system

Abstract

The system according to this embodiment aims to analyze driving data and insurance history, recommend the optimal insurance plan, and predict the risk of traffic accidents. [Solution] The system according to the embodiment comprises a collection unit, a recommendation unit, a prediction unit, and an advice unit. The collection unit collects driving data and insurance history. The recommendation unit analyzes the data collected by the collection unit and recommends the optimal insurance plan. The prediction unit predicts the risk of traffic accidents based on the insurance plan recommended by the recommendation unit. The advice unit provides real-time driving advice based on the risk predicted by the prediction unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to utilize driving data and insurance history to recommend an optimal insurance plan and predict traffic accident risks, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze driving data and insurance history, recommend an optimal insurance plan, and predict traffic accident risks.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a recommendation unit, a prediction unit, and an advice unit. The data collection unit collects driving data and insurance history. The recommendation unit analyzes the data collected by the data collection unit and recommends the optimal insurance plan. The prediction unit predicts the risk of traffic accidents based on the insurance plan recommended by the recommendation unit. The advice unit provides real-time driving advice based on the risk predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze driving data and insurance history, recommend the optimal insurance plan, and predict the risk of traffic accidents. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The autonomous driving AI chatbot system according to an embodiment of the present invention is a system that recommends the optimal insurance plan based on driving data and insurance history, predicts traffic accident risk, and provides real-time driving advice. This system generates revenue from fees from insurance companies and from the promotion of local businesses utilizing the characteristics of AI-generated advertisements. Furthermore, it uses the multimodal functions of LLM to provide driving advice and safe driving training simulations that are adapted to the surrounding environment and weather, addressing all driving situations. This also contributes to sustainable urban development. For example, it collects driving data and insurance history. By collecting data such as the driver's past driving history and insurance usage history, it is possible to understand the driver's driving style and risk profile. Next, the generating AI analyzes the collected data and recommends the optimal insurance plan. For example, based on the driver's driving style, it recommends a plan with lower premiums for low-risk drivers and a plan with higher premiums for high-risk drivers. Furthermore, the generating AI predicts traffic accident risk. For example, it predicts the likelihood of a traffic accident occurring based on the driver's past driving history and current driving situation. This allows drivers to understand the risks in advance and take appropriate measures. It also has a function to provide real-time driving advice. For example, while driving, the generated AI provides advice to the driver such as "Slow down" or "Maintain a safe distance from the vehicle in front." This allows the driver to drive safely. In addition, LLM's multimodal function provides driving advice according to the surrounding environment and weather. For example, in rainy weather it provides advice such as "Be careful as it is slippery," and on snowy roads it provides advice such as "Put on snow chains." Furthermore, it also provides safe driving training simulations. For example, drivers can simulate various driving situations in a virtual environment and acquire safe driving techniques. This allows drivers to drive safely in actual driving situations. This system generates revenue from fees from insurance companies and from the promotion of local businesses by utilizing the characteristics of AI-generated advertisements.For example, advertisements for local restaurants and shops can be displayed to drivers while they are driving, promoting local businesses. Finally, this system also contributes to sustainable urban development. For instance, it is expected that the reduction in traffic accidents and the spread of safe driving will improve the urban traffic environment and promote sustainable urban development. In this way, the autonomous driving AI chatbot system can improve driver safety and contribute to sustainable urban development.

[0029] The autonomous driving AI chatbot system according to this embodiment comprises a data collection unit, a recommendation unit, a prediction unit, and an advice unit. The data collection unit collects driving data and insurance history. Driving data includes, but is not limited to, speed, frequency of brake use, and mileage. Insurance history includes, but is not limited to, past insurance contract details and insurance claim history. The data collection unit can, for example, collect the driver's past driving history. The data collection unit can also collect insurance usage history. Furthermore, the data collection unit can collect data to understand the driver's driving style and risk profile. The recommendation unit analyzes the data collected by the data collection unit and recommends the optimal insurance plan. The recommendation unit, for example, uses generative AI to recommend an insurance plan based on the driver's driving style. For example, it recommends a plan with lower premiums to drivers with low risk and a plan with higher premiums to drivers with high risk. The recommendation unit can also use generative AI to recommend an insurance plan based on the driver's risk profile. For example, the generating AI takes the driver's driving data and insurance history as input and outputs the optimal insurance plan. The prediction unit predicts the risk of traffic accidents based on the insurance plan recommended by the recommendation unit. The prediction unit predicts the risk of traffic accidents based on the driver's past driving history and current driving situation, for example, using the generating AI. For example, the generating AI takes the driver's driving data and insurance history as input and outputs the risk of traffic accidents. The advice unit provides real-time driving advice based on the risk predicted by the prediction unit. For example, the advice unit uses the generating AI to provide advice to the driver while driving, such as "Please slow down" or "Please maintain a safe distance from the vehicle in front." For example, the generating AI takes the driver's driving data and insurance history as input and outputs driving advice. As a result, the autonomous driving AI chatbot system according to this embodiment can recommend the optimal insurance plan based on driving data and insurance history, predict the risk of traffic accidents, and provide real-time driving advice.

[0030] The data collection unit collects driving data and insurance history. Driving data includes, but is not limited to, speed, brake usage frequency, and mileage. Specifically, it collects detailed driving data in real time, such as changes in the driver's speed, brake usage frequency, mileage, and frequency of sudden acceleration and deceleration, through sensors and GPS devices installed in the vehicle. This data is important for understanding the driver's driving style and habits. Insurance history includes, but is not limited to, past insurance contract details and insurance claim history. Insurance history is obtained from insurance company databases and includes details of past insurance contracts, insurance claim history, and accident occurrences. The data collection unit centrally manages this data and uses it as basic data to create driver risk profiles. Furthermore, the data collection unit can also collect data to understand the driver's driving style and risk profile. For example, it collects personal information such as the driver's age, gender, driving experience, and place of residence to help analyze driving style and risk profile. This allows the data collection unit to gain a detailed understanding of the driver's driving behavior and risk factors, thereby improving the overall accuracy and reliability of the system.

[0031] The recommendation department analyzes the data collected by the data collection department and recommends the optimal insurance plan. For example, the recommendation department uses generative AI to recommend insurance plans based on the driver's driving style. Specifically, the generative AI receives the driver's driving data and insurance history as input and generates a driver risk profile. This risk profile includes the driver's driving behavior, past accident history, and frequency of insurance claims. Based on this data, the generative AI evaluates the driver's risk level and recommends the optimal insurance plan. For example, it recommends a lower-premium plan to a low-risk driver and a higher-premium plan to a high-risk driver. The generative AI can also recommend insurance plans based on the driver's risk profile. For example, it takes the driver's driving data and insurance history as input and outputs the optimal insurance plan. The generative AI uses historical data and statistical models to predict the driver's risk and select the optimal insurance plan. This allows the recommendation department to provide drivers with the optimal insurance plan and optimize insurance premiums. Furthermore, the recommendation department can collect driver feedback and continuously improve its insurance plan recommendation algorithm. This allows the recommendation department to always provide drivers with the most suitable insurance plan, creating a system that is beneficial to both insurance companies and drivers.

[0032] The prediction unit predicts traffic accident risk based on insurance plans recommended by the recommendation unit. The prediction unit uses, for example, generative AI to predict traffic accident risk based on the driver's past driving history and current driving conditions. Specifically, the generative AI takes the driver's driving data and insurance history as input and outputs the traffic accident risk. The generative AI analyzes past driving data and insurance history to evaluate the driver's driving behavior and risk factors. For example, it considers the frequency of sudden braking, the number of times speeding occurs, and past accident history to predict the driver's traffic accident risk. Based on this data, the generative AI evaluates the driver's risk level and calculates the probability of an accident occurring. This allows the prediction unit to predict the driver's traffic accident risk with high accuracy and provide insurance companies with information for risk management. Furthermore, the prediction unit can continuously predict traffic accident risk based on real-time updated driving data. For example, if the driver's driving style changes or new risk factors arise, the prediction unit immediately incorporates the new data and re-evaluates the traffic accident risk. This allows the prediction unit to always provide highly accurate risk predictions based on the latest information, creating a system beneficial to both insurance companies and drivers.

[0033] The advice unit provides real-time driving advice based on the risks predicted by the prediction unit. For example, the advice unit uses generative AI to provide advice to the driver during driving, such as "Slow down" or "Maintain a safe following distance." Specifically, the generative AI takes the driver's driving data and insurance history as input and outputs driving advice. The generative AI analyzes the driver's current driving situation in real time and provides appropriate advice according to the driver's risk level. For example, if the driver is speeding on a highway, the generative AI will advise "Slow down," and if the driver is too close to the vehicle in front, it will advise "Maintain a safe following distance." In this way, the advice unit can provide drivers with appropriate real-time driving advice and reduce the risk of traffic accidents. Furthermore, the advice unit can collect driver feedback and continuously improve the accuracy and effectiveness of the advice. For example, if the risk of a traffic accident is reduced as a result of the driver following the advice, the advice algorithm will be improved based on that data. The advice unit can also provide customized advice according to the driver's individual driving style and preferences. This allows the advisory unit to always provide drivers with optimal driving advice, minimizing the risk of traffic accidents.

