system

Abstract

The system according to the embodiment aims to collect vehicle driving data and use it to improve autonomous driving systems. [Solution] A system according to an embodiment includes a collection unit, a provision unit, an analysis unit, an improvement unit, and a reward unit. The collection unit collects vehicle driving data. The provision unit provides the data collected by the collection unit to an autonomous driving system company. The analysis unit analyzes the data provided by the provision unit. The improvement unit improves the autonomous driving system based on the analysis results obtained by the analysis unit. The reward unit pays rewards to data providers based on the data provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to efficiently collect vehicle driving data and utilize it to improve autonomous driving systems, so there is room for improvement.

[0005] The system according to the embodiment aims to collect vehicle driving data and use it to improve autonomous driving systems. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a provision unit, an analysis unit, an improvement unit, and a reward unit. The collection unit collects vehicle driving data. The provision unit provides the data collected by the collection unit to an autonomous driving system company. The analysis unit analyzes the data provided by the provision unit. The improvement unit improves the autonomous driving system based on the analysis results obtained by the analysis unit. The reward unit pays rewards to data providers based on the data provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect vehicle driving data and use it to improve the autonomous driving system. [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 improves the accuracy of an autonomous driving system by collecting vehicle driving history data and providing it to an autonomous driving system company. The system collects vehicle driving history data and provides it to the autonomous driving system company. The autonomous driving system company analyzes the provided data to improve the accuracy of the autonomous driving system. For example, the system collects vehicle driving history data. For example, the system collects detailed driving data using sensors and GPS mounted on the vehicle. Next, the system provides the collected data to the autonomous driving system company. The provided data is securely transferred using encryption technology. For example, the collected data is encrypted and transmitted to the autonomous driving system company using a secure communication protocol. Next, the autonomous driving system company analyzes the provided data. The system analyzes the data using AI to identify traffic patterns and dangerous areas. For example, the system analyzes the frequency of traffic congestion and accidents on specific roads, and uses the results to improve the autonomous driving system. Finally, the system pays rewards to data providers. Data providers can receive rewards for providing their driving history data. For example, the system pays rewards to data providers using an electronic payment system. This allows car owners to effectively utilize their data, earn income, and contribute to improving the accuracy of the autonomous driving system. This allows the system to collect driving history data of the vehicle and provide it to the autonomous driving system company, thereby improving the accuracy of the autonomous driving system. For example, car owners can effectively utilize their data, earn income, and contribute to improving the accuracy of the autonomous driving system.

[0029] An autonomous driving data provision system according to an embodiment includes a collection unit, a provision unit, an analysis unit, an improvement unit, and a reward unit. The collection unit collects vehicle driving data. The vehicle driving data includes, but is not limited to, speed, location information, and acceleration. The collection unit collects driving data using, for example, sensors and GPS mounted on the vehicle. The collection unit can also dynamically change the type and frequency of data collection depending on the driving conditions of the vehicle. For example, when driving on a highway, data on speed and vehicle-to-vehicle distance is collected frequently. The provision unit provides the collected data to an autonomous driving system company. For example, the provision unit encrypts the collected data and transfers it to the autonomous driving system company using a secure communication protocol. For example, an algorithm such as AES or RSA is used for encryption. The analysis unit analyzes the provided data using AI. For example, the analysis unit uses technologies such as deep learning and machine learning to identify traffic patterns and dangerous areas. For example, the analysis unit analyzes the frequency of traffic congestion and accidents on a specific road. The improvement unit improves the autonomous driving system based on the analysis results obtained by the analysis unit. The improvement unit sets, for example, which parts of the system to improve and evaluation criteria for the improvements. The reward unit pays rewards to data providers based on the data provided by the provision unit. The reward unit pays rewards to data providers using, for example, an electronic payment system. In this way, the autonomous driving data provision system according to the embodiment can improve the accuracy of the autonomous driving system by collecting vehicle driving data and providing it to an autonomous driving system company. For example, car owners can effectively utilize their data to earn income and contribute to improving the accuracy of the autonomous driving system.

[0030] The collection unit can collect driving data using a sensor or GPS mounted on the vehicle. Examples of sensors include, but are not limited to, a camera, LIDAR, and radar. The collection unit can collect driving data using, for example, a camera mounted on the vehicle. The collection unit can also collect driving data using a LIDAR mounted on the vehicle. The collection unit can also collect driving data using a radar mounted on the vehicle. Examples of GPS include, but are not limited to, an update frequency and an error range of location information. The collection unit can collect location information using, for example, a GPS mounted on the vehicle. The collection unit can also record a driving route based on the GPS location information. The collection unit can also collect speed data based on the GPS location information. In this way, accurate driving data can be collected by using the sensor or GPS mounted on the vehicle. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data acquired from the sensor or GPS into AI and have the AI ​​analyze and process the data.

[0031] The providing unit can encrypt the collected data and transfer it to the autonomous driving system company using a secure communication protocol. Examples of encryption include, but are not limited to, algorithms such as AES and RSA. For example, the providing unit encrypts the collected data using AES. The providing unit can also encrypt the collected data using RSA. Examples of secure communication protocols include, but are not limited to, HTTPS and TLS. For example, the providing unit transfers the collected data using HTTPS. The providing unit can also transfer the collected data using TLS. The providing unit can also transfer the collected data using a VPN. This allows data privacy to be protected and data to be transferred securely. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the collected data into AI and have the AI ​​encrypt and transfer the data.

[0032] The analysis unit can analyze data using AI to identify traffic patterns and dangerous spots. AI includes, but is not limited to, technologies such as deep learning and machine learning. The analysis unit can analyze data using, for example, deep learning. The analysis unit can also analyze data using machine learning. The analysis unit can also analyze data using natural language processing. Traffic patterns include, but are not limited to, traffic volume by time of day and locations of congestion. The analysis unit can analyze, for example, traffic volume by time of day. The analysis unit can also analyze locations of congestion. The analysis unit can also analyze traffic congestion on a specific road. Dangerous spots include, but are not limited to, locations where accidents frequently occur and sharp curves. The analysis unit can, for example, identify locations where accidents frequently occur. The analysis unit can also identify sharp curves. The analysis unit can also analyze the frequency of accidents on a specific road. As a result, traffic patterns and dangerous spots can be accurately identified by using AI. Some or all of the above-mentioned processing in the analysis unit is performed using AI. For example, the analysis unit can input collected data into AI and have the AI ​​identify traffic patterns and dangerous areas.

[0033] The improvement unit can improve the autonomous driving system based on the analysis results obtained by the analysis unit. Examples of improvements include, but are not limited to, which part of the system to improve and evaluation criteria for the improvement. For example, the improvement unit improves a specific part of the system. The improvement unit can also set evaluation criteria for the improvement. The improvement unit can also adjust system parameters based on the analysis results. This allows the autonomous driving system to be effectively improved based on the analysis results. Some or all of the above-mentioned processing in the improvement unit may be performed using, or without, AI. For example, the improvement unit can input the analysis results into AI and have the AI ​​perform system improvements.

[0034] The reward unit can pay rewards to data providers using an electronic payment system. Examples of electronic payment systems include, but are not limited to, credit cards, electronic money, and bank transfers. The reward unit can pay rewards using, for example, a credit card. The reward unit can also pay rewards using electronic money. The reward unit can also pay rewards using bank transfers. This allows rewards to be paid to data providers quickly and safely. Some or all of the above-described processing in the reward unit may be performed using, for example, AI, or may be performed without using AI. For example, the reward unit can have AI execute the payment of rewards.

