system

Abstract

The system according to this embodiment aims to evaluate the driving ability of elderly drivers and use the results to help prevent accidents. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a cooperation unit. The collection unit collects driving data of elderly people. The analysis unit analyzes the data collected by the collection unit and evaluates driving ability. The generation unit generates a diagnostic report based on the evaluation results obtained by the analysis unit. The provision unit provides the diagnostic report generated by the generation unit. The cooperation unit cooperates with local governments and families based on the diagnostic report provided by the provision 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 performed by at least one processor, the method including 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 cannot be said that the driving ability of elderly drivers has been sufficiently evaluated and utilized for accident prevention, and there is room for improvement.

[0005] The system according to the embodiment aims to evaluate the driving ability of elderly drivers and utilize it for accident prevention.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, a provision unit, and a collaboration unit. The data collection unit collects driving data of elderly people. The analysis unit analyzes the data collected by the data collection unit and evaluates driving ability. The generation unit generates a diagnostic report based on the evaluation results obtained by the analysis unit. The provision unit provides the diagnostic report generated by the generation unit. The collaboration unit collaborates with local governments and families based on the diagnostic report provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can evaluate the driving ability of elderly drivers and help prevent 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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 elderly driver accident prevention system according to an embodiment of the present invention is a system aimed at preventing accidents involving elderly drivers in a super-aging society. This system collects driving data from elderly drivers, and an AI analyzes this data to evaluate their driving ability. For example, changes in reaction speed and cognitive ability while driving are collected as data. This data is analyzed by the AI ​​to evaluate the elderly driver's driving ability. Next, a daily diagnostic report is generated based on the data analyzed by the AI. This report includes the evaluation results of the elderly driver's driving ability, allowing for real-time confirmation of driving safety. For example, if the reaction speed while driving is slowing down, this fact will be noted in the report. Furthermore, if the limit of driving ability is approaching, the AI ​​will cooperate with local governments and families to support the voluntary surrender of the driver's license. Specifically, when the AI ​​detects a decline in the elderly driver's driving ability, it notifies the local government and family of this information and supports the license surrender procedure. For example, information on the procedure for surrendering a license and the necessary documents will be provided. This system will help prevent accidents involving elderly drivers and improve traffic safety for society as a whole. For example, the risk of elderly drivers causing accidents while driving will be reduced. Furthermore, to ensure that elderly people can live safely even after surrendering their driver's licenses, they are advised to use public transportation and mobility assistance services. This helps maintain the quality of life for the elderly. As a result, the elderly driver accident prevention system can collect, analyze, evaluate, provide, and link elderly drivers' driving data, making their driving abilities visible and contributing to accident prevention.

[0029] The elderly driver accident prevention system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a coordination unit. The collection unit collects driving data of elderly drivers. For example, the collection unit collects data on changes in reaction speed and cognitive ability while driving. The collection unit can also collect data on brake reaction time and steering speed while driving. The collection unit can also collect data on changes in eye movement and attention while driving. The analysis unit analyzes the data collected by the collection unit and evaluates the elderly driver's driving ability. For example, the analysis unit evaluates changes in reaction speed and cognitive ability based on the collected data. The analysis unit can also evaluate changes in judgment and attention while driving based on the collected data. The analysis unit can also evaluate changes in eye movement and reaction time while driving based on the collected data. The generation unit generates a diagnostic report based on the evaluation results obtained by the analysis unit. For example, the generation unit generates a daily diagnostic report based on the analysis results. The generation unit can also generate a diagnostic report that includes an evaluation of driving ability based on the analysis results. The generation unit can, for example, generate a diagnostic report that includes areas for improvement in driving ability and recommendations based on the analysis results. The provision unit provides the diagnostic report generated by the generation unit. The provision unit provides the generated diagnostic report to, for example, the elderly person or their family. The provision unit can also provide the generated diagnostic report to, for example, local governments or medical institutions. The provision unit can also provide the generated diagnostic report through, for example, a web application or a mobile application. The collaboration unit collaborates with local governments and families based on the diagnostic report provided by the provision unit. The collaboration unit collaborates with local governments and families based on the diagnostic report to support the voluntary surrender of the driver's license. The collaboration unit can also collaborate with local governments and families based on the diagnostic report to support the license surrender process. The collaboration unit can also collaborate with local governments and families based on the diagnostic report to provide support for life after license surrender. Thus, the elderly driver accident prevention system according to this embodiment can visualize driving ability and contribute to accident prevention by collecting, analyzing, evaluating, providing, and collaborating on elderly people's driving data.

