action history to encourage the user's understanding. The visualization unit can also use the generation AI to display not only future actions but also the past action history. For example, the generation AI displays past driving actions and compares them with future actions. In this way, by also displaying the past action history, the user can better understand future actions. Some or all of the above-mentioned processing in the visualization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input past action history data into the generation AI and cause the generation AI to predict future actions.

[0081] The visualization unit can customize the display style according to the user's visual preferences during visualization. For example, the visualization unit changes the animation style according to the user's preferences. The visualization unit can also adjust the color and design based on the user's visual preferences. For example, the visualization unit suggests an optimal display style based on the user's past selection history. The visualization unit can also customize the display style according to the user's visual preferences using a generation AI. For example, the generation AI changes the animation style according to the user's preferences. This allows the display style to be customized according to the user's visual preferences, thereby providing more attractive information. Some or all of the above-described processing in the visualization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input the user's visual preference data into the generation AI and cause the generation AI to customize the display style.

[0082] When visualizing, the visualization unit can display the future operation not only on the in-car display but also on a smartphone or tablet. For example, the visualization unit can display the future operation on a smartphone in addition to the in-car display. The visualization unit can also display the future operation on a tablet so that the user can check it on multiple devices. For example, the visualization unit synchronizes information between the in-car display and the smartphone and tablet. The visualization unit can also use a generation AI to display not only on the in-car display but also on a smartphone or tablet. For example, the generation AI displays the future operation on a smartphone in addition to the in-car display. This allows the user to check the information on multiple devices by displaying it on a smartphone or tablet. Some or all of the above-mentioned processing in the visualization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input data from a smartphone or tablet to the generation AI and have the generation AI synchronize the display.

[0083] The visualization unit can estimate the user's emotions and adjust the timing of visualization based on the estimated user emotions. For example, the visualization unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the visualization unit calculates an emotion score based on changes in facial expressions. The visualization unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the visualization unit analyzes the tone and speed of the voice and calculates an emotion score. The visualization unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the visualization unit calculates an emotion score based on heart rate fluctuations. This allows the timing of visualization to be adjusted according to the user's emotions, making it possible to provide information at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the visualization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the visualization unit may input user emotion data to the generation AI and have the generation AI adjust the timing of visualization.

[0084] The visualization unit can customize the display content by taking into account the user's driving history when visualizing the data. The visualization unit, for example, adjusts the display content based on the user's past driving history. The visualization unit can also emphasize specific driving actions by taking into account the user's driving history. For example, the visualization unit displays specific road conditions based on the user's driving history. The visualization unit can also customize the display content by taking into account the user's driving history using a generation AI. For example, the generation AI adjusts the display content based on the user's driving history. This makes it possible to provide more appropriate display content by taking into account the user's driving history. Some or all of the above-described processing in the visualization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input the user's driving history data into the generation AI and have the generation AI customize the display content.

[0085] The visualization unit can include car maintenance information in the display when visualizing. For example, the visualization unit displays car maintenance information to inform the user of the car's condition. The visualization unit can also display the condition of specific parts based on the car maintenance information. For example, the visualization unit displays car maintenance information to prompt the user to confirm driving safety. The visualization unit can also include car maintenance information in the display using a generation AI. For example, the generation AI displays car maintenance information to inform the user of the car's condition. In this way, by including the car maintenance information in the display, the user can be informed of the car's condition. Some or all of the above-mentioned processing in the visualization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input car maintenance information to the generation AI and cause the generation AI to generate display content.

[0086] The visualization unit can adjust the display content based on the user's driving style during visualization. For example, the visualization unit analyzes the user's driving style and adjusts the display content. The visualization unit can also emphasize specific driving actions based on the user's driving style. For example, the visualization unit customizes the display content taking the user's driving style into consideration. The visualization unit can also adjust the display content based on the user's driving style using a generation AI. For example, the generation AI adjusts the display content based on the user's driving style. In this way, by adjusting the display content based on the user's driving style, more appropriate information can be provided. Some or all of the above-described processing in the visualization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input the user's driving style data to the generation AI and cause the generation AI to adjust the display content.