[0035] The collection unit can dynamically change the type and frequency of data collection depending on the driving conditions of the vehicle. For example, when driving on a highway, the collection unit collects data on speed and vehicle-to-vehicle distance at a high frequency. Furthermore, when driving in an urban area, the collection unit can also collect detailed data on waiting at traffic lights and at intersections. Furthermore, when parking, the collection unit can also collect data on parking location and parking time. This allows data collection to be optimized depending on the driving conditions, making it possible to collect more useful data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input driving condition data into AI and have the AI ​​change the type and frequency of data collection.

[0036] The collection unit can simultaneously collect in-vehicle environmental data when collecting driving data. Environmental data includes, but is not limited to, temperature, humidity, and sound, for example. For example, when the temperature inside the vehicle is high, the collection unit collects information on air conditioner usage. Furthermore, when the humidity inside the vehicle is high, the collection unit can also collect information on whether the windows are open or closed. Furthermore, when the volume inside the vehicle is high, the collection unit can also collect information on audio system usage. By collecting in-vehicle environmental data, more detailed data analysis becomes possible. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the environmental data into AI and have the AI ​​analyze and process the data.

[0037] The collection unit can collect vehicle maintenance information when collecting driving data. Maintenance information includes, but is not limited to, for example, oil change timing and tire wear status. For example, the collection unit collects information when the oil change time is approaching. The collection unit can also periodically check tire wear status and collect the data. The collection unit can also collect brake pad wear status and provide maintenance information as needed. In this way, by collecting maintenance information as well, the vehicle condition can be comprehensively understood. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input maintenance information into AI and have the AI ​​analyze and process the data.

[0038] When collecting driving data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the vehicle. Geographical location information includes, but is not limited to, GPS data, map information, etc. For example, the collection unit prioritizes collecting data on traffic congestion and waiting at traffic lights in urban areas. Furthermore, the collection unit can prioritize collecting data on speed and fuel efficiency in suburban areas. Furthermore, the collection unit can prioritize collecting data on road gradients and curves in mountainous areas. In this way, highly relevant data can be efficiently collected by taking the geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical location information into AI and cause the AI ​​to prioritize the collection of highly relevant data.

[0039] The collection unit can analyze the social media activity of the vehicle when collecting driving data and collect related data. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. For example, the collection unit collects data on locations where the user checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related driving data. The collection unit can also collect related driving data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input social media data into AI and have the AI ​​collect related data.

[0040] When collecting driving data, the collection unit can customize the collection method by reflecting the user's past feedback. Past feedback includes, but is not limited to, user reviews and survey results. The collection unit customizes the type of data to be collected, for example, based on feedback provided by the user in the past. The collection unit can also adjust the collection frequency based on the user's past feedback. The collection unit can also optimize the collection method by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into AI and have the AI ​​customize the collection method.

[0041] The providing unit can adjust the encryption level of the data based on the importance of the data to be provided. Examples of the importance of the data include, but are not limited to, confidentiality and utility value of the data. For example, the providing unit can provide important data with a high encryption level. The providing unit can also provide general data with a medium encryption level. The providing unit can also provide low-importance data with a low encryption level. Examples of encryption levels include, but are not limited to, AES-128, AES-256, and the like. This allows security to be optimized by adjusting the encryption level according to the importance of the data. Some or all of the above-described processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input the importance of the data into AI and have the AI ​​adjust the encryption level.

[0042] The providing unit can apply different communication protocols depending on the category of data to be provided. Data categories include, but are not limited to, text data, image data, and sensor data. For example, the providing unit can provide location information data using a high-security communication protocol. The providing unit can also provide speed data using a medium-security communication protocol. The providing unit can also provide environmental data using a low-security communication protocol. Communication protocols include, but are not limited to, HTTP, MQTT, and the like. This improves communication efficiency and security by applying a communication protocol depending on the data category. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the data category to AI and have the AI ​​apply the communication protocol.

[0043] The providing unit can dynamically adjust the communication bandwidth according to the amount of data to be provided. The amount of data includes, but is not limited to, data size, the number of data packets, and the like. For example, the providing unit widens the communication bandwidth when providing a large amount of data. The providing unit can also narrow the communication bandwidth when providing a small amount of data. The providing unit can also adjust the communication bandwidth in real time according to the amount of data to be provided. The communication bandwidth includes, but is not limited to, bandwidth, priority setting, and the like. This allows the communication efficiency to be optimized by adjusting the communication bandwidth according to the amount of data. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the amount of data to AI and have the AI ​​adjust the communication bandwidth.

[0044] The providing unit can adjust the data providing method taking into account the geographical distribution of the data to be provided. Examples of geographical distribution include, but are not limited to, regional data distribution and map information. For example, the providing unit may prioritize providing data on traffic congestion and traffic lights in urban areas. The providing unit may also prioritize providing data on speed and fuel efficiency in suburban areas. The providing unit may also prioritize providing data on road gradients and curves in mountainous areas. Examples of data providing methods include, but are not limited to, real-time provision and batch provision. This allows for efficient provision of highly relevant data by taking geographical distribution into account. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input geographical distribution data into AI and have the AI ​​adjust the data providing method.

[0045] The providing unit can improve the accuracy of data provision by referring to literature related to the data to be provided. Examples of related literature include, but are not limited to, academic papers and technical reports. For example, the providing unit can refer to the latest research papers related to the data to be provided. The providing unit can also refer to patent documents related to the data to be provided. The providing unit can also refer to technical literature related to the data to be provided. Examples of provision accuracy include, but are not limited to, the accuracy and completeness of the data. By referring to related literature, the accuracy of data provision can be improved. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input related literature data into AI and cause the AI ​​to improve the accuracy of data provision.

[0046] The providing unit can adjust the data providing method taking into account the market value of the data to be provided. Market value includes, for example, data demand, pricing, etc., but is not limited to these examples. For example, the providing unit provides high-value data with high security. The providing unit can also provide medium-value data with medium security. The providing unit can also provide low-value data with low security. Data providing methods include, for example, real-time provision, batch provision, etc., but are not limited to these examples. This allows the data providing method to be optimized by taking market value into account. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input market value data into AI and have the AI ​​adjust the data providing method.

[0047] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data. Examples of interrelationships between data include, but are not limited to, correlation analysis and identification of causal relationships. For example, the analysis unit performs analysis by taking into account the interrelationships between location information and speed data. The analysis unit can also perform analysis by taking into account the interrelationships between driving routes and fuel efficiency data. The analysis unit can also perform analysis by taking into account the interrelationships between environmental data and driving conditions. In this way, by taking into account the interrelationships between data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit is performed using AI. For example, the analysis unit can input the interrelationships between data into AI and have the AI ​​improve the accuracy of the analysis.

[0048] During analysis, the analysis unit can perform the analysis while taking into consideration the attribute information of the data provider. The attribute information of the provider includes, for example, age, gender, occupation, etc., but is not limited to these examples. The analysis unit performs the analysis while taking into consideration, for example, the age and gender of the data provider. The analysis unit can also perform the analysis while taking into consideration the driving experience of the data provider. The analysis unit can also perform the analysis while taking into consideration the type of vehicle and usage conditions of the data provider. In this way, by taking into consideration the attribute information of the provider, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using AI. For example, the analysis unit can input the attribute information of the provider into AI and have the AI ​​perform the analysis.

[0049] During analysis, the analysis unit can weight the analysis based on the frequency of data provision. Examples of the frequency of data provision include, but are not limited to, the number of times and duration of data provision. For example, the analysis unit may assign a higher weight to frequently provided data during analysis. The analysis unit may also assign a lower weight to rarely provided data during analysis. The analysis unit may also dynamically adjust the weighting of the analysis according to the frequency of data provision. Examples of weighting include, but are not limited to, weighting based on frequency and importance. By weighting based on the frequency of provision, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit can be performed using AI. For example, the analysis unit can input the provision frequency data into AI and have the AI ​​perform the weighting.