[0034] The environmental advice unit can provide driving advice tailored to the surrounding environment and weather conditions. For example, it can use generative AI to provide driving advice that is appropriate to the surrounding environment and weather. For instance, it might provide advice such as "Be careful as it is slippery" in rainy weather, or "Please put on snow chains" on snowy roads. The environmental advice unit can also use generative AI to provide driving advice based on the surrounding environment and weather conditions. For example, the generative AI takes surrounding environment data and weather data as input and outputs driving advice. This improves driver safety by providing driving advice tailored to the surrounding environment and weather conditions.

[0035] The simulation unit can provide safe driving training simulations. For example, the simulation unit can provide safe driving training simulations using generative AI. For instance, a driver can simulate various driving situations in a virtual environment and acquire safe driving techniques. Furthermore, the simulation unit can also provide safe driving training simulations using generative AI. For example, the generative AI takes driving simulation data as input and outputs a safe driving training simulation. This allows for the improvement of drivers' driving skills by providing safe driving training simulations.

[0036] The revenue stream can generate revenue from fees from insurance companies and from promoting local businesses using the characteristics of AI-generated advertisements. For example, the revenue stream can generate revenue from fees from insurance companies. Alternatively, the revenue stream can also generate revenue from promoting local businesses using the characteristics of AI-generated advertisements. For instance, advertisements for local restaurants and shops could be displayed to drivers while they are driving, thus promoting local businesses. This allows for a sustainable revenue model by generating revenue from fees from insurance companies and from promoting local businesses using the characteristics of AI-generated advertisements.

[0037] The data collection unit can collect a driver's past driving history and insurance claim history. For example, the data collection unit can collect a driver's past driving history, such as past driving routes and driving time. The data collection unit can also collect insurance claim history, such as past insurance claims and insurance policy change history. By collecting a driver's past driving history and insurance claim history, it becomes possible to recommend insurance plans with greater accuracy.

[0038] The advice unit can provide real-time driving advice to the driver while driving. For example, the advice unit uses generative AI to provide real-time driving advice to the driver while driving. For instance, it might provide advice such as "Slow down" or "Maintain a safe distance from the vehicle in front." Furthermore, the advice unit can also use generative AI to provide real-time driving advice to the driver while driving. For example, the generative AI takes driving data as input and outputs driving advice. This allows for improved driver safety by providing real-time driving advice while driving.

[0039] The data collection unit can analyze the driver's past driving history and select the optimal data collection method. For example, the unit can analyze data such as past driving routes and driving times. Furthermore, based on past driving history, the unit can concentrate data collection during specific time periods. For instance, it can collect data for specific driving patterns based on past driving history. Additionally, the unit can analyze past driving history and prioritize data collection under specific road conditions. This allows for the selection of the optimal data collection method by analyzing the driver's past driving history.

[0040] The data collection unit can filter data based on the driver's current driving status and areas of interest during data collection. For example, the data collection unit can filter data considering the driver's current driving status. For example, it can filter data based on speed, traffic volume, road conditions, etc. The data collection unit can also filter data based on the driver's areas of interest. For example, it can filter data based on the driver's past driving history or survey results. Furthermore, the data collection unit can filter out unnecessary data considering the driver's current driving status. In this way, by filtering data based on the driver's current driving status and areas of interest, only important data can be collected.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the driver's geographical location during data collection. For example, the data collection unit collects data considering the driver's geographical location. For instance, if the driver is in a specific area, it prioritizes the collection of data related to that area. The data collection unit can also collect important data based on the driver's current location. Furthermore, the data collection unit can filter highly relevant data by considering the driver's geographical location. This allows for more accurate data collection by collecting highly relevant data while considering the driver's geographical location.

[0042] The data collection unit can analyze the driver's social media activity and collect relevant data during data collection. For example, the unit can analyze the driver's social media activity, such as the content of posts and the number of likes. The unit can also collect data related to topics of interest from the driver's social media activity. For example, it can analyze the content of the driver's social media posts and prioritize the collection of relevant data. Furthermore, the unit can collect relevant data by referring to the activities of the driver's social media followers and friends. In this way, relevant data can be collected by analyzing the driver's social media activity.

[0043] The recommendation department can adjust the level of detail in insurance plan recommendations based on the driver's risk profile. For example, it can provide a concise explanation of insurance plans to low-risk drivers and a detailed explanation to high-risk drivers. The recommendation department can also provide an appropriate explanation of insurance plans based on the driver's risk profile. By adjusting the level of detail in recommendations based on the driver's risk profile, it is possible to provide more appropriate insurance plans.

[0044] The recommendation system can apply different recommendation algorithms depending on the driver's category when recommending insurance plans. For example, it can apply an algorithm that recommends low-risk insurance plans to younger drivers and an algorithm that recommends high-risk insurance plans to older drivers. The recommendation system can also apply an algorithm that recommends the most suitable insurance plan depending on the driver's category. By applying different recommendation algorithms depending on the driver's category, it can provide more appropriate insurance plans.

[0045] The recommendation department can determine the priority of insurance plans based on the driver's past insurance claim history. For example, it can prioritize recommending the most suitable insurance plan based on the driver's past insurance claim history. The recommendation department can also prioritize recommending insurance plans with lower risk based on past claim history. Furthermore, the recommendation department can analyze past claim history and prioritize recommending the most appropriate insurance plan. This allows for the provision of more appropriate insurance plans by prioritizing recommendations based on the driver's past insurance claim history.

[0046] The recommendation department can improve the accuracy of insurance plan recommendations by referring to relevant market data for the driver. For example, the recommendation department can improve the accuracy of insurance plan recommendations by referring to relevant market data for the driver. For example, it can refer to insurance market trends and competitor data. The recommendation department can also recommend the most suitable insurance plan based on relevant market data. For example, it can recommend insurance plans with low risk by referring to market data. Furthermore, the recommendation department can analyze market data and recommend the most appropriate insurance plan. In this way, by referring to relevant market data for the driver, it is possible to provide more accurate insurance plans.

[0047] The prediction unit can improve the accuracy of its predictions when predicting traffic accident risk by referring to the driver's past driving history. For example, the prediction unit predicts traffic accident risk by referring to the driver's past driving history. For example, it makes predictions based on data such as past driving routes and driving time. The prediction unit can also identify high-risk driving patterns based on past driving history. For example, it can identify low-risk driving patterns by referring to past driving history. Furthermore, the prediction unit can analyze past driving history and apply the most appropriate prediction method. As a result, more accurate risk prediction becomes possible by referring to the driver's past driving history.

[0048] The prediction unit can optimize the prediction algorithm based on the driver's current driving conditions when predicting traffic accident risk. For example, the prediction unit can optimize the prediction algorithm based on the driver's current driving conditions. For example, it can adjust the prediction algorithm based on data such as speed, traffic volume, and road conditions. The prediction unit can also apply a high-risk prediction algorithm by referring to the current driving conditions. For example, it can apply a low-risk prediction algorithm based on the current driving conditions. Furthermore, the prediction unit can analyze the current driving conditions and apply the most appropriate prediction algorithm. As a result, by optimizing the prediction algorithm based on the driver's current driving conditions, more accurate risk prediction becomes possible.

[0049] The prediction unit can improve the accuracy of its predictions by considering the driver's geographical location when predicting traffic accident risk. For example, the prediction unit predicts traffic accident risk by considering the driver's geographical location. For example, if the driver is in a specific area, it predicts the risk associated with that area. The prediction unit can also identify high-risk areas based on geographical location information. For example, it can identify low-risk areas by referring to geographical location information. Furthermore, the prediction unit can analyze geographical location information and apply the most appropriate prediction method. This makes it possible to predict risks with higher accuracy by considering the driver's geographical location information.

[0050] The prediction unit can improve the accuracy of its predictions when predicting traffic accident risk by referring to relevant literature on drivers. For example, the prediction unit predicts traffic accident risk by referring to relevant literature on drivers. For example, it makes predictions based on data such as academic papers and technical reports. The prediction unit can also identify high-risk driving patterns based on relevant literature. For example, it can identify low-risk driving patterns by referring to relevant literature. Furthermore, the prediction unit can analyze relevant literature and apply the most appropriate prediction method. As a result, by referring to relevant literature on drivers, more accurate risk prediction becomes possible.

