system

Abstract

The system according to the embodiment aims to generate advanced information based on input from the user and provide a safe and comfortable car life. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input from a user. The generating unit analyzes the information received by the receiving unit and generates necessary information. The providing unit provides the information generated by the generating unit to the user.

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] With conventional technology, the functions of car navigation systems and drive recorders were limited, and there was a need for more advanced information provision and safety support.

[0005] The system according to the embodiment aims to generate advanced information based on input from the user and provide a safe and comfortable car life. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input from a user. The generating unit analyzes the information received by the receiving unit and generates necessary information. The providing unit provides the information generated by the generating unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment generates advanced information based on input from the user, and can provide a safe and comfortable car life. [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) The car life support system of this embodiment of the present invention makes car life safer by incorporating a generation AI into conventional car navigation systems and dashcams. This system offers augmented reality (AR) display function, hazard prediction support function using image analysis and self-learning, speed adjustment and fuel economy calculation function using image analysis, driving scoring function, and automatic identification and reporting of dangerous driving. For example, the AR display function displays information superimposed on the actual scenery on the car navigation screen, allowing the driver to intuitively understand the direction of travel and important points to watch out for. Next, in the hazard prediction support function using image analysis and self-learning, the generation AI analyzes video footage captured by a camera and predicts dangerous situations. In the speed adjustment and fuel economy calculation function using image analysis, the generation AI analyzes the driver's driving behavior and suggests optimal speeds and fuel economy. Furthermore, in the driving scoring function, the generation AI evaluates the driver's driving and scores the driving skills of senior drivers. Finally, the generation AI automatically identifies and reports dangerous driving, such as tailgating. This system enables drivers to enjoy a safer and more comfortable car life. This enables car life assistance systems to improve driver safety and provide a comfortable driving experience.

[0029] A car life assistance system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input from a user. The input from the user includes, but is not limited to, voice input, text input, and touch input, for example. The reception unit receives voice input from the user using, for example, voice recognition technology. The reception unit can also receive touch input from the user using a touch screen. The reception unit can also include a keyboard or software keyboard for receiving text input. The generation unit analyzes the information received by the reception unit and generates necessary information. The generation unit can analyze voice input from the user using, for example, natural language processing technology and generate the necessary information. The generation unit can also analyze video captured by a camera using image analysis technology and generate the necessary information. The generation unit can also analyze a user's past input history using data mining technology and generate the necessary information. The provision unit provides the information generated by the generation unit to the user. The provision unit can provide the information to the user using, for example, a screen display. The provision unit can also provide the information to the user using voice guidance. Furthermore, the providing unit can provide information to the user by sending an email. As a result, the car life assistance system according to the embodiment can easily make car life safe by accepting and analyzing input from the user, and generating and providing necessary information.

[0030] The car life support system includes an analysis unit that analyzes video captured by a camera. The analysis unit analyzes the video captured by the camera. The analysis unit analyzes the video using, for example, an image recognition algorithm. For example, the analysis unit detects vehicles and pedestrians from the video captured by the camera and identifies their locations. The analysis unit can also analyze the video using a machine learning model. For example, the analysis unit detects road signs and traffic lights from the video captured by the camera and analyzes the information. The analysis unit can also analyze the video using deep learning technology. For example, the analysis unit predicts dangerous situations from the video captured by the camera and issues a warning to the driver. As a result, the analysis unit can realize functions such as hazard prediction and speed adjustment by analyzing the video captured by the camera.

[0031] The car life assistance system includes a learning unit that collects driving data from the driver and allows the generation AI to self-learn. The learning unit collects driving data from the driver and the generation AI self-learns. The driving data includes, but is not limited to, speed, frequency of brake use, and steering operation. The learning unit collects driving data using, for example, a vehicle sensor. For example, the learning unit collects speed data using a vehicle speed sensor. The learning unit can also collect brake use frequency using a brake sensor. The learning unit can also collect steering operation data using a steering operation sensor. The generation AI self-learns based on the collected driving data. The generation AI self-learns using, for example, a reinforcement learning algorithm. For example, the generation AI learns optimal driving methods based on the driving data. The generation AI can also self-learn using a deep learning algorithm. For example, the generation AI learns how to predict dangerous situations based on the driving data. As a result, the learning unit allows the generation AI to self-learn, thereby improving the accuracy of the system.

[0032] The car life assistance system includes a reporting unit that detects dangerous driving and reports it to the police. The reporting unit detects dangerous driving and reports it to the police. Dangerous driving includes, for example, sudden braking, sudden acceleration, lane departure, etc., but is not limited to these examples. The reporting unit detects dangerous driving using, for example, a vehicle sensor. For example, the reporting unit detects sudden braking using a sudden braking sensor. The reporting unit can also detect sudden acceleration using a sudden acceleration sensor. The reporting unit can also detect lane departure using a lane departure sensor. When the reporting unit detects dangerous driving, it reports it to the police. For example, the reporting unit reports to the police using an automatic reporting system. For example, when the reporting unit detects dangerous driving, it automatically reports it to the police. The reporting unit can also notify an emergency contact. For example, when the reporting unit detects dangerous driving, it notifies an emergency contact. In this way, the reporting unit detects dangerous driving and reports it to the police, thereby ensuring the safety of the driver.

[0033] The providing unit can be equipped with an AR screen providing function. The AR screen providing function displays information superimposed on actual scenery on the car navigation screen. For example, the providing unit displays AR information using a head-up display. The head-up display projects information onto the windshield, allowing the driver to check the information without moving their line of sight. The providing unit can also display AR information using a smartphone app. The smartphone app displays information by superimposing it on images captured by a camera. The providing unit can also display AR information using an in-vehicle display. The in-vehicle display is installed on the dashboard, allowing the driver to check the information without moving their line of sight. In this way, the providing unit's AR screen providing function allows the driver to intuitively understand the direction of travel and points of caution.

[0034] The providing unit can be equipped with a speed adjustment and fuel economy calculation function based on image analysis. In the speed adjustment and fuel economy calculation function based on image analysis, the generation AI analyzes the driver's driving conditions and suggests the optimal speed and fuel economy. For example, the providing unit analyzes images using computer vision technology and adjusts speed. Computer vision technology detects road signs and traffic lights from video captured by a camera and adjusts speed based on that information. The providing unit can also analyze images using deep learning technology and calculate fuel economy. Deep learning technology analyzes road conditions and traffic volume from video captured by a camera and calculates fuel economy based on that information. Furthermore, the providing unit can analyze images using the generation AI and suggest eco-driving methods. The generation AI analyzes the driver's driving style from video captured by a camera and suggests the optimal eco-driving method. This allows the providing unit to suggest eco-driving using the speed adjustment and fuel economy calculation function based on image analysis.

[0035] The providing unit may have a driving scoring function. In the driving scoring function, the generation AI evaluates the driver's driving and scores the driving skills of the senior driver. For example, the providing unit scores the driving based on driving skill evaluation criteria. The driving skill evaluation criteria include, but are not limited to, speed, frequency of brake use, steering operation, etc. The providing unit analyzes the driving data using the generation AI and evaluates the driving skills. The generation AI evaluates the driving skills based on speed data, for example. The generation AI can also evaluate the driving skills based on frequency of brake use. Furthermore, the generation AI can evaluate the driving skills based on steering operation data. The providing unit can notify relatives of the driving skill evaluation results. For example, the providing unit notifies relatives by sending an email. As a result, the providing unit can evaluate the senior driver's driving skills using the driving scoring function and notify the relatives.