[0050] The analysis unit can perform the analysis while taking into account the geographical distribution of the data. Geographical distribution includes, for example, data distribution by region, map information, etc., but is not limited to these examples. For example, the analysis unit can prioritize analyzing data from urban areas to identify traffic congestion patterns. The analysis unit can also prioritize analyzing data from suburban areas to improve fuel efficiency. The analysis unit can also prioritize analyzing data from mountainous areas to evaluate road safety. In this way, by taking geographical distribution into consideration, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using AI. For example, the analysis unit can input geographical distribution data into AI and have the AI ​​perform the analysis.

[0051] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the data. Examples of related literature include, but are not limited to, academic papers and technical reports. For example, the analysis unit can refer to the latest research papers related to the data to be analyzed. The analysis unit can also refer to patent documents related to the data to be analyzed. The analysis unit can also refer to technical literature related to the data to be analyzed. By referring to related literature, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit can be performed using AI. For example, the analysis unit can input related literature data into AI and have the AI ​​improve the accuracy of the analysis.

[0052] The analysis unit can perform the analysis while taking into account the market value of the data. Market value includes, for example, data demand, pricing, etc., but is not limited to these examples. For example, the analysis unit prioritizes the analysis of high-value data. The analysis unit can also analyze data of medium value with medium priority. The analysis unit can also analyze data of low value with low priority. In this way, by taking market value into consideration, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using AI. For example, the analysis unit can input market value data into AI and have the AI ​​perform the analysis.

[0053] When making an improvement, the improvement unit can select the optimal improvement method by referring to past improvement data. Past improvement data includes, for example, past bug fix history and user feedback, but is not limited to these examples. The improvement unit selects the optimal improvement method based on, for example, the past improvement data. The improvement unit can also extract effective improvement methods from the past improvement data. The improvement unit can also analyze the past improvement data and select the most efficient improvement method. In this way, the optimal improvement method can be selected by referring to the past improvement data. Some or all of the above-mentioned processing in the improvement unit may be performed, for example, using AI, or may be performed without using AI. For example, the improvement unit can input past improvement data into AI and have the AI ​​select an improvement method.

[0054] The improvement unit can update the improvement method by reflecting user feedback when making an improvement. User feedback includes, but is not limited to, survey results and review comments, for example. The improvement unit updates the improvement method based on, for example, user feedback. The improvement unit can also extract effective improvement methods from user feedback. The improvement unit can also analyze user feedback and select the most efficient improvement method. This allows the improvement method to be optimized by reflecting user feedback. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input user feedback data into AI and have the AI ​​update the improvement method.

[0055] When making improvements, the improvement unit can make improvements taking into account attribute information of the data provider. Attribute information of the provider includes, for example, age, gender, occupation, etc., but is not limited to these examples. The improvement unit can make improvements taking into account, for example, the age and gender of the data provider. The improvement unit can also make improvements taking into account the driving experience of the data provider. The improvement unit can also make improvements taking into account the vehicle type and usage conditions of the data provider. In this way, by taking into account the attribute information of the provider, the accuracy of the improvements can be improved. Some or all of the above-mentioned processing in the improvement unit can be performed using, for example, AI, or without using AI. For example, the improvement unit can input the attribute information of the provider into AI and have the AI ​​execute the improvements.

[0056] The improvement unit can make improvements by taking into account the geographical distribution of the data. Geographical distribution includes, for example, data distribution by region, map information, etc., but is not limited to these examples. For example, the improvement unit can prioritize improving data in urban areas to identify traffic congestion patterns. The improvement unit can also prioritize improving data in suburban areas to improve fuel efficiency. The improvement unit can also prioritize improving data in mountainous areas to evaluate road safety. In this way, by taking geographical distribution into consideration, the accuracy of the improvement can be improved. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input geographical distribution data into AI and have the AI ​​perform the improvement.

[0057] During improvement, the improvement unit can improve the accuracy of the improvement by referring to literature related to the data. Examples of related literature include, but are not limited to, academic papers and technical reports. For example, the improvement unit can refer to the latest research papers related to the data to be improved. The improvement unit can also refer to patent documents related to the data to be improved. The improvement unit can also refer to technical literature related to the data to be improved. By doing so, the accuracy of the improvement can be improved by referring to the related literature. Some or all of the above-described processing in the improvement unit may be performed using, or without, AI. For example, the improvement unit can input related literature data into AI and have the AI ​​improve the accuracy of the improvement.

[0058] When making improvements, the improvement unit can take into account the market value of the data. Market value includes, but is not limited to, for example, data demand and pricing. For example, the improvement unit prioritizes improving high-value data. The improvement unit can also improve medium-value data with medium priority. The improvement unit can also improve low-value data with low priority. This allows for improved accuracy of improvements by taking market value into consideration. Some or all of the above-described processing in the improvement unit may be performed using, or without, AI. For example, the improvement unit can input market value data into AI and have the AI ​​perform the improvements.

[0059] The compensation unit can select the optimal payment method by referring to past compensation data when paying compensation. Past compensation data includes, for example, past payment history and compensation evaluation, but is not limited to these examples. The compensation unit selects the optimal payment method based on, for example, past compensation data. The compensation unit can also extract effective payment methods from past compensation data. The compensation unit can also analyze past compensation data and select the most efficient payment method. In this way, the optimal payment method can be selected by referring to past compensation data. Some or all of the above-described processing in the compensation unit may be performed using, for example, AI, or may be performed without using AI. For example, the compensation unit can input past compensation data into AI and have the AI ​​select the payment method.

[0060] The reward unit can update the payment method by reflecting user feedback when paying rewards. User feedback includes, but is not limited to, survey results and review comments. The reward unit updates the payment method, for example, based on the user feedback. The reward unit can also extract effective payment methods from the user feedback. The reward unit can also analyze the user feedback and select the most efficient payment method. This allows the payment method to be optimized by reflecting the user feedback. Some or all of the above-described processing in the reward unit may be performed using, for example, AI, or may be performed without using AI. For example, the reward unit can input user feedback data into AI and have the AI ​​update the payment method.

[0061] When paying rewards, the reward unit can weight the rewards based on the frequency of data provision. Examples of the frequency of data provision include, but are not limited to, the number of times and duration of data provision. For example, the reward unit can pay a higher reward to a user who frequently provides data. The reward unit can also pay a lower reward to a user who infrequently provides data. The reward unit can also dynamically adjust the weighting of the rewards according to the frequency of data provision. Examples of weighting include, but are not limited to, weighting based on frequency and weighting based on importance. Thus, weighting based on the frequency of data provision can improve the fairness of rewards. Some or all of the above-described processing in the reward unit may be performed using, or without, AI. For example, the reward unit can input data on the frequency of data provision into AI and have the AI ​​perform the weighting.

[0062] The reward unit can adjust the payment method taking into account the geographic distribution of the data when paying rewards. Examples of geographic distribution include, but are not limited to, regional data distribution and map information. For example, the reward unit can provide a quick payment method to data providers in urban areas. The reward unit can also provide a detailed payment method to data providers in suburban areas. The reward unit can also provide a simple payment method to data providers in mountainous areas. Examples of payment methods include, but are not limited to, bank transfer, electronic money, points, etc. This allows the payment method to be optimized by taking geographic distribution into account. Some or all of the above-described processing in the reward unit may be performed using, or without, AI. For example, the reward unit can input geographic distribution data into AI and have the AI ​​adjust the payment method.