[0051] The advice unit can adjust the level of detail of the advice provided based on the driver's current driving situation. For example, it can adjust the level of detail of the advice based on data such as speed, traffic volume, and road conditions. The advice unit can also provide detailed driving advice by referring to the current driving situation. For example, it can provide concise driving advice based on the current driving situation. Furthermore, the advice unit can analyze the current driving situation and provide the most appropriate driving advice. By adjusting the level of detail of the advice based on the driver's current driving situation, more appropriate driving advice can be provided.

[0052] The advice unit can improve the accuracy of its advice by referring to the driver's past driving history when providing driving advice. For example, the advice unit provides driving advice by referring to the driver's past driving history. For example, it provides advice based on data such as past driving routes and driving time. The advice unit can also provide optimal driving advice based on past driving history. For example, it can provide low-risk driving advice by referring to past driving history. Furthermore, the advice unit can analyze past driving history and provide the most appropriate driving advice. As a result, by referring to the driver's past driving history, more accurate driving advice can be provided.

[0053] The advice unit can provide optimal driving advice by considering the driver's geographical location. For example, if the driver is in a specific area, it will provide advice relevant to that area. The advice unit can also provide optimal driving advice based on geographical location. For example, it can provide low-risk driving advice by referring to geographical location. Furthermore, the advice unit can analyze geographical location and provide the most appropriate driving advice. This allows for the provision of more appropriate driving advice by considering the driver's geographical location.

[0054] The advice department can analyze the driver's social media activity when providing driving advice and offer relevant advice. For example, the advice department can analyze the driver's social media activity, such as the content of posts and the number of likes. The advice department can also provide driving advice related to topics of interest based on the driver's social media activity. For example, it can analyze the content of the driver's social media posts and provide relevant driving advice. Furthermore, the advice department can also provide relevant driving advice by referring to the activities of the driver's social media followers and friends. In this way, by analyzing the driver's social media activity, more relevant driving advice can be provided.

[0055] The environmental advice unit can adjust the level of detail of environmental advice based on the driver's current driving conditions when providing it. For example, the environmental advice unit can adjust the level of detail of advice based on the driver's current driving conditions. For example, it can adjust the level of detail of advice based on data such as speed, traffic volume, and road conditions. The environmental advice unit can also provide detailed environmental advice by referring to the current driving conditions. For example, it can provide concise environmental advice based on the current driving conditions. Furthermore, the environmental advice unit can analyze the current driving conditions and provide the most appropriate environmental advice. In this way, by adjusting the level of detail of advice based on the driver's current driving conditions, more appropriate environmental advice can be provided.

[0056] The Environmental Advice Department can provide optimal environmental advice by considering the driver's geographical location when providing environmental advice. For example, if the driver is in a specific area, the department will provide advice relevant to that area. The Environmental Advice Department can also provide optimal environmental advice based on geographical location information. For example, it will provide low-risk environmental advice by referring to geographical location information. Furthermore, the Environmental Advice Department can analyze geographical location information to provide the most appropriate environmental advice. This allows for the provision of more appropriate environmental advice by considering the driver's geographical location information.

[0057] The simulation unit can provide the optimal simulation by referring to the driver's past driving history when providing a simulation. For example, the simulation unit provides a simulation by referring to the driver's past driving history. For example, it performs simulations based on data such as past driving routes and driving times. The simulation unit can also provide the optimal simulation based on past driving history. For example, it can provide a low-risk simulation by referring to past driving history. Furthermore, the simulation unit can analyze past driving history and provide the most appropriate simulation. In this way, a more appropriate simulation can be provided by referring to the driver's past driving history.

[0058] The simulation unit can provide the optimal simulation by considering the driver's geographical location information when providing the simulation. For example, if the driver is in a specific area, the simulation unit will provide a simulation relevant to that area. The simulation unit can also provide the optimal simulation based on geographical location information. For example, it will provide a low-risk simulation by referring to geographical location information. Furthermore, the simulation unit can analyze geographical location information and provide the most appropriate simulation. This allows for the provision of more appropriate simulations by considering the driver's geographical location information.

[0059] The revenue generation unit can display the most relevant ads by referencing the driver's past purchase history when displaying ads. For example, the revenue generation unit can display ads based on data such as past purchase items and purchase frequency. The revenue generation unit can also display the most relevant ads based on past purchase history. For example, it can display low-risk ads by referring to past purchase history. Furthermore, the revenue generation unit can analyze past purchase history and display the most appropriate ads. This allows for the display of more relevant ads by referring to the driver's past purchase history.

[0060] The revenue-generating unit can display the most relevant ads by considering the driver's geographical location when displaying ads. For example, if the driver is in a specific area, it will display ads relevant to that area. The revenue-generating unit can also display the most relevant ads based on geographical location. For example, it will display low-risk ads by referring to geographical location. Furthermore, the revenue-generating unit can analyze geographical location and display the most appropriate ads. This allows for the display of more relevant ads by considering the driver's geographical location.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The autonomous driving AI chatbot system can also monitor the driver's health and provide driving advice based on that health status. For example, it can monitor the driver's heart rate and blood pressure in real time and provide advice such as "Take a break" or "Consult a doctor" if an abnormality is detected. It can also provide advice to encourage the driver to take a break if they are feeling fatigued. Furthermore, based on the driver's health status, it can provide advice on relaxation music and breathing techniques to reduce stress while driving. In this way, by providing driving advice that takes the driver's health status into consideration, it can improve the driver's safety and health.

[0063] The autonomous driving AI chatbot system can provide customized driving advice based on the driver's past driving history, tailored to their driving style. For example, a driver who frequently uses sudden braking in the past might receive advice such as, "To avoid sudden braking, increase the distance between your vehicle and the car in front of you." Similarly, a driver who frequently speeds in the past might receive advice such as, "Please adhere to the speed limit." Furthermore, a driver who has consistently practiced safe driving might receive positive advice such as, "Keep up the good work and continue driving safely." By providing customized driving advice tailored to each driver's style, the system can improve driver safety.

[0064] The autonomous driving AI chatbot system can provide advice to improve fuel efficiency based on the driver's driving style. For example, it can advise drivers who frequently use sudden acceleration and braking to "improve fuel efficiency by avoiding sudden acceleration and braking." It can also advise drivers who idle their engines for long periods to "improve fuel efficiency by reducing idling." Furthermore, it can advise that maintaining appropriate tire pressure improves fuel efficiency. By providing fuel efficiency improvement advice tailored to the driver's driving style, it can reduce environmental impact and lower driver costs.

[0065] The autonomous driving AI chatbot system can perform risk assessments for specific driving patterns based on the driver's past driving history and provide advice to avoid high-risk driving patterns. For example, if there has been a high number of accidents at a particular intersection in the past, it can provide advice such as, "Please be especially careful at this intersection." Similarly, if there has been a high number of accidents during a specific time period in the past, it can provide advice such as, "Please be especially careful during this time period." Furthermore, if there has been a high number of accidents under specific weather conditions in the past, it can provide advice such as, "Please be especially careful under these weather conditions." By providing risk assessments and advice based on the driver's past driving history, this system can improve driver safety.

[0066] The autonomous driving AI chatbot system can provide training programs to improve drivers' driving skills based on their past driving history. For example, a driver who frequently brakes suddenly in the past can be provided with a training program to avoid sudden braking. Similarly, a driver who frequently speeds in the past can be provided with a training program to adhere to speed limits. Furthermore, a driver who frequently changes lanes in the past can be provided with a training program to learn proper lane change techniques. By providing training programs based on a driver's past driving history, the system can improve drivers' driving skills and promote safer driving.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects driving data and insurance history. Driving data includes speed, brake usage frequency, and mileage. Insurance history includes past insurance contract details and insurance claim history. The collection unit can collect data to understand the driver's past driving history, insurance usage history, driving style, and risk profile. Step 2: The recommendation department analyzes the data collected by the data collection department and recommends the optimal insurance plan. The recommendation department uses generative AI to recommend insurance plans based on the driver's driving style and risk profile. For example, it recommends lower-premium plans to low-risk drivers and higher-premium plans to high-risk drivers. Step 3: The prediction unit predicts the risk of traffic accidents based on the insurance plan recommended by the recommendation unit. The prediction unit uses generating AI to predict the risk of traffic accidents based on the driver's past driving history and current driving conditions. Step 4: The advice unit provides real-time driving advice based on the risks predicted by the prediction unit. The advice unit uses generated AI to provide the driver with advice such as "Slow down" or "Maintain a safe distance from the vehicle in front" while driving.