[0087] The feedback receiving unit can estimate the user's emotion and adjust the feedback receiving method based on the estimated user's emotion. For example, the feedback receiving unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the feedback receiving unit can calculate an emotion score based on changes in facial expression. The feedback receiving unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the feedback receiving unit can analyze the tone and speed of the voice and calculate an emotion score. The feedback receiving unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the feedback receiving unit can calculate an emotion score based on heart rate fluctuations. This allows for adjusting the feedback receiving method according to the user's emotion, thereby collecting more appropriate feedback. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback receiving unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback receiving unit may input user emotion data to the generation AI and cause the generation AI to adjust the method of receiving feedback.

[0088] When receiving feedback, the feedback receiving unit can select the optimal receiving method by referring to the user's past feedback history. For example, the feedback receiving unit can suggest the optimal receiving method based on the user's past feedback history. The feedback receiving unit can also prioritize a specific feedback method by referring to the user's feedback history. For example, the feedback receiving unit can emphasize a specific feedback item based on the user's past feedback history. The feedback receiving unit can also select the optimal receiving method by referring to the user's past feedback history using a generation AI. For example, the generation AI can suggest the optimal receiving method based on the user's past feedback history. In this way, the optimal feedback receiving method can be provided by referring to the user's past feedback history. Some or all of the above-described processing in the feedback receiving unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback receiving unit can input the user's feedback history data into the generation AI and cause the generation AI to select the optimal receiving method.

[0089] The feedback receiving unit can customize the feedback receiving method according to the user's current situation when receiving feedback. For example, the feedback receiving unit analyzes the user's current situation and provides the optimal feedback method. The feedback receiving unit can also prioritize a specific feedback method taking the user's current situation into consideration. For example, the feedback receiving unit customizes feedback items based on the user's current situation. The feedback receiving unit can also customize the feedback receiving method according to the user's current situation using a generation AI. For example, the generation AI analyzes the user's current situation and provides the optimal feedback method. This allows more appropriate feedback to be collected by customizing the feedback receiving method according to the user's current situation. Some or all of the above-described processing in the feedback receiving unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback receiving unit can input data on the user's current situation into the generation AI and cause the generation AI to customize the feedback receiving method.

[0090] The feedback receiving unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. For example, the feedback receiving unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the feedback receiving unit calculates an emotion score based on changes in facial expression. The feedback receiving unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the feedback receiving unit analyzes the tone and speed of the voice and calculates the emotion score. The feedback receiving unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the feedback receiving unit calculates the emotion score based on heart rate fluctuations. This allows the priority of feedback to be determined according to the user's emotions, thereby enabling important feedback to be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback receiving unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback receiving unit may input user emotion data into the generation AI and have the generation AI determine the priority of the feedback.

[0091] When receiving feedback, the feedback receiving unit can select the optimal receiving method by taking into account the user's geographical location information. The feedback receiving unit, for example, suggests the optimal feedback method based on the user's current location. The feedback receiving unit can also prioritize a specific feedback method by taking into account the user's geographical location information. For example, the feedback receiving unit customizes feedback items based on the user's current location. The feedback receiving unit can also select the optimal receiving method by using a generation AI by taking into account the user's geographical location information. For example, the generation AI suggests the optimal feedback method based on the user's current location. In this way, the optimal feedback receiving method can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback receiving unit may be performed by using the generation AI, or may be performed without using the generation AI. For example, the feedback receiving unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal receiving method.

[0092] When receiving feedback, the feedback receiving unit can analyze the user's social media activity to obtain relevant feedback. For example, the feedback receiving unit analyzes the user's social media activity and suggests relevant feedback items. The feedback receiving unit can also prioritize specific feedback items based on the content of the user's social media posts. For example, the feedback receiving unit can suggest relevant feedback items based on the activity of the user's friends on social media. The feedback receiving unit can also analyze the user's social media activity to obtain relevant feedback using a generation AI. For example, the generation AI analyzes the user's social media activity and suggests relevant feedback items. In this way, relevant feedback can be collected by analyzing the user's social media activity. Some or all of the above-described processing in the feedback receiving unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback receiving unit can input the user's social media data into the generation AI and cause the generation AI to obtain relevant feedback.