[0063] When paying a reward, the reward unit may refer to literature related to the data to improve the accuracy of the payment. Examples of related literature include, but are not limited to, academic papers and technical reports. For example, the reward unit may refer to the latest research papers related to the payment. The reward unit may also refer to patent documents related to the payment. The reward unit may also refer to technical literature related to the payment. Examples of payment accuracy include, but are not limited to, the accuracy and completeness of the data. By referring to the literature, the accuracy of the payment can be improved. Some or all of the above-described processing in the reward unit may be performed using, or without, AI. For example, the reward unit may input related literature data into AI and have the AI ​​improve the accuracy of the payment.

[0064] When paying rewards, the reward unit can adjust the payment method taking into account the market value of the data. Market value includes, but is not limited to, data demand, pricing, etc. For example, the reward unit can pay a high reward to a data provider of high value. The reward unit can also pay a medium reward to a data provider of medium value. The reward unit can also pay a low reward to a data provider of low value. Payment methods include, but are not limited to, bank transfer, electronic money, points, etc. This allows the payment method to be optimized by taking market value into account. Some or all of the above-mentioned processing in the reward unit may be performed using, for example, AI, or may be performed without using AI. For example, the reward unit can input market value data into AI and have the AI ​​adjust the payment method.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] In addition to vehicle driving data, the collection unit can also collect behavior data of occupants in the vehicle. For example, it uses cameras and sensors to collect information on the seat positions of occupants and whether or not they are wearing their seat belts. The collection unit can also analyze the occupant behavior data and evaluate safety while driving. For example, it can issue a warning if an occupant is not wearing a seat belt. It can also detect a decrease in attention while driving based on the occupant behavior data and issue a warning. This can improve safety inside the vehicle.

[0067] When providing collected data, the provider can customize the method of providing it depending on the purpose of use of the data. For example, if the data is provided for research purposes, it can include detailed analysis results of the data. If the data is provided for commercial purposes, it can provide data summaries and statistical information. The provider can also adjust the form and format of the data according to the needs of the data user. This allows the data to be provided in the most optimal form for the data user.

[0068] When analyzing data, the analysis unit can improve the accuracy of the analysis by referring to external weather data. For example, when analyzing driving data in the rain, the analysis unit can refer to weather data and take into account the slipperiness of the road. The analysis unit can also analyze fluctuations in vehicle performance and fuel efficiency based on weather data such as temperature and humidity. This allows for more accurate analysis results by taking weather conditions into account when performing analysis.

[0069] The improvement unit can improve the user interface of the autonomous driving system based on the analysis results. For example, it can optimize the layout of the operation screen based on the user operation patterns obtained from the analysis results. The improvement unit can also make improvements to improve operability by reflecting user feedback. For example, it can place functions that users use frequently in positions that make them easier to operate. This can improve user operability.

[0070] The reward unit can diversify the payment methods for rewards to data providers. For example, rewards can be paid not only in cash or electronic money, but also in the form of points or coupons. The reward unit can also allow data providers to select the payment method for rewards according to their preferences. For example, the data provider can register their preferred payment method in advance and rewards can be paid using that method. This can improve the satisfaction of data providers.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The collection unit collects vehicle driving data. This data includes speed, location information, acceleration, and other information. The collection unit collects driving data using sensors and GPS installed in the vehicle, and can dynamically change the type and frequency of data collection depending on the driving situation. For example, when driving on a highway, data on speed and vehicle distance is collected frequently. Step 2: The provider provides the collected data to the autonomous driving system company. The provider encrypts the collected data and transfers it to the autonomous driving system company using a secure communication protocol. Algorithms such as AES and RSA are used for encryption. Step 3: The analysis unit uses AI to analyze the provided data. The analysis unit uses techniques such as deep learning and machine learning to identify traffic patterns and dangerous spots. For example, it analyzes the frequency of traffic congestion and accidents on specific roads. Step 4: The Improvement Department improves the autonomous driving system based on the analysis results obtained by the Analysis Department. The Improvement Department determines which parts of the system to improve and the evaluation criteria for the improvements. Step 5: The compensation unit pays compensation to the data provider based on the data provided by the provider. The compensation unit pays compensation to the data provider using an electronic payment system.

[0073] (Example 2) A system according to an embodiment of the present invention improves the accuracy of an autonomous driving system by collecting vehicle driving history data and providing it to an autonomous driving system company. The system collects vehicle driving history data and provides it to the autonomous driving system company. The autonomous driving system company analyzes the provided data to improve the accuracy of the autonomous driving system. For example, the system collects vehicle driving history data. For example, the system collects detailed driving data using sensors and GPS mounted on the vehicle. Next, the system provides the collected data to the autonomous driving system company. The provided data is securely transferred using encryption technology. For example, the collected data is encrypted and transmitted to the autonomous driving system company using a secure communication protocol. Next, the autonomous driving system company analyzes the provided data. The system analyzes the data using AI to identify traffic patterns and dangerous areas. For example, the system analyzes the frequency of traffic congestion and accidents on specific roads, and uses the results to improve the autonomous driving system. Finally, the system pays rewards to data providers. Data providers can receive rewards for providing their driving history data. For example, the system pays rewards to data providers using an electronic payment system. This allows car owners to effectively utilize their data, earn income, and contribute to improving the accuracy of the autonomous driving system. This allows the system to collect driving history data of the vehicle and provide it to the autonomous driving system company, thereby improving the accuracy of the autonomous driving system. For example, car owners can effectively utilize their data, earn income, and contribute to improving the accuracy of the autonomous driving system.

[0074] An autonomous driving data provision system according to an embodiment includes a collection unit, a provision unit, an analysis unit, an improvement unit, and a reward unit. The collection unit collects vehicle driving data. The vehicle driving data includes, but is not limited to, speed, location information, and acceleration. The collection unit collects driving data using, for example, sensors and GPS mounted on the vehicle. The collection unit can also dynamically change the type and frequency of data collection depending on the driving conditions of the vehicle. For example, when driving on a highway, data on speed and vehicle-to-vehicle distance is collected frequently. The provision unit provides the collected data to an autonomous driving system company. For example, the provision unit encrypts the collected data and transfers it to the autonomous driving system company using a secure communication protocol. For example, an algorithm such as AES or RSA is used for encryption. The analysis unit analyzes the provided data using AI. For example, the analysis unit uses technologies such as deep learning and machine learning to identify traffic patterns and dangerous areas. For example, the analysis unit analyzes the frequency of traffic congestion and accidents on a specific road. The improvement unit improves the autonomous driving system based on the analysis results obtained by the analysis unit. The improvement unit sets, for example, which parts of the system to improve and evaluation criteria for the improvements. The reward unit pays rewards to data providers based on the data provided by the provision unit. The reward unit pays rewards to data providers using, for example, an electronic payment system. In this way, the autonomous driving data provision system according to the embodiment can improve the accuracy of the autonomous driving system by collecting vehicle driving data and providing it to an autonomous driving system company. For example, car owners can effectively utilize their data to earn income and contribute to improving the accuracy of the autonomous driving system.

[0075] The collection unit can collect driving data using a sensor or GPS mounted on the vehicle. Examples of sensors include, but are not limited to, a camera, LIDAR, and radar. The collection unit can collect driving data using, for example, a camera mounted on the vehicle. The collection unit can also collect driving data using a LIDAR mounted on the vehicle. The collection unit can also collect driving data using a radar mounted on the vehicle. Examples of GPS include, but are not limited to, an update frequency and an error range of location information. The collection unit can collect location information using, for example, a GPS mounted on the vehicle. The collection unit can also record a driving route based on the GPS location information. The collection unit can also collect speed data based on the GPS location information. In this way, accurate driving data can be collected by using the sensor or GPS mounted on the vehicle. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data acquired from the sensor or GPS into AI and have the AI ​​analyze and process the data.