[0036] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit analyzes the user's past input history and selects the optimal reception method. The past input history includes, but is not limited to, past input content, input frequency, input pattern, etc. The reception unit selects the optimal reception method based on, for example, the past input content. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also select the optimal reception method based on the past input frequency. For example, the reception unit predicts and suggests an input method to be used in a specific time period based on the user's past input frequency. Furthermore, the reception unit can select the optimal reception method based on the past input pattern. For example, the reception unit automatically completes related input items based on information previously input by the user. In this way, the reception unit can select the optimal reception method by analyzing the user's past input history.

[0037] The reception unit may perform filtering based on the user's current driving situation and areas of interest when receiving input. The reception unit may perform filtering based on the user's current driving situation and areas of interest when receiving input. Examples of driving situations include, but are not limited to, speed, traffic volume, and weather. For example, when a user is driving on a highway, the reception unit provides a simple input interface to allow the user to concentrate on driving. For example, when a user is driving in an urban area, the reception unit provides detailed input options to allow the user to quickly input necessary information. Furthermore, when a user performs input based on a specific area of ​​interest (e.g., eco-driving), information related to that area is preferentially displayed. Examples of areas of interest include, but are not limited to, hobbies, past search history, and social media activity. This allows the reception unit to provide more appropriate information by filtering based on the user's driving situation and areas of interest.

[0038] The reception unit can prioritize receiving highly relevant information based on the user's geographical location information when receiving input. The reception unit prioritizes receiving highly relevant information based on the user's geographical location information when receiving input. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, when the user is in a specific area, the reception unit prioritizes receiving information related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving information around the user's home. In this way, the reception unit can prioritize receiving highly relevant information by taking the user's geographical location information into consideration.

[0039] The reception unit can analyze the user's social media activity and receive related information when receiving input. The reception unit can analyze the user's social media activity and receive related information when receiving input. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The reception unit can, for example, automatically complete related input items based on information shared by the user on social media. The reception unit can also preferentially receive related information based on topics in which the user has shown interest on social media. Furthermore, the reception unit can also receive information related to specific events or places from the user's social media activity. In this way, the reception unit can preferentially receive related information by analyzing the user's social media activity.

[0040] The generation unit can adjust the level of detail of the generated information based on the importance of the driving situation when generating the information. The generation unit adjusts the level of detail of the generated information based on the importance of the driving situation when generating the information. Driving situations include, but are not limited to, for example, speed, traffic volume, and weather. For example, when driving on a highway, the generation unit can prioritize generating concise and important information. When driving in an urban area, the generation unit can also generate detailed information and provide it to the driver. Furthermore, when parking, the generation unit can generate all information in detail and provide it to the driver. In this way, the generation unit can provide more appropriate information by adjusting the level of detail of the generated information based on the importance of the driving situation.

[0041] The generation unit can apply different generation algorithms depending on the driving category when generating information. The generation unit applies different generation algorithms depending on the driving category when generating information. Driving categories include, but are not limited to, urban driving, highway driving, and mountain road driving, for example. For example, in the case of eco-driving, the generation unit can prioritize generating information about fuel efficiency. In addition, in the case of sport driving, the generation unit can also prioritize generating information about speed and acceleration. Furthermore, in the case of city driving, the generation unit can prioritize generating traffic conditions and parking information. In this way, the generation unit can provide more appropriate information by applying different generation algorithms depending on the driving category.

[0042] The generation unit can determine the priority of generation based on the time of submission of driving data when generating information. The generation unit can determine the priority of generation based on the time of submission of driving data when generating information. The time of submission of driving data includes, for example, real-time data, past data, etc., but is not limited to these examples. The generation unit, for example, preferentially analyzes the latest driving data and generates information. It can also generate related information by referring to past driving data. Furthermore, it can generate information based on driving data submitted during a specific time period. In this way, the generation unit can provide more appropriate information by determining the priority of generation based on the time of submission of driving data.

[0043] The generation unit can adjust the order of generation based on the relevance of the driving data when generating information. The generation unit adjusts the order of generation based on the relevance of the driving data when generating information. The relevance of the driving data includes, for example, data correlation, co-occurrence frequency, etc., but is not limited to these examples. The generation unit, for example, preferentially analyzes the most relevant driving data and generates information. It can also postpone driving data with low relevance and generate important information preferentially. Furthermore, it can adjust the order of generation of information based on the relevance of the driving data. In this way, the generation unit can provide more appropriate information by adjusting the order of generation based on the relevance of the driving data.

[0044] The providing unit can adjust the level of detail to be provided based on the importance of the driving situation when providing information. The providing unit adjusts the level of detail to be provided based on the importance of the driving situation when providing information. The driving situation includes, but is not limited to, for example, speed, traffic volume, and weather. For example, when driving on a highway, the providing unit can prioritize providing concise and important information. When driving in an urban area, the providing unit can also provide detailed information to the driver. Furthermore, when parking, the providing unit can provide all information in detail to the driver. In this way, the providing unit can provide more appropriate information by adjusting the level of detail to be provided based on the importance of the driving situation.

[0045] The providing unit can apply different provision algorithms depending on the driving category when providing information. The providing unit applies different provision algorithms depending on the driving category when providing information. Driving categories include, but are not limited to, urban driving, highway driving, and mountain road driving, for example. For example, in the case of eco-driving, the providing unit can prioritize providing information about fuel efficiency. In addition, in the case of sporty driving, the providing unit can also prioritize providing information about speed and acceleration. Furthermore, in the case of city driving, the providing unit can prioritize providing traffic conditions and parking information. In this way, the providing unit can provide more appropriate information by applying different provision algorithms depending on the driving category.

[0046] The providing unit can determine the priority of provision based on the time of submission of driving data when providing information. The providing unit can determine the priority of provision based on the time of submission of driving data when providing information. The time of submission of driving data includes, for example, real-time data, past data, etc., but is not limited to these examples. The providing unit, for example, preferentially analyzes the latest driving data and provides information. It can also provide related information by referring to past driving data. Furthermore, it can provide information based on driving data submitted in a specific time period. In this way, the providing unit can provide more appropriate information by determining the priority of provision based on the time of submission of driving data.

[0047] The providing unit can adjust the order of provision of information based on the relevance of driving data when providing information. The providing unit adjusts the order of provision of information based on the relevance of driving data when providing information. The relevance of driving data includes, for example, data correlation, co-occurrence frequency, etc., but is not limited to such examples. The providing unit, for example, preferentially analyzes the most relevant driving data and provides the information. It can also postpone driving data with low relevance and provide important information preferentially. Furthermore, it can adjust the order of provision of information based on the relevance of driving data. In this way, the providing unit can provide more appropriate information by adjusting the order of provision based on the relevance of driving data.

[0048] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships between the driving data. During analysis, the analysis unit improves the accuracy of the analysis based on the interrelationships between the driving data. Interrelationships between the driving data include, for example, data correlations and co-occurrence frequencies, but are not limited to these examples. For example, the analysis unit analyzes the interrelationships between the driving data and predicts dangerous situations. The analysis unit can also propose an optimal driving method by taking into account the interrelationships between the driving data. Furthermore, the analysis unit can optimize fuel efficiency based on the interrelationships between the driving data. As a result, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the driving data.

[0049] The analysis unit can perform the analysis based on attribute information of the person who submitted the driving data during the analysis. The analysis unit can perform the analysis based on attribute information of the person who submitted the driving data during the analysis. The attribute information of the person who submitted the driving data includes, for example, age, gender, driving experience, etc., but is not limited to these examples. The analysis unit can adjust the analysis criteria for the driving data based on, for example, the age and gender of the person who submitted the driving data. The analysis unit can also adjust the level of detail of the analysis based on the driving experience of the person who submitted the driving data. Furthermore, the analysis result of the driving data can be provided based on the health condition of the person who submitted the driving data. This allows the analysis unit to perform a more appropriate analysis by taking into account the attribute information of the person who submitted the driving data.