[0069] (Example of form 2) The autonomous driving AI chatbot system according to an embodiment of the present invention is a system that recommends the optimal insurance plan based on driving data and insurance history, predicts traffic accident risk, and provides real-time driving advice. This system generates revenue from fees from insurance companies and from the promotion of local businesses utilizing the characteristics of AI-generated advertisements. Furthermore, it uses the multimodal functions of LLM to provide driving advice and safe driving training simulations that are adapted to the surrounding environment and weather, addressing all driving situations. This also contributes to sustainable urban development. For example, it collects driving data and insurance history. By collecting data such as the driver's past driving history and insurance usage history, it is possible to understand the driver's driving style and risk profile. Next, the generating AI analyzes the collected data and recommends the optimal insurance plan. For example, based on the driver's driving style, it recommends a plan with lower premiums for low-risk drivers and a plan with higher premiums for high-risk drivers. Furthermore, the generating AI predicts traffic accident risk. For example, it predicts the likelihood of a traffic accident occurring based on the driver's past driving history and current driving situation. This allows drivers to understand the risks in advance and take appropriate measures. It also has a function to provide real-time driving advice. For example, while driving, the generated AI provides advice to the driver such as "Slow down" or "Maintain a safe distance from the vehicle in front." This allows the driver to drive safely. In addition, LLM's multimodal function provides driving advice according to the surrounding environment and weather. For example, in rainy weather it provides advice such as "Be careful as it is slippery," and on snowy roads it provides advice such as "Put on snow chains." Furthermore, it also provides safe driving training simulations. For example, drivers can simulate various driving situations in a virtual environment and acquire safe driving techniques. This allows drivers to drive safely in actual driving situations. This system generates revenue from fees from insurance companies and from the promotion of local businesses by utilizing the characteristics of AI-generated advertisements.For example, advertisements for local restaurants and shops can be displayed to drivers while they are driving, promoting local businesses. Finally, this system also contributes to sustainable urban development. For instance, it is expected that the reduction in traffic accidents and the spread of safe driving will improve the urban traffic environment and promote sustainable urban development. In this way, the autonomous driving AI chatbot system can improve driver safety and contribute to sustainable urban development.

[0070] The autonomous driving AI chatbot system according to this embodiment comprises a data collection unit, a recommendation unit, a prediction unit, and an advice unit. The data collection unit collects driving data and insurance history. Driving data includes, but is not limited to, speed, frequency of brake use, and mileage. Insurance history includes, but is not limited to, past insurance contract details and insurance claim history. The data collection unit can, for example, collect the driver's past driving history. The data collection unit can also collect insurance usage history. Furthermore, the data collection unit can collect data to understand the driver's driving style and risk profile. The recommendation unit analyzes the data collected by the data collection unit and recommends the optimal insurance plan. The recommendation unit, for example, uses generative AI to recommend an insurance plan based on the driver's driving style. For example, it recommends a plan with lower premiums to drivers with low risk and a plan with higher premiums to drivers with high risk. The recommendation unit can also use generative AI to recommend an insurance plan based on the driver's risk profile. For example, the generating AI takes the driver's driving data and insurance history as input and outputs the optimal insurance plan. The prediction unit predicts the risk of traffic accidents based on the insurance plan recommended by the recommendation unit. The prediction unit predicts the risk of traffic accidents based on the driver's past driving history and current driving situation, for example, using the generating AI. For example, the generating AI takes the driver's driving data and insurance history as input and outputs the risk of traffic accidents. The advice unit provides real-time driving advice based on the risk predicted by the prediction unit. For example, the advice unit uses the generating AI to provide advice to the driver while driving, such as "Please slow down" or "Please maintain a safe distance from the vehicle in front." For example, the generating AI takes the driver's driving data and insurance history as input and outputs driving advice. As a result, the autonomous driving AI chatbot system according to this embodiment can recommend the optimal insurance plan based on driving data and insurance history, predict the risk of traffic accidents, and provide real-time driving advice.

[0071] The data collection unit collects driving data and insurance history. Driving data includes, but is not limited to, speed, brake usage frequency, and mileage. Specifically, it collects detailed driving data in real time, such as changes in the driver's speed, brake usage frequency, mileage, and frequency of sudden acceleration and deceleration, through sensors and GPS devices installed in the vehicle. This data is important for understanding the driver's driving style and habits. Insurance history includes, but is not limited to, past insurance contract details and insurance claim history. Insurance history is obtained from insurance company databases and includes details of past insurance contracts, insurance claim history, and accident occurrences. The data collection unit centrally manages this data and uses it as basic data to create driver risk profiles. Furthermore, the data collection unit can also collect data to understand the driver's driving style and risk profile. For example, it collects personal information such as the driver's age, gender, driving experience, and place of residence to help analyze driving style and risk profile. This allows the data collection unit to gain a detailed understanding of the driver's driving behavior and risk factors, thereby improving the overall accuracy and reliability of the system.

[0072] The recommendation department analyzes the data collected by the data collection department and recommends the optimal insurance plan. For example, the recommendation department uses generative AI to recommend insurance plans based on the driver's driving style. Specifically, the generative AI receives the driver's driving data and insurance history as input and generates a driver risk profile. This risk profile includes the driver's driving behavior, past accident history, and frequency of insurance claims. Based on this data, the generative AI evaluates the driver's risk level and recommends the optimal insurance plan. For example, it recommends a lower-premium plan to a low-risk driver and a higher-premium plan to a high-risk driver. The generative AI can also recommend insurance plans based on the driver's risk profile. For example, it takes the driver's driving data and insurance history as input and outputs the optimal insurance plan. The generative AI uses historical data and statistical models to predict the driver's risk and select the optimal insurance plan. This allows the recommendation department to provide drivers with the optimal insurance plan and optimize insurance premiums. Furthermore, the recommendation department can collect driver feedback and continuously improve its insurance plan recommendation algorithm. This allows the recommendation department to always provide drivers with the most suitable insurance plan, creating a system that is beneficial to both insurance companies and drivers.

[0073] The prediction unit predicts traffic accident risk based on insurance plans recommended by the recommendation unit. The prediction unit uses, for example, generative AI to predict traffic accident risk based on the driver's past driving history and current driving conditions. Specifically, the generative AI takes the driver's driving data and insurance history as input and outputs the traffic accident risk. The generative AI analyzes past driving data and insurance history to evaluate the driver's driving behavior and risk factors. For example, it considers the frequency of sudden braking, the number of times speeding occurs, and past accident history to predict the driver's traffic accident risk. Based on this data, the generative AI evaluates the driver's risk level and calculates the probability of an accident occurring. This allows the prediction unit to predict the driver's traffic accident risk with high accuracy and provide insurance companies with information for risk management. Furthermore, the prediction unit can continuously predict traffic accident risk based on real-time updated driving data. For example, if the driver's driving style changes or new risk factors arise, the prediction unit immediately incorporates the new data and re-evaluates the traffic accident risk. This allows the prediction unit to always provide highly accurate risk predictions based on the latest information, creating a system beneficial to both insurance companies and drivers.

[0074] The advice unit provides real-time driving advice based on the risks predicted by the prediction unit. For example, the advice unit uses generative AI to provide advice to the driver during driving, such as "Slow down" or "Maintain a safe following distance." Specifically, the generative AI takes the driver's driving data and insurance history as input and outputs driving advice. The generative AI analyzes the driver's current driving situation in real time and provides appropriate advice according to the driver's risk level. For example, if the driver is speeding on a highway, the generative AI will advise "Slow down," and if the driver is too close to the vehicle in front, it will advise "Maintain a safe following distance." In this way, the advice unit can provide drivers with appropriate real-time driving advice and reduce the risk of traffic accidents. Furthermore, the advice unit can collect driver feedback and continuously improve the accuracy and effectiveness of the advice. For example, if the risk of a traffic accident is reduced as a result of the driver following the advice, the advice algorithm will be improved based on that data. The advice unit can also provide customized advice according to the driver's individual driving style and preferences. This allows the advisory unit to always provide drivers with optimal driving advice, minimizing the risk of traffic accidents.

[0075] The environmental advice unit can provide driving advice tailored to the surrounding environment and weather conditions. For example, it can use generative AI to provide driving advice that is appropriate to the surrounding environment and weather. For instance, it might provide advice such as "Be careful as it is slippery" in rainy weather, or "Please put on snow chains" on snowy roads. The environmental advice unit can also use generative AI to provide driving advice based on the surrounding environment and weather conditions. For example, the generative AI takes surrounding environment data and weather data as input and outputs driving advice. This improves driver safety by providing driving advice tailored to the surrounding environment and weather conditions.

[0076] The simulation unit can provide safe driving training simulations. For example, the simulation unit can provide safe driving training simulations using generative AI. For instance, a driver can simulate various driving situations in a virtual environment and acquire safe driving techniques. Furthermore, the simulation unit can also provide safe driving training simulations using generative AI. For example, the generative AI takes driving simulation data as input and outputs a safe driving training simulation. This allows for the improvement of drivers' driving skills by providing safe driving training simulations.