[0076] The providing unit can encrypt the collected data and transfer it to the autonomous driving system company using a secure communication protocol. Examples of encryption include, but are not limited to, algorithms such as AES and RSA. For example, the providing unit encrypts the collected data using AES. The providing unit can also encrypt the collected data using RSA. Examples of secure communication protocols include, but are not limited to, HTTPS and TLS. For example, the providing unit transfers the collected data using HTTPS. The providing unit can also transfer the collected data using TLS. The providing unit can also transfer the collected data using a VPN. This allows data privacy to be protected and data to be transferred securely. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the collected data into AI and have the AI ​​encrypt and transfer the data.

[0077] The analysis unit can analyze data using AI to identify traffic patterns and dangerous spots. AI includes, but is not limited to, technologies such as deep learning and machine learning. The analysis unit can analyze data using, for example, deep learning. The analysis unit can also analyze data using machine learning. The analysis unit can also analyze data using natural language processing. Traffic patterns include, but are not limited to, traffic volume by time of day and locations of congestion. The analysis unit can analyze, for example, traffic volume by time of day. The analysis unit can also analyze locations of congestion. The analysis unit can also analyze traffic congestion on a specific road. Dangerous spots include, but are not limited to, locations where accidents frequently occur and sharp curves. The analysis unit can, for example, identify locations where accidents frequently occur. The analysis unit can also identify sharp curves. The analysis unit can also analyze the frequency of accidents on a specific road. As a result, traffic patterns and dangerous spots can be accurately identified by using AI. Some or all of the above-mentioned processing in the analysis unit is performed using AI. For example, the analysis unit can input collected data into AI and have the AI ​​identify traffic patterns and dangerous areas.

[0078] The improvement unit can improve the autonomous driving system based on the analysis results obtained by the analysis unit. Examples of improvements include, but are not limited to, which part of the system to improve and evaluation criteria for the improvement. For example, the improvement unit improves a specific part of the system. The improvement unit can also set evaluation criteria for the improvement. The improvement unit can also adjust system parameters based on the analysis results. This allows the autonomous driving system to be effectively improved based on the analysis results. Some or all of the above-mentioned processing in the improvement unit may be performed using, or without, AI. For example, the improvement unit can input the analysis results into AI and have the AI ​​perform system improvements.

[0079] The reward unit can pay rewards to data providers using an electronic payment system. Examples of electronic payment systems include, but are not limited to, credit cards, electronic money, and bank transfers. The reward unit can pay rewards using, for example, a credit card. The reward unit can also pay rewards using electronic money. The reward unit can also pay rewards using bank transfers. This allows rewards to be paid to data providers quickly and safely. Some or all of the above-described processing in the reward unit may be performed using, for example, AI, or may be performed without using AI. For example, the reward unit can have AI execute the payment of rewards.

[0080] The collection unit can estimate the user's emotions and adjust the timing of collecting driving data based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the collection frequency to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the collection frequency to collect detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data. This reduces the user's burden by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​adjust the collection timing.

[0081] The collection unit can dynamically change the type and frequency of data collection depending on the driving conditions of the vehicle. For example, when driving on a highway, the collection unit collects data on speed and vehicle-to-vehicle distance at a high frequency. Furthermore, when driving in an urban area, the collection unit can also collect detailed data on waiting at traffic lights and at intersections. Furthermore, when parking, the collection unit can also collect data on parking location and parking time. This allows data collection to be optimized depending on the driving conditions, making it possible to collect more useful data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input driving condition data into AI and have the AI ​​change the type and frequency of data collection.

[0082] The collection unit can simultaneously collect in-vehicle environmental data when collecting driving data. Environmental data includes, but is not limited to, temperature, humidity, and sound, for example. For example, when the temperature inside the vehicle is high, the collection unit collects information on air conditioner usage. Furthermore, when the humidity inside the vehicle is high, the collection unit can also collect information on whether the windows are open or closed. Furthermore, when the volume inside the vehicle is high, the collection unit can also collect information on audio system usage. By collecting in-vehicle environmental data, more detailed data analysis becomes possible. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the environmental data into AI and have the AI ​​analyze and process the data.

[0083] The collection unit can collect vehicle maintenance information when collecting driving data. Maintenance information includes, but is not limited to, for example, oil change timing and tire wear status. For example, the collection unit collects information when the oil change time is approaching. The collection unit can also periodically check tire wear status and collect the data. The collection unit can also collect brake pad wear status and provide maintenance information as needed. In this way, by collecting maintenance information as well, the vehicle condition can be comprehensively understood. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input maintenance information into AI and have the AI ​​analyze and process the data.

[0084] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting only important data. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data related to the user's driving route. Thus, by determining the priority of data according to the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​determine the priority of the data.

[0085] When collecting driving data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the vehicle. Geographical location information includes, but is not limited to, GPS data, map information, etc. For example, the collection unit prioritizes collecting data on traffic congestion and waiting at traffic lights in urban areas. Furthermore, the collection unit can prioritize collecting data on speed and fuel efficiency in suburban areas. Furthermore, the collection unit can prioritize collecting data on road gradients and curves in mountainous areas. In this way, highly relevant data can be efficiently collected by taking the geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical location information into AI and cause the AI ​​to prioritize the collection of highly relevant data.

[0086] The collection unit can analyze the social media activity of the vehicle when collecting driving data and collect related data. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. For example, the collection unit collects data on locations where the user checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related driving data. The collection unit can also collect related driving data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input social media data into AI and have the AI ​​collect related data.

[0087] When collecting driving data, the collection unit can customize the collection method by reflecting the user's past feedback. Past feedback includes, but is not limited to, user reviews and survey results. The collection unit customizes the type of data to be collected, for example, based on feedback provided by the user in the past. The collection unit can also adjust the collection frequency based on the user's past feedback. The collection unit can also optimize the collection method by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into AI and have the AI ​​customize the collection method.

[0088] The providing unit can estimate the user's emotions and adjust the timing of data provision based on the estimated user emotions. For example, the providing unit can reduce the frequency of data provision when the user is stressed. The providing unit can also increase the frequency of data provision when the user is relaxed. The providing unit can also prioritize providing only important data when the user is in a hurry. This reduces the burden on the user by adjusting the timing of data provision according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of data provision.

[0089] The providing unit can adjust the encryption level of the data based on the importance of the data to be provided. Examples of the importance of the data include, but are not limited to, confidentiality and utility value of the data. For example, the providing unit can provide important data with a high encryption level. The providing unit can also provide general data with a medium encryption level. The providing unit can also provide low-importance data with a low encryption level. Examples of encryption levels include, but are not limited to, AES-128, AES-256, and the like. This allows security to be optimized by adjusting the encryption level according to the importance of the data. Some or all of the above-described processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input the importance of the data into AI and have the AI ​​adjust the encryption level.

[0090] The providing unit can apply different communication protocols depending on the category of data to be provided. Data categories include, but are not limited to, text data, image data, and sensor data. For example, the providing unit can provide location information data using a high-security communication protocol. The providing unit can also provide speed data using a medium-security communication protocol. The providing unit can also provide environmental data using a low-security communication protocol. Communication protocols include, but are not limited to, HTTP, MQTT, and the like. This improves communication efficiency and security by applying a communication protocol depending on the data category. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the data category to AI and have the AI ​​apply the communication protocol.

[0091] The providing unit can dynamically adjust the communication bandwidth according to the amount of data to be provided. The amount of data includes, but is not limited to, data size, the number of data packets, and the like. For example, the providing unit widens the communication bandwidth when providing a large amount of data. The providing unit can also narrow the communication bandwidth when providing a small amount of data. The providing unit can also adjust the communication bandwidth in real time according to the amount of data to be provided. The communication bandwidth includes, but is not limited to, bandwidth, priority setting, and the like. This allows the communication efficiency to be optimized by adjusting the communication bandwidth according to the amount of data. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the amount of data to AI and have the AI ​​adjust the communication bandwidth.