[0093] The improvement unit can estimate the user's emotions and adjust the improvement method based on the estimated user's emotions. For example, the improvement unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the improvement unit calculates an emotion score based on changes in facial expressions. The improvement unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the improvement unit analyzes the tone and speed of the voice and calculates an emotion score. The improvement unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the improvement unit calculates an emotion score based on heart rate fluctuations. This allows for more appropriate improvement by adjusting the improvement method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the improvement unit can be performed using the generation AI, or without the generation AI. For example, the improvement unit can input user emotion data into the generation AI and have the generation AI adjust the improvement method.

[0094] When making an improvement, the improvement unit can select the optimal improvement method by referring to the user's past feedback history. For example, the improvement unit can suggest the optimal improvement method based on the user's past feedback history. The improvement unit can also prioritize a specific improvement method by referring to the user's feedback history. For example, the improvement unit can emphasize a specific improvement item based on the user's past feedback history. The improvement unit can also select the optimal improvement method by referring to the user's past feedback history using the generation AI. For example, the generation AI can suggest the optimal improvement method based on the user's past feedback history. In this way, the optimal improvement method can be provided by referring to the user's past feedback history. Some or all of the above-described processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's feedback history data into the generation AI and have the generation AI select the optimal improvement method.

[0095] During improvement, the improvement unit can customize the improvement method according to the user's current situation. For example, the improvement unit analyzes the user's current situation and provides the optimal improvement method. The improvement unit can also prioritize a specific improvement method by taking the user's current situation into consideration. For example, the improvement unit customizes improvement items based on the user's current situation. The improvement unit can also customize the improvement method according to the user's current situation using a generation AI. For example, the generation AI analyzes the user's current situation and provides the optimal improvement method. This allows for more appropriate improvement by customizing the improvement method according to the user's current situation. Some or all of the above-described processing in the improvement unit may be performed using or without the generation AI. For example, the improvement unit can input data on the user's current situation into the generation AI and have the generation AI customize the improvement method.

[0096] During improvement, the improvement unit can adjust the analysis algorithm by reflecting user feedback. For example, the improvement unit adjusts the analysis algorithm based on user feedback. The improvement unit can also reflect user feedback and prioritize specific analysis items. For example, the improvement unit customizes the analysis results based on user feedback. The improvement unit can also adjust the analysis algorithm by reflecting user feedback using a generation AI. For example, the generation AI adjusts the analysis algorithm based on user feedback. This allows the analysis algorithm to be more appropriately adjusted by reflecting user feedback. Some or all of the above-mentioned processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input user feedback data into the generation AI and have the generation AI adjust the analysis algorithm.

[0097] The improvement unit can estimate the user's emotions and determine priorities for improvement based on the estimated user emotions. For example, the improvement unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the improvement unit calculates an emotion score based on changes in facial expression. The improvement unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the improvement unit analyzes the tone and speed of the voice and calculates an emotion score. The improvement unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the improvement unit calculates an emotion score based on heart rate fluctuations. This allows the prioritization of improvements based on the user's emotions, thereby prioritizing important improvement items. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit may input user emotion data into the generation AI and have the generation AI determine the priority of improvements.

[0098] When making an improvement, the improvement unit can select the optimal improvement method by taking into account the user's geographical location information. For example, the improvement unit can suggest the optimal improvement method based on the user's current location. The improvement unit can also prioritize a specific improvement method by taking into account the user's geographical location information. For example, the improvement unit customizes improvement items based on the user's current location. The improvement unit can also use a generation AI to select the optimal improvement method by taking into account the user's geographical location information. For example, the generation AI suggests the optimal improvement method based on the user's current location. This makes it possible to provide the optimal improvement method by taking into account the user's geographical location information. Some or all of the above-described processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal improvement method.

[0099] When making an improvement, the improvement unit can analyze the user's social media activity and suggest relevant improvement methods. For example, the improvement unit analyzes the user's social media activity and suggests relevant improvement items. The improvement unit can also prioritize specific improvement items based on the content of the user's social media posts. For example, the improvement unit can suggest relevant improvement items based on the activity of the user's friends on social media. The improvement unit can also analyze the user's social media activity and suggest relevant improvement methods using a generation AI. For example, the generation AI analyzes the user's social media activity and suggests relevant improvement items. In this way, relevant improvement methods can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's social media data into the generation AI and have the generation AI execute the suggestion of relevant improvement methods.