[0077] The revenue stream can generate revenue from fees from insurance companies and from promoting local businesses using the characteristics of AI-generated advertisements. For example, the revenue stream can generate revenue from fees from insurance companies. Alternatively, the revenue stream can also generate revenue from promoting local businesses using the characteristics of AI-generated advertisements. For instance, advertisements for local restaurants and shops could be displayed to drivers while they are driving, thus promoting local businesses. This allows for a sustainable revenue model by generating revenue from fees from insurance companies and from promoting local businesses using the characteristics of AI-generated advertisements.

[0078] The data collection unit can collect a driver's past driving history and insurance claim history. For example, the data collection unit can collect a driver's past driving history, such as past driving routes and driving time. The data collection unit can also collect insurance claim history, such as past insurance claims and insurance policy change history. By collecting a driver's past driving history and insurance claim history, it becomes possible to recommend insurance plans with greater accuracy.

[0079] The advice unit can provide real-time driving advice to the driver while driving. For example, the advice unit uses generative AI to provide real-time driving advice to the driver while driving. For instance, it might provide advice such as "Slow down" or "Maintain a safe distance from the vehicle in front." Furthermore, the advice unit can also use generative AI to provide real-time driving advice to the driver while driving. For example, the generative AI takes driving data as input and outputs driving advice. This allows for improved driver safety by providing real-time driving advice while driving.

[0080] The data collection unit can estimate the driver's emotions and adjust the timing of data collection based on the estimated emotions. The data collection unit estimates the driver's emotions using, for example, an emotion engine or generative AI. For example, it can estimate the driver's emotions using facial recognition or voice analysis. The data collection unit can also adjust the timing of data collection based on the estimated emotions of the driver. For example, if the driver is stressed, data collection can be temporarily delayed. If the driver is relaxed, data collection can be carried out as usual. Furthermore, if the driver is excited, data collection can be carried out quickly. By adjusting the timing of data collection based on the driver's emotions, more appropriate data collection becomes possible.

[0081] The data collection unit can analyze the driver's past driving history and select the optimal data collection method. For example, the unit can analyze data such as past driving routes and driving times. Furthermore, based on past driving history, the unit can concentrate data collection during specific time periods. For instance, it can collect data for specific driving patterns based on past driving history. Additionally, the unit can analyze past driving history and prioritize data collection under specific road conditions. This allows for the selection of the optimal data collection method by analyzing the driver's past driving history.

[0082] The data collection unit can filter data based on the driver's current driving status and areas of interest during data collection. For example, the data collection unit can filter data considering the driver's current driving status. For example, it can filter data based on speed, traffic volume, road conditions, etc. The data collection unit can also filter data based on the driver's areas of interest. For example, it can filter data based on the driver's past driving history or survey results. Furthermore, the data collection unit can filter out unnecessary data considering the driver's current driving status. In this way, by filtering data based on the driver's current driving status and areas of interest, only important data can be collected.

[0083] The data collection unit can estimate the driver's emotions and prioritize the data to be collected based on the estimated emotions. The data collection unit estimates the driver's emotions using, for example, an emotion engine or generative AI. For example, it may use facial recognition or voice analysis to estimate the driver's emotions. Furthermore, the data collection unit can prioritize the data to be collected based on the estimated emotions of the driver. For example, if the driver is stressed, important data will be collected preferentially. If the driver is relaxed, normal data collection can be performed. Additionally, if the driver is agitated, highly urgent data can be collected preferentially. This allows for the priority collection of important data by prioritizing the data to be collected based on the driver's emotions.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the driver's geographical location during data collection. For example, the data collection unit collects data considering the driver's geographical location. For instance, if the driver is in a specific area, it prioritizes the collection of data related to that area. The data collection unit can also collect important data based on the driver's current location. Furthermore, the data collection unit can filter highly relevant data by considering the driver's geographical location. This allows for more accurate data collection by collecting highly relevant data while considering the driver's geographical location.

[0085] The data collection unit can analyze the driver's social media activity and collect relevant data during data collection. For example, the unit can analyze the driver's social media activity, such as the content of posts and the number of likes. The unit can also collect data related to topics of interest from the driver's social media activity. For example, it can analyze the content of the driver's social media posts and prioritize the collection of relevant data. Furthermore, the unit can collect relevant data by referring to the activities of the driver's social media followers and friends. In this way, relevant data can be collected by analyzing the driver's social media activity.

[0086] The recommendation system can estimate the driver's emotions and adjust its insurance plan recommendation method based on those emotions. The recommendation system can estimate the driver's emotions using, for example, an emotion engine or generative AI. For instance, it might use facial recognition or voice analysis to estimate the driver's emotions. Furthermore, the recommendation system can adjust its insurance plan recommendation method based on the estimated driver's emotions. For example, if the driver is relaxed, it can provide a detailed explanation of the insurance plan. If the driver is in a hurry, it can provide a concise explanation. Additionally, if the driver is excited, it can provide a visually appealing explanation. By adjusting the insurance plan recommendation method based on the driver's emotions, it can provide a more appropriate insurance plan.

[0087] The recommendation department can adjust the level of detail in insurance plan recommendations based on the driver's risk profile. For example, it can provide a concise explanation of insurance plans to low-risk drivers and a detailed explanation to high-risk drivers. The recommendation department can also provide an appropriate explanation of insurance plans based on the driver's risk profile. By adjusting the level of detail in recommendations based on the driver's risk profile, it is possible to provide more appropriate insurance plans.

[0088] The recommendation system can apply different recommendation algorithms depending on the driver's category when recommending insurance plans. For example, it can apply an algorithm that recommends low-risk insurance plans to younger drivers and an algorithm that recommends high-risk insurance plans to older drivers. The recommendation system can also apply an algorithm that recommends the most suitable insurance plan depending on the driver's category. By applying different recommendation algorithms depending on the driver's category, it can provide more appropriate insurance plans.

[0089] The recommendation system can estimate the driver's emotions and adjust the order in which insurance plans are recommended based on those emotions. The recommendation system estimates the driver's emotions using, for example, an emotion engine or generative AI. For instance, it might use facial recognition or voice analysis to estimate the driver's emotions. Furthermore, the recommendation system can adjust the order in which insurance plans are recommended based on the estimated emotions. For example, if the driver is relaxed, it might prioritize recommending a detailed insurance plan. If the driver is in a hurry, it might prioritize recommending a concise insurance plan. Additionally, if the driver is excited, it might prioritize recommending a visually appealing insurance plan. This allows the system to provide a more appropriate insurance plan by adjusting the order of recommendations based on the driver's emotions.

[0090] The recommendation department can determine the priority of insurance plans based on the driver's past insurance claim history. For example, it can prioritize recommending the most suitable insurance plan based on the driver's past insurance claim history. The recommendation department can also prioritize recommending insurance plans with lower risk based on past claim history. Furthermore, the recommendation department can analyze past claim history and prioritize recommending the most appropriate insurance plan. This allows for the provision of more appropriate insurance plans by prioritizing recommendations based on the driver's past insurance claim history.

[0091] The recommendation department can improve the accuracy of insurance plan recommendations by referring to relevant market data for the driver. For example, the recommendation department can improve the accuracy of insurance plan recommendations by referring to relevant market data for the driver. For example, it can refer to insurance market trends and competitor data. The recommendation department can also recommend the most suitable insurance plan based on relevant market data. For example, it can recommend insurance plans with low risk by referring to market data. Furthermore, the recommendation department can analyze market data and recommend the most appropriate insurance plan. In this way, by referring to relevant market data for the driver, it is possible to provide more accurate insurance plans.

[0092] The prediction unit can estimate the driver's emotions and adjust the accident risk prediction method based on the estimated emotions. The prediction unit estimates the driver's emotions using, for example, an emotion engine or generative AI. For example, it can estimate the driver's emotions using facial recognition or voice analysis. The prediction unit can also adjust the accident risk prediction method based on the estimated emotions of the driver. For example, if the driver is relaxed, the normal prediction method can be applied. If the driver is stressed, a high-risk prediction method can be applied. Furthermore, if the driver is excited, a high-risk prediction method can also be applied. By adjusting the accident risk prediction method based on the driver's emotions, more accurate risk prediction becomes possible.

[0093] The prediction unit can improve the accuracy of its predictions when predicting traffic accident risk by referring to the driver's past driving history. For example, the prediction unit predicts traffic accident risk by referring to the driver's past driving history. For example, it makes predictions based on data such as past driving routes and driving time. The prediction unit can also identify high-risk driving patterns based on past driving history. For example, it can identify low-risk driving patterns by referring to past driving history. Furthermore, the prediction unit can analyze past driving history and apply the most appropriate prediction method. As a result, more accurate risk prediction becomes possible by referring to the driver's past driving history.