[0092] The providing unit can estimate the user's emotions and determine the priority of data to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing only important data. Furthermore, if the user is relaxed, the providing unit can prioritize providing detailed data. Furthermore, if the user is in a hurry, the providing unit can prioritize providing data related to the driving route. Thus, by determining the priority of data according to the user's emotions, important data can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into an AI and have the AI ​​determine the priority of the data.

[0093] The providing unit can adjust the data providing method taking into account the geographical distribution of the data to be provided. Examples of geographical distribution include, but are not limited to, regional data distribution and map information. For example, the providing unit may prioritize providing data on traffic congestion and traffic lights in urban areas. The providing unit may also prioritize providing data on speed and fuel efficiency in suburban areas. The providing unit may also prioritize providing data on road gradients and curves in mountainous areas. Examples of data providing methods include, but are not limited to, real-time provision and batch provision. This allows for efficient provision of highly relevant data by taking geographical distribution into account. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input geographical distribution data into AI and have the AI ​​adjust the data providing method.

[0094] The providing unit can improve the accuracy of data provision by referring to literature related to the data to be provided. Examples of related literature include, but are not limited to, academic papers and technical reports. For example, the providing unit can refer to the latest research papers related to the data to be provided. The providing unit can also refer to patent documents related to the data to be provided. The providing unit can also refer to technical literature related to the data to be provided. Examples of provision accuracy include, but are not limited to, the accuracy and completeness of the data. By referring to related literature, the accuracy of data provision can be improved. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input related literature data into AI and cause the AI ​​to improve the accuracy of data provision.

[0095] The providing unit can adjust the data providing method taking into account the market value of the data to be provided. Market value includes, for example, data demand, pricing, etc., but is not limited to these examples. For example, the providing unit provides high-value data with high security. The providing unit can also provide medium-value data with medium security. The providing unit can also provide low-value data with low security. Data providing methods include, for example, real-time provision, batch provision, etc., but are not limited to these examples. This allows the data providing method to be optimized by taking market value into account. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input market value data into AI and have the AI ​​adjust the data providing method.

[0096] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, the analysis unit can relax the analysis criteria when the user is stressed. The analysis unit can also tighten the analysis criteria when the user is relaxed. The analysis unit can also prioritize analysis of only important data when the user is in a hurry. This allows the analysis criteria to be adjusted according to the user's emotions, improving the accuracy of the analysis. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI. For example, the analysis unit can input the user's emotion data into AI and have the AI ​​adjust the analysis criteria.

[0097] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data. Examples of interrelationships between data include, but are not limited to, correlation analysis and identification of causal relationships. For example, the analysis unit performs analysis by taking into account the interrelationships between location information and speed data. The analysis unit can also perform analysis by taking into account the interrelationships between driving routes and fuel efficiency data. The analysis unit can also perform analysis by taking into account the interrelationships between environmental data and driving conditions. In this way, by taking into account the interrelationships between data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit is performed using AI. For example, the analysis unit can input the interrelationships between data into AI and have the AI ​​improve the accuracy of the analysis.

[0098] During analysis, the analysis unit can perform the analysis while taking into consideration the attribute information of the data provider. The attribute information of the provider includes, for example, age, gender, occupation, etc., but is not limited to these examples. The analysis unit performs the analysis while taking into consideration, for example, the age and gender of the data provider. The analysis unit can also perform the analysis while taking into consideration the driving experience of the data provider. The analysis unit can also perform the analysis while taking into consideration the type of vehicle and usage conditions of the data provider. In this way, by taking into consideration the attribute information of the provider, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using AI. For example, the analysis unit can input the attribute information of the provider into AI and have the AI ​​perform the analysis.

[0099] During analysis, the analysis unit can weight the analysis based on the frequency of data provision. Examples of the frequency of data provision include, but are not limited to, the number of times and duration of data provision. For example, the analysis unit may assign a higher weight to frequently provided data during analysis. The analysis unit may also assign a lower weight to rarely provided data during analysis. The analysis unit may also dynamically adjust the weighting of the analysis according to the frequency of data provision. Examples of weighting include, but are not limited to, weighting based on frequency and importance. By weighting based on the frequency of provision, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit can be performed using AI. For example, the analysis unit can input the provision frequency data into AI and have the AI ​​perform the weighting.

[0100] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user emotions. For example, when the user is feeling stressed, the analysis unit can prioritize displaying important analysis results. Furthermore, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can prioritize displaying key analysis results. This allows important information to be prioritized by adjusting the display order according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI. For example, the analysis unit can input the user's emotion data into AI and have the AI ​​adjust the display order.

[0101] The analysis unit can perform the analysis while taking into account the geographical distribution of the data. Geographical distribution includes, for example, data distribution by region, map information, etc., but is not limited to these examples. For example, the analysis unit can prioritize analyzing data from urban areas to identify traffic congestion patterns. The analysis unit can also prioritize analyzing data from suburban areas to improve fuel efficiency. The analysis unit can also prioritize analyzing data from mountainous areas to evaluate road safety. In this way, by taking geographical distribution into consideration, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using AI. For example, the analysis unit can input geographical distribution data into AI and have the AI ​​perform the analysis.

[0102] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the data. Examples of related literature include, but are not limited to, academic papers and technical reports. For example, the analysis unit can refer to the latest research papers related to the data to be analyzed. The analysis unit can also refer to patent documents related to the data to be analyzed. The analysis unit can also refer to technical literature related to the data to be analyzed. By referring to related literature, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit can be performed using AI. For example, the analysis unit can input related literature data into AI and have the AI ​​improve the accuracy of the analysis.

[0103] The analysis unit can perform the analysis while taking into account the market value of the data. Market value includes, for example, data demand, pricing, etc., but is not limited to these examples. For example, the analysis unit prioritizes the analysis of high-value data. The analysis unit can also analyze data of medium value with medium priority. The analysis unit can also analyze data of low value with low priority. In this way, by taking market value into consideration, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using AI. For example, the analysis unit can input market value data into AI and have the AI ​​perform the analysis.

[0104] The improvement unit can estimate the user's emotions and adjust the improvement method of the autonomous driving system based on the estimated user emotions. For example, the improvement unit can reduce the frequency of system improvements when the user is stressed. The improvement unit can also increase the frequency of system improvements when the user is relaxed. The improvement unit can also prioritize only important improvements when the user is in a hurry. This reduces the burden on the user by adjusting the improvement method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the improvement unit can be performed using, for example, an AI, or without an AI. For example, the improvement unit can input the user's emotion data into an AI and have the AI ​​adjust the improvement method.

[0105] When making an improvement, the improvement unit can select the optimal improvement method by referring to past improvement data. Past improvement data includes, for example, past bug fix history and user feedback, but is not limited to these examples. The improvement unit selects the optimal improvement method based on, for example, the past improvement data. The improvement unit can also extract effective improvement methods from the past improvement data. The improvement unit can also analyze the past improvement data and select the most efficient improvement method. In this way, the optimal improvement method can be selected by referring to the past improvement data. Some or all of the above-mentioned processing in the improvement unit may be performed, for example, using AI, or may be performed without using AI. For example, the improvement unit can input past improvement data into AI and have the AI ​​select an improvement method.

[0106] The improvement unit can update the improvement method by reflecting user feedback when making an improvement. User feedback includes, but is not limited to, survey results and review comments, for example. The improvement unit updates the improvement method based on, for example, user feedback. The improvement unit can also extract effective improvement methods from user feedback. The improvement unit can also analyze user feedback and select the most efficient improvement method. This allows the improvement method to be optimized by reflecting user feedback. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input user feedback data into AI and have the AI ​​update the improvement method.