[0050] The analysis unit can perform the analysis based on the geographical distribution of the driving data during the analysis. The analysis unit performs the analysis based on the geographical distribution of the driving data during the analysis. Examples of the geographical distribution include, but are not limited to, GPS data, geographic information systems (GIS), etc. The analysis unit, for example, prioritizes the analysis of driving data in a specific region to understand driving trends specific to the region. The analysis unit can also classify the analysis results of the driving data by region based on the geographical distribution. Furthermore, the analysis accuracy of the driving data can be improved by taking the geographical distribution into consideration. This allows the analysis unit to perform more appropriate analysis by taking the geographical distribution of the driving data into consideration.

[0051] The analysis unit can improve the accuracy of the analysis by referring to literature related to the driving data during analysis. The analysis unit can improve the accuracy of the analysis by referring to literature related to the driving data during analysis. Relevant literature includes, but is not limited to, academic papers, technical reports, etc. For example, the analysis unit can improve the accuracy of the analysis by referring to relevant academic papers during analysis of the driving data. In addition, the analysis unit can optimize the analysis algorithm based on past research results. Furthermore, new analysis methods can be introduced based on the relevant literature. In this way, the analysis unit can improve the accuracy of the analysis by referring to literature related to the driving data.

[0052] The learning unit can adjust the learning algorithm by referring to past learning data during learning. The learning unit adjusts the learning algorithm by referring to past learning data during learning. Past learning data includes, but is not limited to, past training data sets and learning histories, for example. The learning unit adjusts parameters of the learning algorithm based on the past learning data, for example. The accuracy of learning can also be improved by referring to the past learning data. Furthermore, a new learning method can be introduced based on the past learning data. In this way, the learning unit can improve the accuracy of the learning algorithm by referring to the past learning data.

[0053] During learning, the learning unit can weight the learning data based on the time when the driving data was submitted. During learning, the learning unit weights the learning data based on the time when the driving data was submitted. The time when the driving data was submitted includes, for example, real-time data, past data, etc., but is not limited to these examples. The learning unit, for example, weights the most recent driving data during learning. Also, it can weight the learning data by referring to past driving data. Furthermore, it can weight the learning data based on driving data submitted during a specific time period. In this way, the learning unit weights the learning data based on the time when the driving data was submitted, enabling more appropriate learning.

[0054] The reporting unit can adjust the level of detail of the report based on the importance of the driving data when reporting. The reporting unit adjusts the level of detail of the report based on the importance of the driving data when reporting. The importance of the driving data includes, for example, the risk of an accident, driving safety, etc., but is not limited to these examples. For example, the reporting unit issues a detailed report when it detects serious dangerous driving. Also, the reporting unit can issue a brief report when it detects minor dangerous driving. Furthermore, the reporting unit can adjust the level of detail of the report based on the importance of the driving data. As a result, the reporting unit can adjust the level of detail of the report based on the importance of the driving data, thereby enabling more appropriate reporting.

[0055] The reporting unit can apply different reporting algorithms depending on the category of driving data when reporting. The reporting unit applies different reporting algorithms depending on the category of driving data when reporting. Driving data categories include, but are not limited to, speed data, braking data, and steering operation data, for example. For example, the reporting unit can immediately report if it detects aggressive driving. Furthermore, it can also report in detail if it detects speeding. Furthermore, it can adjust the reporting algorithm depending on the category of dangerous driving. This allows the reporting unit to apply different reporting algorithms depending on the category of driving data, enabling more appropriate reporting.

[0056] The reporting unit can determine the priority of a report by taking into account the geographical location information of the driving data when reporting. The reporting unit determines the priority of a report by taking into account the geographical location information of the driving data when reporting. Examples of geographical location information include, but are not limited to, GPS data, location information services, etc. The reporting unit, for example, prioritizes reporting of dangerous driving in a specific area. The reporting unit can also adjust the priority of a report based on the geographical location information. Furthermore, the reporting unit can also determine the priority of a report by taking into account the geographical location information. This allows the reporting unit to make more appropriate reports by taking into account the geographical location information of the driving data.

[0057] The reporting unit can improve the accuracy of the report by referring to literature related to the driving data when reporting. The reporting unit can improve the accuracy of the report by referring to literature related to the driving data when reporting. Relevant literature includes, but is not limited to, academic papers, technical reports, etc. For example, the reporting unit can improve the accuracy of the report by referring to relevant academic papers when reporting the driving data. In addition, the reporting unit can optimize the reporting algorithm based on past research results. Furthermore, a new reporting method can be introduced based on the relevant literature. In this way, the reporting unit can improve the accuracy of the report by referring to literature related to the driving data.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can analyze the user's driving style and suggest the optimal input method. For example, if the user frequently uses voice input, the reception unit can preferentially suggest voice input. Also, if the user prefers touch input, the reception unit can preferentially suggest touch input. Furthermore, if the user tends to use a specific input method during a specific time period, the reception unit can suggest the optimal input method for that time period. In this way, the reception unit can provide a more comfortable operation by suggesting the optimal input method according to the user's driving style.

[0060] The analysis unit can analyze the user's driving patterns based on the user's driving history and provide advice according to the predicted driving situation. For example, if the user has frequently used a specific route in the past, it can provide traffic information and cautions about that route. Also, if the user tends to drive during a specific time period, it can predict the traffic conditions during that time period and suggest the optimal driving method. Furthermore, it can provide advice on optimizing fuel efficiency and safe driving based on the user's driving style. In this way, the analysis unit can support safer and more efficient driving by providing appropriate advice based on the user's driving history.

[0061] The learning unit can generate a training program to support the improvement of the user's driving skills based on the user's driving data. For example, if the user has difficulty with a particular driving skill (e.g., lane changing, parking, etc.), it can provide a training program to improve that skill. It can also analyze the user's driving data, identify driving weaknesses, and provide advice to overcome those weaknesses. It can also evaluate the improvement of the user's driving skills and provide feedback on the progress. In this way, the learning unit can support the improvement of the user's driving skills, thereby achieving safer and more efficient driving.

[0062] The reporting unit can learn dangerous driving patterns based on the user's driving data and issue preventative reports. For example, if the user has frequently braked or accelerated suddenly in the past, the unit can learn those patterns and issue a warning before a similar situation occurs. Also, if the user has a tendency to engage in dangerous driving on specific roads or at specific times of the day, the unit can issue preventative reports based on that information. Furthermore, the unit can evaluate the risk of dangerous driving based on the user's driving style and notify the police or emergency contacts as necessary. In this way, the reporting unit can ensure the safety of the driver by preventing dangerous driving and taking early action.

[0063] The providing unit can evaluate the user's attention while driving based on the user's driving data and provide advice to maintain attention. For example, if the user has been driving for a long time, the providing unit can suggest a break to maintain attention. Also, if the user is tired, the providing unit can suggest exercises or refreshing methods to improve attention. Furthermore, the providing unit can provide advice to maintain attention based on the user's driving style. In this way, the providing unit can manage the user's attention and support safer driving.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives input from a user. The input from a user may be voice input, text input, touch input, or the like. For example, the reception unit may receive voice input from a user using voice recognition technology, touch input from a user using a touch screen, or text input from a keyboard or software keyboard. Step 2: The generation unit analyzes the information received by the reception unit and generates the necessary information. The generation unit can analyze the user's voice input using natural language processing technology, analyze video captured by a camera using image analysis technology, or analyze the user's past input history using data mining technology. Step 3: The providing unit provides the information generated by the generating unit to the user. The providing unit can provide the information by displaying the information on a screen, by providing audio guidance, or by sending an email.