[0094] The prediction unit can optimize the prediction algorithm based on the driver's current driving conditions when predicting traffic accident risk. For example, the prediction unit can optimize the prediction algorithm based on the driver's current driving conditions. For example, it can adjust the prediction algorithm based on data such as speed, traffic volume, and road conditions. The prediction unit can also apply a high-risk prediction algorithm by referring to the current driving conditions. For example, it can apply a low-risk prediction algorithm based on the current driving conditions. Furthermore, the prediction unit can analyze the current driving conditions and apply the most appropriate prediction algorithm. As a result, by optimizing the prediction algorithm based on the driver's current driving conditions, more accurate risk prediction becomes possible.

[0095] The prediction unit can estimate the driver's emotions and adjust the display method of the risk prediction based on the estimated emotions. The prediction unit estimates the driver's emotions using, for example, an emotion engine or generative AI. For example, it can estimate the driver's emotions using facial recognition or voice analysis. The prediction unit can also adjust the display method of the risk prediction based on the estimated emotions of the driver. For example, if the driver is relaxed, a detailed risk prediction can be displayed. If the driver is stressed, a concise risk prediction can be displayed. Furthermore, if the driver is excited, a visually appealing risk prediction can be displayed. In this way, by adjusting the display method of the risk prediction based on the driver's emotions, it becomes possible to display a more appropriate risk prediction.

[0096] The prediction unit can improve the accuracy of its predictions by considering the driver's geographical location when predicting traffic accident risk. For example, the prediction unit predicts traffic accident risk by considering the driver's geographical location. For example, if the driver is in a specific area, it predicts the risk associated with that area. The prediction unit can also identify high-risk areas based on geographical location information. For example, it can identify low-risk areas by referring to geographical location information. Furthermore, the prediction unit can analyze geographical location information and apply the most appropriate prediction method. This makes it possible to predict risks with higher accuracy by considering the driver's geographical location information.

[0097] The prediction unit can improve the accuracy of its predictions when predicting traffic accident risk by referring to relevant literature on drivers. For example, the prediction unit predicts traffic accident risk by referring to relevant literature on drivers. For example, it makes predictions based on data such as academic papers and technical reports. The prediction unit can also identify high-risk driving patterns based on relevant literature. For example, it can identify low-risk driving patterns by referring to relevant literature. Furthermore, the prediction unit can analyze relevant literature and apply the most appropriate prediction method. As a result, by referring to relevant literature on drivers, more accurate risk prediction becomes possible.

[0098] The advice unit can estimate the driver's emotions and adjust the way driving advice is presented based on those emotions. The advice unit estimates the driver's emotions using, for example, an emotion engine or generative AI. For instance, it might use facial recognition or voice analysis to estimate the driver's emotions. Furthermore, the advice unit can adjust the way driving advice is presented based on the estimated emotions. For example, if the driver is relaxed, it can provide detailed driving advice. If the driver is stressed, it can provide concise driving advice. Additionally, if the driver is excited, it can provide visually appealing driving advice. By adjusting the presentation of driving advice based on the driver's emotions, more appropriate driving advice can be provided.

[0099] The advice unit can adjust the level of detail of the advice provided based on the driver's current driving situation. For example, it can adjust the level of detail of the advice based on data such as speed, traffic volume, and road conditions. The advice unit can also provide detailed driving advice by referring to the current driving situation. For example, it can provide concise driving advice based on the current driving situation. Furthermore, the advice unit can analyze the current driving situation and provide the most appropriate driving advice. By adjusting the level of detail of the advice based on the driver's current driving situation, more appropriate driving advice can be provided.

[0100] The advice unit can improve the accuracy of its advice by referring to the driver's past driving history when providing driving advice. For example, the advice unit provides driving advice by referring to the driver's past driving history. For example, it provides advice based on data such as past driving routes and driving time. The advice unit can also provide optimal driving advice based on past driving history. For example, it can provide low-risk driving advice by referring to past driving history. Furthermore, the advice unit can analyze past driving history and provide the most appropriate driving advice. As a result, by referring to the driver's past driving history, more accurate driving advice can be provided.

[0101] The advice unit can estimate the driver's emotions and prioritize advice based on those emotions. The advice unit estimates the driver's emotions using, for example, an emotion engine or generative AI. For instance, it might use facial recognition or voice analysis to estimate the driver's emotions. Furthermore, the advice unit can prioritize advice based on the estimated emotions of the driver. For example, if the driver is relaxed, detailed advice can be prioritized. If the driver is stressed, concise advice can be prioritized. Additionally, if the driver is excited, visually appealing advice can be prioritized. This allows for more appropriate driving advice to be provided by prioritizing advice based on the driver's emotions.

[0102] The advice unit can provide optimal driving advice by considering the driver's geographical location. For example, if the driver is in a specific area, it will provide advice relevant to that area. The advice unit can also provide optimal driving advice based on geographical location. For example, it can provide low-risk driving advice by referring to geographical location. Furthermore, the advice unit can analyze geographical location and provide the most appropriate driving advice. This allows for the provision of more appropriate driving advice by considering the driver's geographical location.

[0103] The advice department can analyze the driver's social media activity when providing driving advice and offer relevant advice. For example, the advice department can analyze the driver's social media activity, such as the content of posts and the number of likes. The advice department can also provide driving advice related to topics of interest based on the driver's social media activity. For example, it can analyze the content of the driver's social media posts and provide relevant driving advice. Furthermore, the advice department can also provide relevant driving advice by referring to the activities of the driver's social media followers and friends. In this way, by analyzing the driver's social media activity, more relevant driving advice can be provided.

[0104] The environmental advice unit can estimate the driver's emotions and adjust the way environmental advice is presented based on those emotions. The environmental advice unit estimates the driver's emotions using, for example, an emotion engine or generative AI. For instance, it might use facial recognition or voice analysis to estimate the driver's emotions. Furthermore, the environmental advice unit can adjust the way environmental advice is presented based on the estimated emotions of the driver. For example, if the driver is relaxed, it can provide detailed environmental advice. If the driver is stressed, it can provide concise environmental advice. Additionally, if the driver is excited, it can provide visually appealing environmental advice. By adjusting the presentation of environmental advice based on the driver's emotions, more appropriate environmental advice can be provided.

[0105] The environmental advice unit can adjust the level of detail of environmental advice based on the driver's current driving conditions when providing it. For example, the environmental advice unit can adjust the level of detail of advice based on the driver's current driving conditions. For example, it can adjust the level of detail of advice based on data such as speed, traffic volume, and road conditions. The environmental advice unit can also provide detailed environmental advice by referring to the current driving conditions. For example, it can provide concise environmental advice based on the current driving conditions. Furthermore, the environmental advice unit can analyze the current driving conditions and provide the most appropriate environmental advice. In this way, by adjusting the level of detail of advice based on the driver's current driving conditions, more appropriate environmental advice can be provided.

[0106] The environmental advice unit can estimate the driver's emotions and prioritize environmental advice based on those emotions. The environmental advice unit estimates the driver's emotions using, for example, an emotion engine or generative AI. For instance, it might use facial recognition or voice analysis to estimate the driver's emotions. Furthermore, the environmental advice unit can prioritize environmental advice based on the estimated driver's emotions. For example, if the driver is relaxed, detailed environmental advice can be prioritized. If the driver is stressed, concise environmental advice can be prioritized. Additionally, if the driver is excited, visually appealing environmental advice can be prioritized. This allows for the provision of more appropriate environmental advice by prioritizing it based on the driver's emotions.

[0107] The Environmental Advice Department can provide optimal environmental advice by considering the driver's geographical location when providing environmental advice. For example, if the driver is in a specific area, the department will provide advice relevant to that area. The Environmental Advice Department can also provide optimal environmental advice based on geographical location information. For example, it will provide low-risk environmental advice by referring to geographical location information. Furthermore, the Environmental Advice Department can analyze geographical location information to provide the most appropriate environmental advice. This allows for the provision of more appropriate environmental advice by considering the driver's geographical location information.

[0108] The simulation unit can estimate the driver's emotions and adjust the simulation content based on the estimated emotions. The simulation unit estimates the driver's emotions using, for example, an emotion engine or generative AI. For example, it may use facial recognition or voice analysis to estimate the driver's emotions. Furthermore, the simulation unit can adjust the simulation content based on the estimated emotions of the driver. For example, if the driver is relaxed, it can provide a detailed simulation. If the driver is stressed, it can provide a concise simulation. Additionally, if the driver is excited, it can provide a visually appealing simulation. By adjusting the simulation content based on the driver's emotions, a more appropriate simulation can be provided.