[0107] When making improvements, the improvement unit can make improvements taking into account attribute information of the data provider. Attribute information of the provider includes, for example, age, gender, occupation, etc., but is not limited to these examples. The improvement unit can make improvements taking into account, for example, the age and gender of the data provider. The improvement unit can also make improvements taking into account the driving experience of the data provider. The improvement unit can also make improvements taking into account the vehicle type and usage conditions of the data provider. In this way, by taking into account the attribute information of the provider, the accuracy of the improvements can be improved. Some or all of the above-mentioned processing in the improvement unit can be performed using, for example, AI, or without using AI. For example, the improvement unit can input the attribute information of the provider into AI and have the AI ​​execute the improvements.

[0108] The improvement unit can estimate the user's emotions and determine the priority of improvements based on the estimated user emotions. For example, if the user is feeling stressed, the improvement unit can prioritize only important improvements. Furthermore, if the user is relaxed, the improvement unit can prioritize detailed improvements. Furthermore, if the user is in a hurry, the improvement unit can prioritize improvements that focus on the essential points. Thus, by determining the priority of improvements according to the user's emotions, important improvements can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the improvement unit can be performed using, for example, an AI, or without an AI. For example, the improvement unit can input the user's emotion data into an AI and have the AI ​​determine the priority of improvements.

[0109] The improvement unit can make improvements by taking into account the geographical distribution of the data. Geographical distribution includes, for example, data distribution by region, map information, etc., but is not limited to these examples. For example, the improvement unit can prioritize improving data in urban areas to identify traffic congestion patterns. The improvement unit can also prioritize improving data in suburban areas to improve fuel efficiency. The improvement unit can also prioritize improving data in mountainous areas to evaluate road safety. In this way, by taking geographical distribution into consideration, the accuracy of the improvement can be improved. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input geographical distribution data into AI and have the AI ​​perform the improvement.

[0110] During improvement, the improvement unit can improve the accuracy of the improvement by referring to literature related to the data. Examples of related literature include, but are not limited to, academic papers and technical reports. For example, the improvement unit can refer to the latest research papers related to the data to be improved. The improvement unit can also refer to patent documents related to the data to be improved. The improvement unit can also refer to technical literature related to the data to be improved. By doing so, the accuracy of the improvement can be improved by referring to the related literature. Some or all of the above-described processing in the improvement unit may be performed using, or without, AI. For example, the improvement unit can input related literature data into AI and have the AI ​​improve the accuracy of the improvement.

[0111] When making improvements, the improvement unit can take into account the market value of the data. Market value includes, but is not limited to, for example, data demand and pricing. For example, the improvement unit prioritizes improving high-value data. The improvement unit can also improve medium-value data with medium priority. The improvement unit can also improve low-value data with low priority. This allows for improved accuracy of improvements by taking market value into consideration. Some or all of the above-described processing in the improvement unit may be performed using, or without, AI. For example, the improvement unit can input market value data into AI and have the AI ​​perform the improvements.

[0112] The reward unit can estimate the user's emotions and adjust the reward payment method based on the estimated user emotions. For example, if the user is feeling stressed, the reward unit selects a quick payment method. The reward unit can also provide a detailed payment method if the user is relaxed. The reward unit can also provide a simple payment method if the user is in a hurry. This allows the payment method to be adjusted according to the user's emotions, thereby improving user satisfaction. The emotion estimation is achieved using an emotion estimation function, for example, 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 such examples. Some or all of the above-described processing in the reward unit can be performed using an AI, or without an AI. For example, the reward unit can input the user's emotion data into an AI and have the AI ​​adjust the payment method.

[0113] The compensation unit can select the optimal payment method by referring to past compensation data when paying compensation. Past compensation data includes, for example, past payment history and compensation evaluation, but is not limited to these examples. The compensation unit selects the optimal payment method based on, for example, past compensation data. The compensation unit can also extract effective payment methods from past compensation data. The compensation unit can also analyze past compensation data and select the most efficient payment method. In this way, the optimal payment method can be selected by referring to past compensation data. Some or all of the above-described processing in the compensation unit may be performed using, for example, AI, or may be performed without using AI. For example, the compensation unit can input past compensation data into AI and have the AI ​​select the payment method.

[0114] The reward unit can update the payment method by reflecting user feedback when paying rewards. User feedback includes, but is not limited to, survey results and review comments. The reward unit updates the payment method, for example, based on the user feedback. The reward unit can also extract effective payment methods from the user feedback. The reward unit can also analyze the user feedback and select the most efficient payment method. This allows the payment method to be optimized by reflecting the user feedback. Some or all of the above-described processing in the reward unit may be performed using, for example, AI, or may be performed without using AI. For example, the reward unit can input user feedback data into AI and have the AI ​​update the payment method.

[0115] When paying rewards, the reward unit can weight the rewards based on the frequency of data provision. Examples of the frequency of data provision include, but are not limited to, the number of times and duration of data provision. For example, the reward unit can pay a higher reward to a user who frequently provides data. The reward unit can also pay a lower reward to a user who infrequently provides data. The reward unit can also dynamically adjust the weighting of the rewards according to the frequency of data provision. Examples of weighting include, but are not limited to, weighting based on frequency and weighting based on importance. Thus, weighting based on the frequency of data provision can improve the fairness of rewards. Some or all of the above-described processing in the reward unit may be performed using, or without, AI. For example, the reward unit can input data on the frequency of data provision into AI and have the AI ​​perform the weighting.

[0116] The reward unit can estimate the user's emotions and determine the priority of rewards based on the estimated user emotions. For example, if the user is stressed, the reward unit can prioritize quick payments. Also, if the user is relaxed, the reward unit can prioritize detailed payments. Also, if the user is in a hurry, the reward unit can prioritize concise payments. This allows for reward prioritization based on the user's emotions, thereby improving user satisfaction. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reward unit can be performed using, for example, an AI, or without an AI. For example, the reward unit can input the user's emotion data into an AI and have the AI ​​determine the reward priorities.

[0117] The reward unit can adjust the payment method taking into account the geographic distribution of the data when paying rewards. Examples of geographic distribution include, but are not limited to, regional data distribution and map information. For example, the reward unit can provide a quick payment method to data providers in urban areas. The reward unit can also provide a detailed payment method to data providers in suburban areas. The reward unit can also provide a simple payment method to data providers in mountainous areas. Examples of payment methods include, but are not limited to, bank transfer, electronic money, points, etc. This allows the payment method to be optimized by taking geographic distribution into account. Some or all of the above-described processing in the reward unit may be performed using, or without, AI. For example, the reward unit can input geographic distribution data into AI and have the AI ​​adjust the payment method.

[0118] When paying a reward, the reward unit may refer to literature related to the data to improve the accuracy of the payment. Examples of related literature include, but are not limited to, academic papers and technical reports. For example, the reward unit may refer to the latest research papers related to the payment. The reward unit may also refer to patent documents related to the payment. The reward unit may also refer to technical literature related to the payment. Examples of payment accuracy include, but are not limited to, the accuracy and completeness of the data. By referring to the literature, the accuracy of the payment can be improved. Some or all of the above-described processing in the reward unit may be performed using, or without, AI. For example, the reward unit may input related literature data into AI and have the AI ​​improve the accuracy of the payment.