[0066] (Example 2) The car life support system of this embodiment of the present invention makes car life safer by incorporating a generation AI into conventional car navigation systems and dashcams. This system offers augmented reality (AR) display function, hazard prediction support function using image analysis and self-learning, speed adjustment and fuel economy calculation function using image analysis, driving scoring function, and automatic identification and reporting of dangerous driving. For example, the AR display function displays information superimposed on the actual scenery on the car navigation screen, allowing the driver to intuitively understand the direction of travel and important points to watch out for. Next, in the hazard prediction support function using image analysis and self-learning, the generation AI analyzes video footage captured by a camera and predicts dangerous situations. In the speed adjustment and fuel economy calculation function using image analysis, the generation AI analyzes the driver's driving behavior and suggests optimal speeds and fuel economy. Furthermore, in the driving scoring function, the generation AI evaluates the driver's driving and scores the driving skills of senior drivers. Finally, the generation AI automatically identifies and reports dangerous driving, such as tailgating. This system enables drivers to enjoy a safer and more comfortable car life. This enables car life assistance systems to improve driver safety and provide a comfortable driving experience.

[0067] A car life assistance system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input from a user. The input from the user includes, but is not limited to, voice input, text input, and touch input, for example. The reception unit receives voice input from the user using, for example, voice recognition technology. The reception unit can also receive touch input from the user using a touch screen. The reception unit can also include a keyboard or software keyboard for receiving text input. The generation unit analyzes the information received by the reception unit and generates necessary information. The generation unit can analyze voice input from the user using, for example, natural language processing technology and generate the necessary information. The generation unit can also analyze video captured by a camera using image analysis technology and generate the necessary information. The generation unit can also analyze a user's past input history using data mining technology and generate the necessary information. The provision unit provides the information generated by the generation unit to the user. The provision unit can provide the information to the user using, for example, a screen display. The provision unit can also provide the information to the user using voice guidance. Furthermore, the providing unit can provide information to the user by sending an email. As a result, the car life assistance system according to the embodiment can easily make car life safe by accepting and analyzing input from the user, and generating and providing necessary information.

[0068] The car life support system includes an analysis unit that analyzes video captured by a camera. The analysis unit analyzes the video captured by the camera. The analysis unit analyzes the video using, for example, an image recognition algorithm. For example, the analysis unit detects vehicles and pedestrians from the video captured by the camera and identifies their locations. The analysis unit can also analyze the video using a machine learning model. For example, the analysis unit detects road signs and traffic lights from the video captured by the camera and analyzes the information. The analysis unit can also analyze the video using deep learning technology. For example, the analysis unit predicts dangerous situations from the video captured by the camera and issues a warning to the driver. As a result, the analysis unit can realize functions such as hazard prediction and speed adjustment by analyzing the video captured by the camera.

[0069] The car life assistance system includes a learning unit that collects driving data from the driver and allows the generation AI to self-learn. The learning unit collects driving data from the driver and the generation AI self-learns. The driving data includes, but is not limited to, speed, frequency of brake use, and steering operation. The learning unit collects driving data using, for example, a vehicle sensor. For example, the learning unit collects speed data using a vehicle speed sensor. The learning unit can also collect brake use frequency using a brake sensor. The learning unit can also collect steering operation data using a steering operation sensor. The generation AI self-learns based on the collected driving data. The generation AI self-learns using, for example, a reinforcement learning algorithm. For example, the generation AI learns optimal driving methods based on the driving data. The generation AI can also self-learn using a deep learning algorithm. For example, the generation AI learns how to predict dangerous situations based on the driving data. As a result, the learning unit allows the generation AI to self-learn, thereby improving the accuracy of the system.

[0070] The car life assistance system includes a reporting unit that detects dangerous driving and reports it to the police. The reporting unit detects dangerous driving and reports it to the police. Dangerous driving includes, for example, sudden braking, sudden acceleration, lane departure, etc., but is not limited to these examples. The reporting unit detects dangerous driving using, for example, a vehicle sensor. For example, the reporting unit detects sudden braking using a sudden braking sensor. The reporting unit can also detect sudden acceleration using a sudden acceleration sensor. The reporting unit can also detect lane departure using a lane departure sensor. When the reporting unit detects dangerous driving, it reports it to the police. For example, the reporting unit reports to the police using an automatic reporting system. For example, when the reporting unit detects dangerous driving, it automatically reports it to the police. The reporting unit can also notify an emergency contact. For example, when the reporting unit detects dangerous driving, it notifies an emergency contact. In this way, the reporting unit detects dangerous driving and reports it to the police, thereby ensuring the safety of the driver.

[0071] The providing unit can be equipped with an AR screen providing function. The AR screen providing function displays information superimposed on actual scenery on the car navigation screen. For example, the providing unit displays AR information using a head-up display. The head-up display projects information onto the windshield, allowing the driver to check the information without moving their line of sight. The providing unit can also display AR information using a smartphone app. The smartphone app displays information by superimposing it on images captured by a camera. The providing unit can also display AR information using an in-vehicle display. The in-vehicle display is installed on the dashboard, allowing the driver to check the information without moving their line of sight. In this way, the providing unit's AR screen providing function allows the driver to intuitively understand the direction of travel and points of caution.

[0072] The providing unit can be equipped with a speed adjustment and fuel economy calculation function based on image analysis. In the speed adjustment and fuel economy calculation function based on image analysis, the generation AI analyzes the driver's driving conditions and suggests the optimal speed and fuel economy. For example, the providing unit analyzes images using computer vision technology and adjusts speed. Computer vision technology detects road signs and traffic lights from video captured by a camera and adjusts speed based on that information. The providing unit can also analyze images using deep learning technology and calculate fuel economy. Deep learning technology analyzes road conditions and traffic volume from video captured by a camera and calculates fuel economy based on that information. Furthermore, the providing unit can analyze images using the generation AI and suggest eco-driving methods. The generation AI analyzes the driver's driving style from video captured by a camera and suggests the optimal eco-driving method. This allows the providing unit to suggest eco-driving using the speed adjustment and fuel economy calculation function based on image analysis.

[0073] The providing unit may have a driving scoring function. In the driving scoring function, the generation AI evaluates the driver's driving and scores the driving skills of the senior driver. For example, the providing unit scores the driving based on driving skill evaluation criteria. The driving skill evaluation criteria include, but are not limited to, speed, frequency of brake use, steering operation, etc. The providing unit analyzes the driving data using the generation AI and evaluates the driving skills. The generation AI evaluates the driving skills based on speed data, for example. The generation AI can also evaluate the driving skills based on frequency of brake use. Furthermore, the generation AI can evaluate the driving skills based on steering operation data. The providing unit can notify relatives of the driving skill evaluation results. For example, the providing unit notifies relatives by sending an email. As a result, the providing unit can evaluate the senior driver's driving skills using the driving scoring function and notify the relatives.

[0074] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user's emotion. The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user's emotion. The emotion can be estimated using, for example, facial expression recognition technology. For example, the reception unit can analyze the user's facial expression captured by a camera to estimate the emotion. The reception unit can also estimate the emotion using voice analysis technology. For example, the reception unit can analyze the user's voice to estimate the emotion. Furthermore, the reception unit can also estimate the emotion using biometrics technology. For example, the reception unit can analyze the user's heart rate or electrodermal activity to estimate the emotion. The reception unit adjusts the timing of input reception based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit can delay the timing of input reception, providing a relaxing environment. Furthermore, if the user is relaxed, the reception unit can immediately accept input, enabling smooth operation. Furthermore, if the user is in a hurry, the reception unit can accelerate the timing of input reception, allowing the user to receive information quickly. This allows the receiving unit to adjust the timing of input reception according to the user's emotions, thereby enabling more appropriate input reception.