[0100] When making an improvement, the improvement unit can customize the improvement method based on the user's driving style. For example, the improvement unit analyzes the user's driving style and adjusts the improvement method. The improvement unit can also prioritize improving specific driving actions based on the user's driving style. For example, the improvement unit customizes the improvement method taking the user's driving style into consideration. The improvement unit can also customize the improvement method based on the user's driving style using a generation AI. For example, the generation AI adjusts the improvement method based on the user's driving style. This allows for more appropriate improvement by customizing the improvement method based on the user's driving style. Some or all of the above-described processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's driving style data into the generation AI and have the generation AI customize the improvement method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, visualization unit, feedback reception unit, and improvement unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the visualization unit is realized by the display 40A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the feedback reception unit is realized by the touch panel 38A or microphone 38B of the smart device 14, or the specific processing unit 290 of the data processing device 12. For example, the improvement unit is realized by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, visualization unit, feedback reception unit, and improvement unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the visualization unit is realized by the display of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the feedback reception unit is realized by the microphone 238 or touch panel of the smart glasses 214, or the specific processing unit 290 of the data processing device 12. For example, the improvement unit is realized by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, visualization unit, feedback reception unit, and improvement unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the visualization unit is realized by the display 343 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the feedback reception unit is realized by the microphone 238 or touch panel of the headset type terminal 314, or the specific processing unit 290 of the data processing device 12. For example, the improvement unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, visualization unit, feedback reception unit, and improvement unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the visualization unit is realized by the display of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the feedback reception unit is realized by the microphone 238 or touch panel of the robot 414, or the specific processing unit 290 of the data processing device 12. For example, the improvement unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The analysis unit can analyze the user's driving style and customize the analysis results. For example, the analysis unit can analyze the user's past driving style and provide analysis results based on that style the next time the user drives. The analysis unit can also emphasize specific driving actions depending on the user's driving style. For example, for a user who frequently brakes suddenly, detailed information about the timing and strength of braking can be provided. This allows for more appropriate driving assistance by providing analysis results that are tailored to the user's driving style.

[0103] The visualization unit can estimate the user's emotions and adjust the display content based on the estimated emotions. For example, if the user is nervous, the visualization unit can provide more detailed information to give the user a sense of security. If the user is relaxed, the visualization unit can simplify the display content and provide only the minimum necessary information. Furthermore, if the user is excited, the display content can be made more interactive to attract the user's interest. This makes it possible to provide more appropriate information by providing display content according to the user's emotions.

[0104] The feedback receiving unit can analyze user feedback in real time and immediately make suggestions for improvement. For example, if a user feels anxious while driving, the feedback can be immediately analyzed and suggestions can be made to reduce that anxiety the next time the user drives. Also, if a user is dissatisfied with a particular function, the feedback can be used to suggest improvements to the function. Furthermore, if a user experiences difficulty in a particular driving situation, driving assistance appropriate to that situation can be suggested. This allows for faster response by analyzing user feedback in real time and immediately making suggestions for improvement.

[0105] The improvement unit can estimate the user's emotions and determine the priority of improvements based on the estimated emotions. For example, if the user is feeling strong anxiety, improvements to reduce that anxiety are prioritized. Also, if the user is satisfied, improvements can be made to maintain that satisfaction. Furthermore, if the user is excited, new functions that take advantage of that excitement can be proposed. In this way, by determining the priority of improvements according to the user's emotions, more effective improvements are possible.

[0106] The analysis unit can integrate in-vehicle sensor data to improve the accuracy of the analysis. For example, in-vehicle camera data can be integrated into the analysis to take the driver's condition into account. In-vehicle temperature sensor data can also be integrated into the analysis to maintain a comfortable driving environment. Furthermore, in-vehicle voice data can be integrated into the analysis to reflect the driver's instructions. In this way, by integrating in-vehicle sensor data, the accuracy of the analysis can be improved.