[0119] When paying rewards, the reward unit can adjust the payment method taking into account the market value of the data. Market value includes, but is not limited to, data demand, pricing, etc. For example, the reward unit can pay a high reward to a data provider of high value. The reward unit can also pay a medium reward to a data provider of medium value. The reward unit can also pay a low reward to a data provider of low value. Payment methods include, but are not limited to, bank transfer, electronic money, points, etc. This allows the payment method to be optimized by taking market value into account. Some or all of the above-mentioned processing in the reward unit may be performed using, for example, AI, or may be performed without using AI. For example, the reward unit can input market value data into AI and have the AI ​​adjust the payment method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, provision unit, analysis unit, improvement unit, and reward unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects driving data using sensors and GPS of the smart device 14. For example, the provision unit encrypts the data collected by the specific processing unit 290 of the data processing device 12 and transfers it to an autonomous driving system company using a secure communication protocol. For example, the analysis unit analyzes the data using AI by the specific processing unit 290 of the data processing device 12. For example, the improvement unit improves the autonomous driving system based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the reward unit can pay rewards to data providers using an electronic payment system by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, provision unit, analysis unit, improvement unit, and reward unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects driving data using sensors and GPS of the smart glasses 214. For example, the provision unit encrypts the data collected by the specific processing unit 290 of the data processing device 12 and transfers it to an autonomous driving system company using a secure communication protocol. For example, the analysis unit analyzes the data using AI by the specific processing unit 290 of the data processing device 12. For example, the improvement unit improves the autonomous driving system based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the reward unit can pay rewards to data providers using an electronic payment system by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, provision unit, analysis unit, improvement unit, and reward unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects driving data using sensors and GPS of the headset type terminal 314. For example, the provision unit encrypts the data collected by the specific processing unit 290 of the data processing device 12 and transfers it to the autonomous driving system company using a secure communication protocol. For example, the analysis unit analyzes the data using AI by the specific processing unit 290 of the data processing device 12. For example, the improvement unit improves the autonomous driving system based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the reward unit can pay rewards to data providers using an electronic payment system by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, provision unit, analysis unit, improvement unit, and reward unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects driving data using sensors and GPS of the robot 414. For example, the provision unit encrypts the data collected by the specific processing unit 290 of the data processing device 12 and transfers it to an autonomous driving system company using a secure communication protocol. For example, the analysis unit analyzes the data using AI by the specific processing unit 290 of the data processing device 12. For example, the improvement unit improves the autonomous driving system based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the reward unit can pay rewards to data providers using an electronic payment system by the control unit 46A of the robot 414.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] In addition to vehicle driving data, the collection unit can also collect behavior data of occupants in the vehicle. For example, it uses cameras and sensors to collect information on the seat positions of occupants and whether or not they are wearing their seat belts. The collection unit can also analyze the occupant behavior data and evaluate safety while driving. For example, it can issue a warning if an occupant is not wearing a seat belt. It can also detect a decrease in attention while driving based on the occupant behavior data and issue a warning. This can improve safety inside the vehicle.

[0122] When providing collected data, the provider can customize the method of providing it depending on the purpose of use of the data. For example, if the data is provided for research purposes, it can include detailed analysis results of the data. If the data is provided for commercial purposes, it can provide data summaries and statistical information. The provider can also adjust the form and format of the data according to the needs of the data user. This allows the data to be provided in the most optimal form for the data user.

[0123] When analyzing data, the analysis unit can improve the accuracy of the analysis by referring to external weather data. For example, when analyzing driving data in the rain, the analysis unit can refer to weather data and take into account the slipperiness of the road. The analysis unit can also analyze fluctuations in vehicle performance and fuel efficiency based on weather data such as temperature and humidity. This allows for more accurate analysis results by taking weather conditions into account when performing analysis.

[0124] The improvement unit can improve the user interface of the autonomous driving system based on the analysis results. For example, it can optimize the layout of the operation screen based on the user operation patterns obtained from the analysis results. The improvement unit can also make improvements to improve operability by reflecting user feedback. For example, it can place functions that users use frequently in positions that make them easier to operate. This can improve user operability.

[0125] The reward unit can diversify the payment methods for rewards to data providers. For example, rewards can be paid not only in cash or electronic money, but also in the form of points or coupons. The reward unit can also allow data providers to select the payment method for rewards according to their preferences. For example, the data provider can register their preferred payment method in advance and rewards can be paid using that method. This can improve the satisfaction of data providers.

[0126] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the types of data to be collected can be reduced to reduce the burden on the user. Also, if the user is relaxed, detailed data can be collected. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data. In this way, the burden on the user can be reduced by adjusting the type of data to be collected according to the user's emotions.

[0127] The providing unit can estimate the user's emotions and adjust the frequency of data provision based on the estimated user's emotions. For example, if the user is feeling stressed, the frequency of data provision can be reduced. Also, if the user is relaxed, the frequency of data provision can be increased. Also, if the user is in a hurry, only important data can be provided preferentially. In this way, the burden on the user can be reduced by adjusting the frequency of data provision according to the user's emotions.

[0128] 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, if the user is feeling stressed, the analysis results can be displayed concisely. If the user is relaxed, detailed analysis results can be displayed. If the user is in a hurry, analysis results that focus on the main points can be displayed. In this way, the burden on the user can be reduced by adjusting the display method of the analysis results according to the user's emotions.

[0129] The improvement unit can estimate the user's emotions and adjust the improvements to the autonomous driving system based on the estimated user emotions. For example, if the user is feeling stressed, the improvement details can be simplified. Also, if the user is relaxed, detailed improvement details can be provided. Also, if the user is in a hurry, only important improvement details can be provided preferentially. In this way, the burden on the user can be reduced by adjusting the improvement details according to the user's emotions.

[0130] The reward unit can estimate the user's emotions and adjust the timing of reward payment based on the estimated user's emotions. For example, if the user is feeling stressed, the reward can be paid quickly. If the user is relaxed, detailed reward details can be provided. If the user is in a hurry, brief reward details can be provided. In this way, by adjusting the timing of reward payment according to the user's emotions, user satisfaction can be improved.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The collection unit collects vehicle driving data. This data includes speed, location information, acceleration, and other information. The collection unit collects driving data using sensors and GPS installed in the vehicle, and can dynamically change the type and frequency of data collection depending on the driving situation. For example, when driving on a highway, data on speed and vehicle distance is collected frequently. Step 2: The provider provides the collected data to the autonomous driving system company. The provider encrypts the collected data and transfers it to the autonomous driving system company using a secure communication protocol. Algorithms such as AES and RSA are used for encryption. Step 3: The analysis unit uses AI to analyze the provided data. The analysis unit uses techniques such as deep learning and machine learning to identify traffic patterns and dangerous spots. For example, it analyzes the frequency of traffic congestion and accidents on specific roads. Step 4: The Improvement Department improves the autonomous driving system based on the analysis results obtained by the Analysis Department. The Improvement Department determines which parts of the system to improve and the evaluation criteria for the improvements. Step 5: The compensation unit pays compensation to the data provider based on the data provided by the provider. The compensation unit pays compensation to the data provider using an electronic payment system.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects vehicle driving data; a providing unit that provides the data collected by the collecting unit to an autonomous driving system company; an analysis unit that analyzes the data provided by the provision unit; an improvement unit that improves the autonomous driving system based on the analysis results obtained by the analysis unit; a reward unit that pays a reward to a data provider based on the data provided by the providing unit. A system characterized by:

2. The collecting unit Collect driving data using sensors or GPS mounted on the vehicle 2. The system of claim 1.

3. The providing unit The collected data will be encrypted and transmitted to the autonomous driving system company using a secure communication protocol.

2. The system of claim 1.

4. The analysis unit AI is used to analyze data and identify traffic patterns and dangerous spots.

2. The system of claim 1.

5. The improvement unit Improve the automated driving system based on the analysis results obtained by the analysis unit 2. The system of claim 1.

6. The reward unit: Remuneration will be paid to data providers using an electronic payment system 2. The system of claim 1.

7. The collecting unit Estimates user emotions and adjusts the timing of collecting driving data based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Dynamically change the type and frequency of data collected depending on the vehicle's driving situation 2. The system of claim 1.

Images