[0075] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit analyzes the user's past input history and selects the optimal reception method. The past input history includes, but is not limited to, past input content, input frequency, input pattern, etc. The reception unit selects the optimal reception method based on, for example, the past input content. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also select the optimal reception method based on the past input frequency. For example, the reception unit predicts and suggests an input method to be used in a specific time period based on the user's past input frequency. Furthermore, the reception unit can select the optimal reception method based on the past input pattern. For example, the reception unit automatically completes related input items based on information previously input by the user. In this way, the reception unit can select the optimal reception method by analyzing the user's past input history.

[0076] The reception unit may perform filtering based on the user's current driving situation and areas of interest when receiving input. The reception unit may perform filtering based on the user's current driving situation and areas of interest when receiving input. Examples of driving situations include, but are not limited to, speed, traffic volume, and weather. For example, when a user is driving on a highway, the reception unit provides a simple input interface to allow the user to concentrate on driving. For example, when a user is driving in an urban area, the reception unit provides detailed input options to allow the user to quickly input necessary information. Furthermore, when a user performs input based on a specific area of ​​interest (e.g., eco-driving), information related to that area is preferentially displayed. Examples of areas of interest include, but are not limited to, hobbies, past search history, and social media activity. This allows the reception unit to provide more appropriate information by filtering based on the user's driving situation and areas of interest.

[0077] The reception unit can estimate the user's emotion and determine the priority of information to be received based on the estimated user's emotion. The reception unit estimates the user's emotion and determines the priority of information to be received based on the estimated user's emotion. Emotion estimation is performed, for example, using facial expression recognition technology. For example, the reception unit analyzes the user's facial expression captured with a camera to estimate the emotion. The reception unit can also estimate the emotion using voice analysis technology. For example, the reception unit analyzes the user's voice to estimate the emotion. Furthermore, the reception unit can also estimate the emotion using biometrics technology. For example, the reception unit analyzes the user's heart rate or electrodermal activity to estimate the emotion. The reception unit determines the priority of information to be received based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit prioritizes receiving important information and postpones unnecessary information. Furthermore, if the user is relaxed, the reception unit receives all information equally and provides detailed information. Furthermore, if the user is in a hurry, the reception unit prioritizes receiving the most important information and processes it quickly. This allows the reception unit to prioritize information according to the user's emotions, thereby allowing important information to be received preferentially.

[0078] The reception unit can prioritize receiving highly relevant information based on the user's geographical location information when receiving input. The reception unit prioritizes receiving highly relevant information based on the user's geographical location information when receiving input. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, when the user is in a specific area, the reception unit prioritizes receiving information related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving information around the user's home. In this way, the reception unit can prioritize receiving highly relevant information by taking the user's geographical location information into consideration.

[0079] The reception unit can analyze the user's social media activity and receive related information when receiving input. The reception unit can analyze the user's social media activity and receive related information when receiving input. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The reception unit can, for example, automatically complete related input items based on information shared by the user on social media. The reception unit can also preferentially receive related information based on topics in which the user has shown interest on social media. Furthermore, the reception unit can also receive information related to specific events or places from the user's social media activity. In this way, the reception unit can preferentially receive related information by analyzing the user's social media activity.

[0080] The generation unit can estimate the user's emotion and adjust the expression method of the generated information based on the estimated user's emotion. The generation unit estimates the user's emotion and adjusts the expression method of the generated information based on the estimated user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology. For example, the generation unit analyzes the user's facial expression captured with a camera to estimate the emotion. The generation unit can also estimate the emotion using voice analysis technology. For example, the generation unit analyzes the user's voice to estimate the emotion. Furthermore, the generation unit can also estimate the emotion using biometrics technology. For example, the generation unit analyzes the user's heart rate or electrodermal activity to estimate the emotion. The generation unit adjusts the expression method of the generated information based on the estimated user's emotion. For example, if the user is relaxed, the generation unit generates information in a calm tone. If the user is in a hurry, the generation unit generates concise and to-the-point information. If the user is excited, the generation unit generates information with visually stimulating effects. This allows the generation unit to provide more appropriate information by adjusting the way information is expressed in accordance with the user's emotions.

[0081] The generation unit can adjust the level of detail of the generated information based on the importance of the driving situation when generating the information. The generation unit adjusts the level of detail of the generated information based on the importance of the driving situation when generating the information. Driving situations include, but are not limited to, for example, speed, traffic volume, and weather. For example, when driving on a highway, the generation unit can prioritize generating concise and important information. When driving in an urban area, the generation unit can also generate detailed information and provide it to the driver. Furthermore, when parking, the generation unit can generate all information in detail and provide it to the driver. In this way, the generation unit can provide more appropriate information by adjusting the level of detail of the generated information based on the importance of the driving situation.

[0082] The generation unit can apply different generation algorithms depending on the driving category when generating information. The generation unit applies different generation algorithms depending on the driving category when generating information. Driving categories include, but are not limited to, urban driving, highway driving, and mountain road driving, for example. For example, in the case of eco-driving, the generation unit can prioritize generating information about fuel efficiency. In addition, in the case of sport driving, the generation unit can also prioritize generating information about speed and acceleration. Furthermore, in the case of city driving, the generation unit can prioritize generating traffic conditions and parking information. In this way, the generation unit can provide more appropriate information by applying different generation algorithms depending on the driving category.

[0083] The generation unit can estimate the user's emotion and adjust the length of the information to be generated based on the estimated user's emotion. The generation unit estimates the user's emotion and adjusts the length of the information to be generated based on the estimated user's emotion. The emotion estimation is performed using, for example, facial expression recognition technology. For example, the generation unit analyzes the user's facial expression captured by a camera to estimate the emotion. The generation unit can also estimate the emotion using voice analysis technology. For example, the generation unit analyzes the user's voice to estimate the emotion. Furthermore, the generation unit can also estimate the emotion using biometrics technology. For example, the generation unit analyzes the user's heart rate or electrodermal activity to estimate the emotion. The generation unit adjusts the length of the information to be generated based on the estimated user's emotion. For example, if the user is in a hurry, the generation unit generates short, to-the-point information. If the user is relaxed, the generation unit generates longer information including detailed explanations. If the user is excited, the generation unit generates information with visually stimulating effects. This allows the generation unit to provide more appropriate information by adjusting the length of the information depending on the user's emotions.

[0084] The generation unit can determine the priority of generation based on the time of submission of driving data when generating information. The generation unit can determine the priority of generation based on the time of submission of driving data when generating information. The time of submission of driving data includes, for example, real-time data, past data, etc., but is not limited to these examples. The generation unit, for example, preferentially analyzes the latest driving data and generates information. It can also generate related information by referring to past driving data. Furthermore, it can generate information based on driving data submitted during a specific time period. In this way, the generation unit can provide more appropriate information by determining the priority of generation based on the time of submission of driving data.

[0085] The generation unit can adjust the order of generation based on the relevance of the driving data when generating information. The generation unit adjusts the order of generation based on the relevance of the driving data when generating information. The relevance of the driving data includes, for example, data correlation, co-occurrence frequency, etc., but is not limited to these examples. The generation unit, for example, preferentially analyzes the most relevant driving data and generates information. It can also postpone driving data with low relevance and generate important information preferentially. Furthermore, it can adjust the order of generation of information based on the relevance of the driving data. In this way, the generation unit can provide more appropriate information by adjusting the order of generation based on the relevance of the driving data.