[0107] The visualization unit can estimate the user's emotions and adjust the timing of visualization based on the estimated emotions. For example, if the user is nervous, the visualization unit can provide information at an earlier timing to give the user a sense of security. Also, if the user is relaxed, the visualization unit can delay the timing of providing information and provide only the minimum amount of information necessary. Furthermore, if the user is excited, the visualization unit can adjust the timing of providing information to attract the user's interest. In this way, by adjusting the timing of providing information according to the user's emotions, more appropriate information can be provided.

[0108] The feedback receiving unit can reflect the user's feedback in subsequent analyses. For example, if the user is dissatisfied with a particular driving behavior, the analysis algorithm can be adjusted based on that feedback. Also, if the user gives a high rating to a particular function, an analysis can be performed to enhance that function. Furthermore, if the user experiences difficulty in a particular driving situation, an analysis can be performed that is tailored to that situation. This allows for more appropriate driving assistance by improving the analysis results based on user feedback.

[0109] The analysis unit can predict future behavior by referring to past driving data. For example, it can predict behavior at a specific intersection based on past driving data. It can also predict speed adjustments on a specific road based on past driving data. It can also predict behavior under specific weather conditions based on past driving data. Thus, by referring to past driving data, it is possible to more accurately predict future behavior.

[0110] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. For example, if the user is nervous, the visualization unit can provide more detailed information to give a sense of security. If the user is relaxed, the visualization unit can simplify the display content and provide only the minimum necessary information. Furthermore, if the user is excited, the display content can be made more interactive to attract the user's interest. This makes it possible to provide more appropriate information by providing display content that corresponds to the user's emotions.

[0111] The analysis unit can include surrounding traffic conditions and weather information in the analysis. For example, it can analyze real-time traffic congestion information and reflect it in the vehicle's behavior. It can also analyze current weather information and adjust driving behavior in rainy weather. It can also analyze the movements of surrounding vehicles and predict actions to avoid collisions. By including surrounding traffic conditions and weather information in the analysis, it is possible to predict future behavior more accurately.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The analysis unit analyzes the autonomous driving control content. For example, the analysis unit analyzes information such as what action the vehicle will take next and what route it will take. The analysis unit can also use the generation AI to analyze the autonomous driving control content in real time. For example, the generation AI predicts the next action and route based on the vehicle's sensor data and map information. Step 2: The visualization unit visualizes future actions based on the information analyzed by the analysis unit. For example, the visualization unit displays the next action or route as an animation on an in-car display. The visualization unit can also use a generative AI to visualize future actions in a way that is understandable to the user. For example, the generative AI displays the next action or route as an animation based on the analysis results. Step 3: The feedback receiving unit receives the user's feedback. For example, the feedback receiving unit receives feedback by the user inputting it on a touch panel or by providing it by voice. The feedback receiving unit can also analyze the user's feedback using a generation AI. For example, the generation AI extracts information to improve the analysis results based on the user's feedback. Step 4: The improvement unit improves the analysis results based on the feedback received by the feedback receiving unit. For example, the improvement unit reflects the user's feedback in subsequent analyses. The improvement unit can also use the generation AI to improve the analysis results. For example, the generation AI adjusts the analysis algorithm based on the user's feedback.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] 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.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the control content of autonomous driving; a visualization unit that visualizes future actions based on the information analyzed by the analysis unit; a feedback receiving unit that receives user feedback; an improvement unit that improves the analysis result based on the feedback received by the feedback receiving unit. A system characterized by:

2. The analysis unit Analyzing information on what the car will do next and what route it will take 2. The system of claim 1.

3. The visualization unit Based on the analyzed information, the next action and route are displayed as an animation on the in-car display.

2. The system of claim 1.

4. The feedback receiving unit Allow users to enter feedback on a touch screen or provide feedback by voice 2. The system of claim 1.

5. The improvement unit Reflect user feedback in future analyses 2. The system of claim 1.

6. The analysis unit Estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions.

2. The system of claim 1.

7. The analysis unit Analysis includes not only vehicle behavior but also surrounding traffic conditions and weather information 2. The system of claim 1.

8. The analysis unit During analysis, past operating data is referenced to predict future behavior 2. The system of claim 1.

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