[0109] The simulation unit can provide the optimal simulation by referring to the driver's past driving history when providing a simulation. For example, the simulation unit provides a simulation by referring to the driver's past driving history. For example, it performs simulations based on data such as past driving routes and driving times. The simulation unit can also provide the optimal simulation based on past driving history. For example, it can provide a low-risk simulation by referring to past driving history. Furthermore, the simulation unit can analyze past driving history and provide the most appropriate simulation. In this way, a more appropriate simulation can be provided by referring to the driver's past driving history.

[0110] The simulation unit can estimate the driver's emotions and determine the priority of simulations based on the estimated emotions. The simulation unit estimates the driver's emotions using, for example, an emotion engine or generative AI. For instance, it might use facial recognition or voice analysis to estimate the driver's emotions. Furthermore, the simulation unit can determine the priority of simulations based on the estimated emotions. For example, if the driver is relaxed, a detailed simulation can be prioritized. If the driver is stressed, a concise simulation can be prioritized. Additionally, if the driver is excited, a visually appealing simulation can be prioritized. This allows for the provision of more appropriate simulations by prioritizing simulations based on the driver's emotions.

[0111] The simulation unit can provide the optimal simulation by considering the driver's geographical location information when providing the simulation. For example, if the driver is in a specific area, the simulation unit will provide a simulation relevant to that area. The simulation unit can also provide the optimal simulation based on geographical location information. For example, it will provide a low-risk simulation by referring to geographical location information. Furthermore, the simulation unit can analyze geographical location information and provide the most appropriate simulation. This allows for the provision of more appropriate simulations by considering the driver's geographical location information.

[0112] The revenue generation unit can estimate the driver's emotions and adjust how ads are displayed based on those estimated emotions. The revenue generation unit can estimate the driver's emotions using, for example, an emotion engine or generative AI. For example, it can use facial recognition or voice analysis to estimate the driver's emotions. Furthermore, the revenue generation unit can adjust how ads are displayed based on the estimated emotions of the driver. For example, if the driver is relaxed, a detailed ad can be displayed. If the driver is stressed, a concise ad can be displayed. Additionally, if the driver is excited, a visually appealing ad can be displayed. This allows for more effective ad display by adjusting how ads are displayed based on the driver's emotions.

[0113] The revenue generation unit can display the most relevant ads by referencing the driver's past purchase history when displaying ads. For example, the revenue generation unit can display ads based on data such as past purchase items and purchase frequency. The revenue generation unit can also display the most relevant ads based on past purchase history. For example, it can display low-risk ads by referring to past purchase history. Furthermore, the revenue generation unit can analyze past purchase history and display the most appropriate ads. This allows for the display of more relevant ads by referring to the driver's past purchase history.

[0114] The revenue generation unit can estimate the driver's emotions and prioritize ads based on those emotions. The unit can estimate the driver's emotions using, for example, an emotion engine or generative AI. For instance, it could use facial recognition or voice analysis. The revenue generation unit can also prioritize ads based on the estimated driver's emotions. For example, if the driver is relaxed, detailed ads can be prioritized. If the driver is stressed, concise ads can be prioritized. Furthermore, if the driver is excited, visually appealing ads can be prioritized. This allows for more effective ad display by prioritizing ads based on the driver's emotions.

[0115] The revenue-generating unit can display the most relevant ads by considering the driver's geographical location when displaying ads. For example, if the driver is in a specific area, it will display ads relevant to that area. The revenue-generating unit can also display the most relevant ads based on geographical location. For example, it will display low-risk ads by referring to geographical location. Furthermore, the revenue-generating unit can analyze geographical location and display the most appropriate ads. This allows for the display of more relevant ads by considering the driver's geographical location.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The autonomous driving AI chatbot system can also monitor the driver's health and provide driving advice based on that health status. For example, it can monitor the driver's heart rate and blood pressure in real time and provide advice such as "Take a break" or "Consult a doctor" if an abnormality is detected. It can also provide advice to encourage the driver to take a break if they are feeling fatigued. Furthermore, based on the driver's health status, it can provide advice on relaxation music and breathing techniques to reduce stress while driving. In this way, by providing driving advice that takes the driver's health status into consideration, it can improve the driver's safety and health.

[0118] The autonomous driving AI chatbot system can estimate the driver's emotions and adjust the tone of driving advice based on those emotions. For example, if the driver is stressed, it can provide advice such as "Please relax" in a gentle tone. If the driver is relaxed, it can provide advice such as "Please slow down" in a normal tone. Furthermore, if the driver is agitated, it can provide advice such as "Please drive calmly" in a calm tone. By providing driving advice in an appropriate tone according to the driver's emotions, this system can improve driver safety.

[0119] The autonomous driving AI chatbot system can provide customized driving advice based on the driver's past driving history, tailored to their driving style. For example, a driver who frequently uses sudden braking in the past might receive advice such as, "To avoid sudden braking, increase the distance between your vehicle and the car in front of you." Similarly, a driver who frequently speeds in the past might receive advice such as, "Please adhere to the speed limit." Furthermore, a driver who has consistently practiced safe driving might receive positive advice such as, "Keep up the good work and continue driving safely." By providing customized driving advice tailored to each driver's style, the system can improve driver safety.

[0120] An autonomous driving AI chatbot system can estimate the driver's emotions and provide entertainment content while driving based on those emotions. For example, if the driver is stressed, it can provide relaxing music or podcasts. If the driver is relaxed, it can provide interesting audiobooks or news. Furthermore, if the driver is agitated, it can provide calming meditation guides or nature sounds. By providing entertainment content tailored to the driver's emotions, it can reduce stress while driving and improve driver safety.

[0121] The autonomous driving AI chatbot system can provide advice to improve fuel efficiency based on the driver's driving style. For example, it can advise drivers who frequently use sudden acceleration and braking to "improve fuel efficiency by avoiding sudden acceleration and braking." It can also advise drivers who idle their engines for long periods to "improve fuel efficiency by reducing idling." Furthermore, it can advise that maintaining appropriate tire pressure improves fuel efficiency. By providing fuel efficiency improvement advice tailored to the driver's driving style, it can reduce environmental impact and lower driver costs.

[0122] The autonomous driving AI chatbot system can estimate the driver's emotions and suggest appropriate rest stops based on those emotions. For example, if the driver is stressed, it might suggest, "Take a break at the next service area." If the driver is relaxed, it might suggest, "Continue driving to the next rest stop." Furthermore, if the driver is agitated, it might suggest, "Take a deep breath and calm down." By suggesting appropriate rest times based on the driver's emotions, it can reduce driver fatigue and promote safe driving.

[0123] The autonomous driving AI chatbot system can perform risk assessments for specific driving patterns based on the driver's past driving history and provide advice to avoid high-risk driving patterns. For example, if there has been a high number of accidents at a particular intersection in the past, it can provide advice such as, "Please be especially careful at this intersection." Similarly, if there has been a high number of accidents during a specific time period in the past, it can provide advice such as, "Please be especially careful during this time period." Furthermore, if there has been a high number of accidents under specific weather conditions in the past, it can provide advice such as, "Please be especially careful under these weather conditions." By providing risk assessments and advice based on the driver's past driving history, this system can improve driver safety.

[0124] The autonomous driving AI chatbot system can estimate the driver's emotions and adjust its communication style during driving based on those emotions. For example, if the driver is stressed, it can communicate in a calm voice, saying, "It's okay, please calm down." If the driver is relaxed, it can communicate in a normal voice, saying, "Please proceed to your next destination." Furthermore, if the driver is agitated, it can communicate in a calm voice, saying, "Take a deep breath and calm down." By providing appropriate communication methods according to the driver's emotions, it can reduce driver stress and promote safe driving.

[0125] The autonomous driving AI chatbot system can provide training programs to improve drivers' driving skills based on their past driving history. For example, a driver who frequently brakes suddenly in the past can be provided with a training program to avoid sudden braking. Similarly, a driver who frequently speeds in the past can be provided with a training program to adhere to speed limits. Furthermore, a driver who frequently changes lanes in the past can be provided with a training program to learn proper lane change techniques. By providing training programs based on a driver's past driving history, the system can improve drivers' driving skills and promote safer driving.