[0086] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and adjusts the display method of the information to be provided based on the estimated user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology. For example, the providing unit analyzes the user's facial expression captured by a camera to estimate the emotion. The providing unit can also estimate the emotion using voice analysis technology. For example, the providing unit analyzes the user's voice to estimate the emotion. Furthermore, the providing unit can also estimate the emotion using biometrics technology. For example, the providing unit analyzes the user's heart rate or electrodermal activity to estimate the emotion. The providing unit adjusts the display method of the information to be provided based on the estimated user's emotion. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. If the user is relaxed, the providing unit provides a display method including detailed information. If the user is in a hurry, the providing unit provides a display method that focuses on the main points. This allows the providing unit to provide more appropriate information by adjusting the information display method in accordance with the user's emotions.

[0087] The providing unit can adjust the level of detail to be provided based on the importance of the driving situation when providing information. The providing unit adjusts the level of detail to be provided based on the importance of the driving situation when providing information. The driving situation includes, but is not limited to, for example, speed, traffic volume, and weather. For example, when driving on a highway, the providing unit can prioritize providing concise and important information. When driving in an urban area, the providing unit can also provide detailed information to the driver. Furthermore, when parking, the providing unit can provide all information in detail to the driver. In this way, the providing unit can provide more appropriate information by adjusting the level of detail to be provided based on the importance of the driving situation.

[0088] The providing unit can apply different provision algorithms depending on the driving category when providing information. The providing unit applies different provision algorithms depending on the driving category when providing information. Driving categories include, but are not limited to, urban driving, highway driving, and mountain road driving, for example. For example, in the case of eco-driving, the providing unit can prioritize providing information about fuel efficiency. In addition, in the case of sporty driving, the providing unit can also prioritize providing information about speed and acceleration. Furthermore, in the case of city driving, the providing unit can prioritize providing traffic conditions and parking information. In this way, the providing unit can provide more appropriate information by applying different provision algorithms depending on the driving category.

[0089] The providing unit can estimate the user's emotion and adjust the length of information to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and adjusts the length of information to be provided based on the estimated user's emotion. Emotion estimation is performed, for example, using facial expression recognition technology. For example, the providing unit analyzes the user's facial expression captured by a camera to estimate the emotion. The providing unit can also estimate the emotion using voice analysis technology. For example, the providing unit analyzes the user's voice to estimate the emotion. Furthermore, the providing unit can also estimate the emotion using biometrics technology. For example, the providing unit analyzes the user's heart rate or electrodermal activity to estimate the emotion. The providing unit adjusts the length of information to be provided based on the estimated user's emotion. For example, if the user is in a hurry, the providing unit provides short, to-the-point information. If the user is relaxed, the providing unit provides longer information including detailed explanations. If the user is excited, the providing unit provides information with visually stimulating effects. This allows the providing unit to provide more appropriate information by adjusting the length of the information depending on the user's emotions.

[0090] The providing unit can determine the priority of provision based on the time of submission of driving data when providing information. The providing unit can determine the priority of provision based on the time of submission of driving data when providing information. The time of submission of driving data includes, for example, real-time data, past data, etc., but is not limited to these examples. The providing unit, for example, preferentially analyzes the latest driving data and provides information. It can also provide related information by referring to past driving data. Furthermore, it can provide information based on driving data submitted in a specific time period. In this way, the providing unit can provide more appropriate information by determining the priority of provision based on the time of submission of driving data.

[0091] The providing unit can adjust the order of provision of information based on the relevance of driving data when providing information. The providing unit adjusts the order of provision of information based on the relevance of driving data when providing information. The relevance of driving data includes, for example, data correlation, co-occurrence frequency, etc., but is not limited to such examples. The providing unit, for example, preferentially analyzes the most relevant driving data and provides the information. It can also postpone driving data with low relevance and provide important information preferentially. Furthermore, it can adjust the order of provision of information based on the relevance of driving data. In this way, the providing unit can provide more appropriate information by adjusting the order of provision based on the relevance of driving data.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user's emotions. The analysis unit estimates the user's emotions and adjusts the analysis criteria based on the estimated user's emotions. Emotion estimation is performed, for example, using facial expression recognition technology. For example, the analysis unit analyzes the user's facial expressions captured with a camera to estimate the emotions. The analysis unit can also estimate emotions using voice analysis technology. For example, the analysis unit analyzes the user's voice to estimate the emotions. Furthermore, the analysis unit can also estimate emotions using biometrics technology. For example, the analysis unit analyzes the user's heart rate and electrodermal activity to estimate the emotions. The analysis unit adjusts the analysis criteria based on the estimated user's emotions. For example, if the user is nervous, the analysis unit prioritizes analysis of concise and important information. On the other hand, if the user is relaxed, the analysis unit performs analysis including detailed information. On the other hand, if the user is in a hurry, the analysis unit performs analysis that focuses on the main points. This allows the analysis unit to adjust the analysis criteria according to the user's emotions, enabling more appropriate analysis.

[0093] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships between the driving data. During analysis, the analysis unit improves the accuracy of the analysis based on the interrelationships between the driving data. Interrelationships between the driving data include, for example, data correlations and co-occurrence frequencies, but are not limited to these examples. For example, the analysis unit analyzes the interrelationships between the driving data and predicts dangerous situations. The analysis unit can also propose an optimal driving method by taking into account the interrelationships between the driving data. Furthermore, the analysis unit can optimize fuel efficiency based on the interrelationships between the driving data. As a result, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the driving data.

[0094] The analysis unit can perform the analysis based on attribute information of the person who submitted the driving data during the analysis. The analysis unit can perform the analysis based on attribute information of the person who submitted the driving data during the analysis. The attribute information of the person who submitted the driving data includes, for example, age, gender, driving experience, etc., but is not limited to these examples. The analysis unit can adjust the analysis criteria for the driving data based on, for example, the age and gender of the person who submitted the driving data. The analysis unit can also adjust the level of detail of the analysis based on the driving experience of the person who submitted the driving data. Furthermore, the analysis result of the driving data can be provided based on the health condition of the person who submitted the driving data. This allows the analysis unit to perform a more appropriate analysis by taking into account the attribute information of the person who submitted the driving data.

[0095] The analysis unit can estimate the user's emotion and adjust the order in which the analysis results are displayed based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the order in which the analysis results are displayed based on the estimated user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology. For example, the analysis unit analyzes the user's facial expression captured with a camera to estimate the emotion. The analysis unit can also estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the user's voice to estimate the emotion. Furthermore, the analysis unit can also estimate the emotion using biometrics technology. For example, the analysis unit analyzes the user's heart rate or electrodermal activity to estimate the emotion. The analysis unit adjusts the order in which the analysis results are displayed based on the estimated user's emotion. For example, if the user is nervous, the analysis unit displays important analysis results first. On the other hand, if the user is relaxed, the analysis unit displays detailed analysis results sequentially. On the other hand, if the user is in a hurry, the analysis unit displays analysis results that focus on the main points first. This allows the analysis unit to provide more appropriate information by adjusting the display order of the analysis results according to the user's emotions.

[0096] The analysis unit can perform the analysis based on the geographical distribution of the driving data during the analysis. The analysis unit performs the analysis based on the geographical distribution of the driving data during the analysis. Examples of the geographical distribution include, but are not limited to, GPS data, geographic information systems (GIS), etc. The analysis unit, for example, prioritizes the analysis of driving data in a specific region to understand driving trends specific to the region. The analysis unit can also classify the analysis results of the driving data by region based on the geographical distribution. Furthermore, the analysis accuracy of the driving data can be improved by taking the geographical distribution into consideration. This allows the analysis unit to perform more appropriate analysis by taking the geographical distribution of the driving data into consideration.