[0126] The autonomous driving AI chatbot system can estimate the driver's emotions and provide warnings while driving based on those emotions. For example, if the driver is stressed, it can provide a warning such as, "Please be careful, there is an obstacle ahead." If the driver is relaxed, it can provide standard warnings such as, "Turn right at the next intersection." Furthermore, if the driver is agitated, it can provide calm warnings such as, "Please drive calmly." By providing appropriate warnings according to the driver's emotions, it can improve driver safety.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The collection unit collects driving data and insurance history. Driving data includes speed, brake usage frequency, and mileage. Insurance history includes past insurance contract details and insurance claim history. The collection unit can collect data to understand the driver's past driving history, insurance usage history, driving style, and risk profile. Step 2: The recommendation department analyzes the data collected by the data collection department and recommends the optimal insurance plan. The recommendation department uses generative AI to recommend insurance plans based on the driver's driving style and risk profile. For example, it recommends lower-premium plans to low-risk drivers and higher-premium plans to high-risk drivers. Step 3: The prediction unit predicts the risk of traffic accidents based on the insurance plan recommended by the recommendation unit. The prediction unit uses generating AI to predict the risk of traffic accidents based on the driver's past driving history and current driving conditions. Step 4: The advice unit provides real-time driving advice based on the risks predicted by the prediction unit. The advice unit uses generated AI to provide the driver with advice such as "Slow down" or "Maintain a safe distance from the vehicle in front" while driving.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the data collection unit, recommendation unit, prediction unit, advice unit, environmental advice unit, simulation unit, and revenue unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects driving data using the camera 42 and sensors of the smart device 14, and the specific processing unit 290 of the data processing unit 12 collects insurance history. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to recommend the optimal insurance plan. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the risk of traffic accidents. The advice unit is implemented by the control unit 46A of the smart device 14 and provides real-time driving advice. The environmental advice unit is implemented by the sensors of the smart device 14 and the specific processing unit 290 of the data processing unit 12 and provides driving advice according to the surrounding environment and weather. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides safe driving training simulations. The revenue-generating section is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12, and its revenue sources include fees from insurance companies and promotions of local businesses utilizing the characteristics of AI-generated advertisements. The correspondence between each section and the devices and control units is not limited to the example described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the data collection unit, recommendation unit, prediction unit, advice unit, environmental advice unit, simulation unit, and revenue unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects driving data using the camera 42 and sensors of the smart glasses 214 and collects insurance history using the identification processing unit 290 of the data processing unit 12. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends the optimal insurance plan by analyzing the collected data. The prediction unit is implemented by the identification processing unit 290 of the data processing unit 12 and predicts the risk of traffic accidents. The advice unit is implemented by the control unit 46A of the smart glasses 214 and provides real-time driving advice. The environmental advice unit is implemented by the sensors of the smart glasses 214 and the identification processing unit 290 of the data processing unit 12 and provides driving advice according to the surrounding environment and weather. The simulation unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides safe driving training simulations. The revenue-generating section is realized by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12, and its revenue sources include fees from insurance companies and promotions of local businesses utilizing the characteristics of AI-generated advertisements. The correspondence between each section and the devices and control units is not limited to the example described above and can be modified in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the data collection unit, recommendation unit, prediction unit, advice unit, environmental advice unit, simulation unit, and revenue unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects driving data using the camera 42 and sensors of the headset terminal 314 and collects insurance history using the specific processing unit 290 of the data processing unit 12. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the optimal insurance plan by analyzing the collected data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the risk of traffic accidents. The advice unit is implemented by the control unit 46A of the headset terminal 314 and provides real-time driving advice. The environmental advice unit is implemented by the sensors of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12 and provides driving advice according to the surrounding environment and weather. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides safe driving training simulations. The revenue-generating section is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing device 12, and its revenue sources include fees from insurance companies and promotions of local businesses utilizing the characteristics of AI-generated advertisements. The correspondence between each section and the devices and control units is not limited to the example described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the data collection unit, recommendation unit, prediction unit, advice unit, environmental advice unit, simulation unit, and revenue unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects driving data using the camera 42 and sensors of the robot 414 and collects insurance history using the identification processing unit 290 of the data processing unit 12. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends the optimal insurance plan by analyzing the collected data. The prediction unit is implemented by the identification processing unit 290 of the data processing unit 12 and predicts the risk of traffic accidents. The advice unit is implemented by the control unit 46A of the robot 414 and provides driving advice in real time. The environmental advice unit is implemented by the sensors of the robot 414 and the identification processing unit 290 of the data processing unit 12 and provides driving advice according to the surrounding environment and weather. The simulation unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides a safe driving training simulation. The revenue-generating section is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12, and its revenue sources include fees from insurance companies and promotions of local businesses utilizing the characteristics of AI-generated advertisements. The correspondence between each section and the devices and control units is not limited to the example described above and can be modified in various ways.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) A collection unit that collects driving data and insurance history, The data collected by the aforementioned collection unit is analyzed by the recommendation unit, which then recommends the most suitable insurance plan. A prediction unit that predicts the risk of traffic accidents based on the insurance plan recommended by the aforementioned recommendation unit, The system includes an advice unit that provides real-time driving advice based on the risks predicted by the prediction unit. A system characterized by the following features. (Note 2) It is equipped with an environmental advice department that provides driving advice tailored to the surrounding environment and weather conditions. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a simulation unit that provides safe driving training simulations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The company has a revenue-generating division that earns revenue from fees from insurance companies and from promoting local businesses using the characteristics of AI-generated advertisements. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect the driver's past driving history and insurance usage history. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, Providing real-time driving advice to the driver while driving. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the driver's emotions and adjusts the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the driver's past driving history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the driver's current driving situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the driver's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the driver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the driver's social media activity is analyzed and relevant data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recommendation department, The system estimates the driver's emotions and adjusts the insurance plan recommendation method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recommendation department, When recommending insurance plans, adjust the level of detail of the recommendation based on the driver's risk profile. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recommendation department, When recommending insurance plans, different recommendation algorithms are applied depending on the driver's category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recommendation department, The system estimates the driver's emotions and adjusts the order of recommended insurance plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recommendation department, When recommending insurance plans, we prioritize recommendations based on the driver's past insurance claim history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recommendation department, When recommending insurance plans, we improve the accuracy of recommendations by referencing relevant market data for drivers. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, We estimate the driver's emotions and adjust the method of predicting traffic accident risk based on the estimated driver's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, When predicting traffic accident risk, the accuracy of the prediction is improved by referring to the driver's past driving history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When predicting traffic accident risk, the prediction algorithm is optimized based on the driver's current driving behavior. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, The system estimates the driver's emotions and adjusts how risk predictions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, When predicting traffic accident risk, consider the driver's geographical location to improve the accuracy of the prediction. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When predicting traffic accident risk, referencing relevant literature on drivers improves the accuracy of predictions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, The system estimates the driver's emotions and adjusts the way driving advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, When providing driving advice, the level of detail in the advice is adjusted based on the driver's current driving situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, When providing driving advice, we refer to the driver's past driving history to improve the accuracy of the advice. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, The system estimates the driver's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, When providing driving advice, the driver's geographical location information is taken into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advice section, When providing driving advice, we analyze the driver's social media activity and provide relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned environmental advice department, The system estimates the driver's emotions and adjusts the way environmental advice is presented based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned environmental advice department, When providing environmental advice, the level of detail in the advice will be adjusted based on the driver's current driving situation. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned environmental advice department, The system estimates the driver's emotions and prioritizes environmental advice based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned environmental advice department, When providing environmental advice, we will consider the driver's geographical location to provide the most appropriate advice. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned simulation unit, The system estimates the driver's emotions and adjusts the simulation based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned simulation unit, When providing a simulation, the system will refer to the driver's past driving history to provide the optimal simulation. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned simulation unit, The system estimates the driver's emotions and determines the priority of the simulation based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned simulation unit, When providing simulations, we take into account the driver's geographical location information to provide the optimal simulation. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned revenue-generating section is, The system estimates the driver's emotions and adjusts how ads are displayed based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned revenue-generating section is, When displaying ads, the system uses the driver's past purchase history to show the most relevant ads. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned revenue-generating section is, The system estimates the driver's emotions and prioritizes advertisements based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned revenue-generating section is, When displaying ads, the system takes into account the driver's geographical location to show the most relevant ads. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects driving data and insurance history, The data collected by the aforementioned collection unit is analyzed by the recommendation unit, which then recommends the most suitable insurance plan. A prediction unit that predicts the risk of traffic accidents based on the insurance plan recommended by the aforementioned recommendation unit, The system includes an advice unit that provides real-time driving advice based on the risks predicted by the prediction unit. A system characterized by the following features.

2. It is equipped with an environmental advice department that provides driving advice tailored to the surrounding environment and weather conditions. The system according to feature 1.

3. It includes a simulation unit that provides safe driving training simulations. The system according to feature 1.

4. The company has a revenue-generating division that earns revenue from fees from insurance companies and from promoting local businesses using the characteristics of AI-generated advertisements. The system according to feature 1.

5. The aforementioned collection unit is Collect the driver's past driving history and insurance usage history. The system according to feature 1.

6. The aforementioned advice section, Providing real-time driving advice to the driver while driving. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the driver's emotions and adjusts the timing of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the driver's past driving history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is During data collection, filtering is performed based on the driver's current driving situation and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is The system estimates the driver's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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