[0097] The analysis unit can improve the accuracy of the analysis by referring to literature related to the driving data during analysis. The analysis unit can improve the accuracy of the analysis by referring to literature related to the driving data during analysis. Relevant literature includes, but is not limited to, academic papers, technical reports, etc. For example, the analysis unit can improve the accuracy of the analysis by referring to relevant academic papers during analysis of the driving data. In addition, the analysis unit can optimize the analysis algorithm based on past research results. Furthermore, new analysis methods can be introduced based on the relevant literature. In this way, the analysis unit can improve the accuracy of the analysis by referring to literature related to the driving data.

[0098] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit estimates the user's emotions and selects training data based on the estimated user emotions. Emotion estimation is performed using, for example, facial expression recognition technology. For example, the learning unit analyzes the user's facial expressions captured with a camera to estimate the emotions. The learning unit can also estimate emotions using voice analysis technology. For example, the learning unit analyzes the user's voice to estimate the emotions. Furthermore, the learning unit can also estimate emotions using biometrics technology. For example, the learning unit analyzes the user's heart rate or electrodermal activity to estimate the emotions. The learning unit selects training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit selects detailed training data. If the user is in a hurry, the learning unit selects concise and to-the-point training data. If the user is excited, the learning unit selects visually stimulating training data. This allows the learning unit to select learning data according to the user's emotions, thereby enabling more appropriate learning.

[0099] The learning unit can adjust the learning algorithm by referring to past learning data during learning. The learning unit adjusts the learning algorithm by referring to past learning data during learning. Past learning data includes, but is not limited to, past training data sets and learning histories, for example. The learning unit adjusts parameters of the learning algorithm based on the past learning data, for example. The accuracy of learning can also be improved by referring to the past learning data. Furthermore, a new learning method can be introduced based on the past learning data. In this way, the learning unit can improve the accuracy of the learning algorithm by referring to the past learning data.

[0100] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit estimates the user's emotions and adjusts the frequency of learning based on the estimated user emotions. Emotion estimation is performed using, for example, facial expression recognition technology. For example, the learning unit analyzes the user's facial expressions captured with a camera to estimate the emotions. The learning unit can also estimate emotions using voice analysis technology. For example, the learning unit analyzes the user's voice to estimate the emotions. Furthermore, the learning unit can also estimate emotions using biometrics technology. For example, the learning unit analyzes the user's heart rate or electrodermal activity to estimate the emotions. The learning unit adjusts the frequency of learning based on the estimated user emotions. For example, if the user is relaxed, the learning unit increases the frequency of learning. Also, if the user is in a hurry, the learning unit decreases the frequency of learning. Furthermore, if the user is excited, the learning unit adjusts the frequency of learning. In this way, the learning unit adjusts the frequency of learning according to the user's emotions, enabling more appropriate learning.

[0101] During learning, the learning unit can weight the learning data based on the time when the driving data was submitted. During learning, the learning unit weights the learning data based on the time when the driving data was submitted. The time when the driving data was submitted includes, for example, real-time data, past data, etc., but is not limited to these examples. The learning unit, for example, weights the most recent driving data during learning. Also, it can weight the learning data by referring to past driving data. Furthermore, it can weight the learning data based on driving data submitted during a specific time period. In this way, the learning unit weights the learning data based on the time when the driving data was submitted, enabling more appropriate learning.

[0102] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. The notification unit estimates the user's emotion and adjusts the timing of the notification based on the estimated user's emotion. The emotion estimation is performed using, for example, facial expression recognition technology. For example, the notification unit analyzes the user's facial expression captured by a camera to estimate the emotion. The notification unit can also estimate the emotion using voice analysis technology. For example, the notification unit analyzes the user's voice to estimate the emotion. Furthermore, the notification unit can also estimate the emotion using biometrics technology. For example, the notification unit analyzes the user's heart rate or electrodermal activity to estimate the emotion. The notification unit adjusts the timing of the notification based on the estimated user's emotion. For example, if the user is nervous, the notification unit delays the timing of the notification. Also, if the user is relaxed, the notification unit immediately issues a notification. Furthermore, if the user is in a hurry, the notification unit quickly issues a notification. In this way, the notification unit can adjust the timing of the notification according to the user's emotion, thereby enabling more appropriate notification.

[0103] The reporting unit can adjust the level of detail of the report based on the importance of the driving data when reporting. The reporting unit adjusts the level of detail of the report based on the importance of the driving data when reporting. The importance of the driving data includes, for example, the risk of an accident, driving safety, etc., but is not limited to these examples. For example, the reporting unit issues a detailed report when it detects serious dangerous driving. Also, the reporting unit can issue a brief report when it detects minor dangerous driving. Furthermore, the reporting unit can adjust the level of detail of the report based on the importance of the driving data. As a result, the reporting unit can adjust the level of detail of the report based on the importance of the driving data, thereby enabling more appropriate reporting.

[0104] The reporting unit can apply different reporting algorithms depending on the category of driving data when reporting. The reporting unit applies different reporting algorithms depending on the category of driving data when reporting. Driving data categories include, but are not limited to, speed data, braking data, and steering operation data, for example. For example, the reporting unit can immediately report if it detects aggressive driving. Furthermore, it can also report in detail if it detects speeding. Furthermore, it can adjust the reporting algorithm depending on the category of dangerous driving. This allows the reporting unit to apply different reporting algorithms depending on the category of driving data, enabling more appropriate reporting.

[0105] The reporting unit can estimate the user's emotions and determine the priority of reports based on the estimated user emotions. The reporting unit estimates the user's emotions and determines the priority of reports based on the estimated user emotions. The emotion estimation is performed using, for example, facial expression recognition technology. For example, the reporting unit analyzes the user's facial expressions captured by a camera to estimate the emotions. The reporting unit can also estimate the emotions using voice analysis technology. For example, the reporting unit analyzes the user's voice to estimate the emotions. Furthermore, the reporting unit can also estimate the emotions using biometrics technology. For example, the reporting unit analyzes the user's heart rate or electrodermal activity to estimate the emotions. The reporting unit determines the priority of reports based on the estimated user emotions. For example, if the user is nervous, the reporting unit prioritizes important reports. Also, if the user is relaxed, the reporting unit handles all reports equally. Furthermore, if the user is in a hurry, the reporting unit prioritizes the most important reports. This allows the reporting unit to prioritize reports according to the user's emotions, enabling more appropriate reporting.

[0106] The reporting unit can determine the priority of a report by taking into account the geographical location information of the driving data when reporting. The reporting unit determines the priority of a report by taking into account the geographical location information of the driving data when reporting. Examples of geographical location information include, but are not limited to, GPS data, location information services, etc. The reporting unit, for example, prioritizes reporting of dangerous driving in a specific area. The reporting unit can also adjust the priority of a report based on the geographical location information. Furthermore, the reporting unit can also determine the priority of a report by taking into account the geographical location information. This allows the reporting unit to make more appropriate reports by taking into account the geographical location information of the driving data.

[0107] The reporting unit can improve the accuracy of the report by referring to literature related to the driving data when reporting. The reporting unit can improve the accuracy of the report by referring to literature related to the driving data when reporting. Relevant literature includes, but is not limited to, academic papers, technical reports, etc. For example, the reporting unit can improve the accuracy of the report by referring to relevant academic papers when reporting the driving data. In addition, the reporting unit can optimize the reporting algorithm based on past research results. Furthermore, a new reporting method can be introduced based on the relevant literature. In this way, the reporting unit can improve the accuracy of the report by referring to literature related to the driving data. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, provision unit, analysis unit, learning unit, and reporting 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 reception unit receives user input using a touch panel 38A or a microphone 38B of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates information using natural language processing technology or image analysis technology. The provision unit provides information using the display 40A or the speaker 40B of the smart device 14. The analysis unit analyzes video captured using the camera 42 of the smart device 14. The learning unit collects driving data using the sensors of the smart device 14 and performs self-learning using the specific processing unit 290 of the data processing device 12. The reporting unit reports to the police using the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, analysis unit, learning unit, and reporting unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates information using natural language processing technology or image analysis technology. The provision unit provides information using the speaker 240 of the smart glasses 214. The analysis unit analyzes video captured using the camera 42 of the smart glasses 214. The learning unit collects driving data using the sensor of the smart glasses 214 and performs self-learning using the specific processing unit 290 of the data processing device 12. The reporting unit reports to the police using the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, analysis unit, learning unit, and reporting unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates information using natural language processing technology or image analysis technology. The provision unit provides information using the speaker 240 of the headset type terminal 314. The analysis unit analyzes video captured using the camera 42 of the headset type terminal 314. The learning unit collects driving data using the sensor of the headset type terminal 314 and performs self-learning using the specific processing unit 290 of the data processing device 12. The reporting unit reports to the police using the communication I / F 44 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, analysis unit, learning unit, and reporting unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates information using natural language processing technology or image analysis technology. The provision unit provides information using the speaker 240 of the robot 414. The analysis unit analyzes video captured using the camera 42 of the robot 414. The learning unit collects driving data using the sensors of the robot 414 and performs self-learning using the specific processing unit 290 of the data processing device 12. The reporting unit reports to the police using the communication I / F 44 of the robot 414.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The reception unit can analyze the user's driving style and suggest the optimal input method. For example, if the user frequently uses voice input, the reception unit can preferentially suggest voice input. Also, if the user prefers touch input, the reception unit can preferentially suggest touch input. Furthermore, if the user tends to use a specific input method during a specific time period, the reception unit can suggest the optimal input method for that time period. In this way, the reception unit can provide a more comfortable operation by suggesting the optimal input method according to the user's driving style.

[0110] The analysis unit can analyze the user's driving patterns based on the user's driving history and provide advice according to the predicted driving situation. For example, if the user has frequently used a specific route in the past, it can provide traffic information and cautions about that route. Also, if the user tends to drive during a specific time period, it can predict the traffic conditions during that time period and suggest the optimal driving method. Furthermore, it can provide advice on optimizing fuel efficiency and safe driving based on the user's driving style. In this way, the analysis unit can support safer and more efficient driving by providing appropriate advice based on the user's driving history.

[0111] The learning unit can generate a training program to support the improvement of the user's driving skills based on the user's driving data. For example, if the user has difficulty with a particular driving skill (e.g., lane changing, parking, etc.), it can provide a training program to improve that skill. It can also analyze the user's driving data, identify driving weaknesses, and provide advice to overcome those weaknesses. It can also evaluate the improvement of the user's driving skills and provide feedback on the progress. In this way, the learning unit can support the improvement of the user's driving skills, thereby achieving safer and more efficient driving.

[0112] The reporting unit can learn dangerous driving patterns based on the user's driving data and issue preventative reports. For example, if the user has frequently braked or accelerated suddenly in the past, the unit can learn those patterns and issue a warning before a similar situation occurs. Also, if the user has a tendency to engage in dangerous driving on specific roads or at specific times of the day, the unit can issue preventative reports based on that information. Furthermore, the unit can evaluate the risk of dangerous driving based on the user's driving style and notify the police or emergency contacts as necessary. In this way, the reporting unit can ensure the safety of the driver by preventing dangerous driving and taking early action.

[0113] The providing unit can evaluate the stress level while driving based on the user's driving data and provide a relaxing environment. For example, if the user has been driving for a long time, the providing unit can display relaxing music or scenery. Also, if the user is stuck in traffic, the providing unit can suggest relaxing activities or exercises. Furthermore, the providing unit can suggest breaks while driving and provide advice to refresh the user according to the user's stress level. In this way, the providing unit can manage the user's stress level and provide a more comfortable driving environment.

[0114] The reception unit can estimate the user's emotion and adjust the design of the input interface based on the estimated emotion. For example, if the user is nervous, the reception unit can provide a simple and intuitive interface. If the user is relaxed, the reception unit can provide an interface containing detailed information. Furthermore, if the user is excited, the reception unit can provide a visually stimulating design. In this way, the reception unit can provide a more comfortable operation by adjusting the input interface according to the user's emotion.

[0115] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can display important information concisely. If the user is relaxed, the analysis unit can display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can display analysis results that focus on the main points. In this way, the analysis unit can provide more appropriate information by adjusting the display method of the analysis results according to the user's emotions.

[0116] The learning unit can estimate the user's emotions and adjust the timing of learning based on the estimated emotions. For example, if the user is relaxed, the learning unit can increase the frequency of learning. Also, if the user is in a hurry, the learning unit can decrease the frequency of learning. Furthermore, if the user is excited, the learning unit can adjust the timing of learning. In this way, the learning unit can achieve more effective learning by adjusting the timing of learning according to the user's emotions.

[0117] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is nervous, the notification unit can send a notification including detailed information. If the user is relaxed, the notification unit can also send a concise notification. Furthermore, if the user is in a hurry, the notification unit can also give priority to reporting important information. In this way, the notification unit can adjust the content of the notification according to the user's emotions, thereby realizing more appropriate notifications.

[0118] The providing unit can evaluate the user's attention while driving based on the user's driving data and provide advice to maintain attention. For example, if the user has been driving for a long time, the providing unit can suggest a break to maintain attention. Also, if the user is tired, the providing unit can suggest exercises or refreshing methods to improve attention. Furthermore, the providing unit can provide advice to maintain attention based on the user's driving style. In this way, the providing unit can manage the user's attention and support safer driving.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The reception unit receives input from a user. The input from a user may be voice input, text input, touch input, or the like. For example, the reception unit may receive voice input from a user using voice recognition technology, touch input from a user using a touch screen, or text input from a keyboard or software keyboard. Step 2: The generation unit analyzes the information received by the reception unit and generates the necessary information. The generation unit can analyze the user's voice input using natural language processing technology, analyze video captured by a camera using image analysis technology, or analyze the user's past input history using data mining technology. Step 3: The providing unit provides the information generated by the generating unit to the user. The providing unit can provide the information by displaying the information on a screen, by providing audio guidance, or by sending an email.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 reception unit that receives input from a user; a generating unit that analyzes the information received by the receiving unit and generates necessary information; a providing unit that provides the information generated by the generating unit to a user. A system characterized by:

2. Equipped with an analysis unit that analyzes the images captured by the camera The system of claim 1 .

3. Equipped with a learning unit that collects driver driving data and enables the generating AI to self-learn The system of claim 1 .

4. Equipped with a reporting unit that detects dangerous driving and notifies the police The system of claim 1 .

5. The providing unit Equipped with AR screen provision function The system of claim 1 .

6. The providing unit Equipped with speed adjustment and fuel consumption calculation functions based on image analysis The system of claim 1 .

7. The providing unit Equipped with a driving scoring function The system of claim 1 .

8. The reception unit Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions. The system of claim 1 .

9. The reception unit Analyze the user's past input history and select the appropriate reception method The system of claim 1 .

10. The reception unit When input is received, it filters based on the user's current driving situation and areas of interest. The system of claim 1 .

11. The reception unit Estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions. The system of claim 1 .

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