[0030] The data collection unit collects driving data from elderly drivers. For example, the unit collects data on changes in reaction speed and cognitive ability while driving. Specifically, it uses sensors and cameras mounted in the vehicle to monitor brake reaction time, steering speed, eye movement, and changes in attention in real time while driving. This data is recorded in a data logger inside the vehicle and transmitted to a central server via wireless communication. The data collection unit can collect data on brake reaction time and steering speed while driving. Brake reaction time is measured by the time it takes for the driver to press the brake pedal, and steering speed is detected by sensors that measure the rotation speed and angle of the steering wheel. This allows for evaluation of the driver's reaction speed and the accuracy of their actions. The data collection unit can also collect data on eye movement and changes in attention while driving. Eye movement is tracked by cameras installed inside the vehicle, and changes in attention are evaluated by analyzing the degree of focus and deviation of the gaze while driving. This allows for a detailed understanding of changes in the driver's attention and cognitive ability. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit to evaluate the driving ability of elderly drivers. For example, the analysis unit evaluates changes in reaction speed and cognitive ability based on the collected data. Specifically, it uses AI to analyze the collected data in real time and evaluate changes in the driver's reaction speed and cognitive ability. The AI ​​uses machine learning algorithms to detect changes in the driver's driving patterns and reactions by comparing them with past data. For example, if the brake reaction time is longer than usual or the steering becomes unstable, the AI ​​detects this as an abnormality and suggests that there may be a problem with the driver's driving ability. The analysis unit can also evaluate changes in judgment and attention while driving based on the collected data. The AI ​​analyzes eye movement data to evaluate how much attention the driver is paying to the road and surrounding conditions. For example, if the driver's gaze frequently wanders or if they concentrate in a particular direction for a long time, the AI ​​detects this as a decrease in attention. Furthermore, the analysis unit can also evaluate changes in eye movement and reaction time while driving based on the collected data. The AI ​​integrates eye movement data and reaction time data to comprehensively evaluate changes in the driver's cognitive ability and reaction speed. This allows the analysis unit to quickly and accurately analyze collected data and evaluate the driving abilities of elderly drivers in detail. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term fluctuations and trends in driving ability. For example, based on past driving data, it can predict fluctuations in driving ability at specific times or situations and assess future risks. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis unit can not only perform real-time driving ability evaluation but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The generation unit generates diagnostic reports based on the evaluation results obtained by the analysis unit. For example, the generation unit generates daily diagnostic reports based on the analysis results. Specifically, it uses AI to automatically organize the analysis results and create detailed reports on the driver's driving ability. The reports include evaluation results of changes in reaction speed and cognitive ability, as well as judgment and attention, visualizing the current state of the driver's driving ability. The generation unit can also generate diagnostic reports that include driving ability evaluation results based on the analysis results. The reports include specific data such as brake reaction time, steering speed, and eye movement patterns during driving, providing a detailed evaluation of the driver's driving ability. The generation unit can also generate diagnostic reports that include areas for improvement and recommendations for driving ability based on the analysis results. The reports specifically describe areas for improvement and recommendations for driving ability, providing advice on how the driver can improve their driving ability. For example, if reaction speed is reduced, the reports will include training methods to improve reaction speed and advice on how to enhance attention. The generation unit can automatically generate these reports and provide them to the driver and relevant parties. This allows the generation unit to quickly and accurately generate detailed diagnostic reports based on analysis results, which can be used to evaluate and improve drivers' driving abilities. Furthermore, the generation unit can customize the content of the reports, creating reports tailored to specific needs and requirements. For example, reports for local governments and medical institutions may include not only driving ability evaluation results but also recommendations for license surrender and suggestions for lifestyle support. This enables the generation unit to provide appropriate information to various stakeholders and contribute to preventing accidents involving elderly drivers.

[0033] The service provider provides the diagnostic reports generated by the generation service provider. For example, the service provider provides the generated diagnostic reports to elderly individuals and their families. Specifically, it provides the diagnostic reports to elderly individuals and their families through web applications and mobile applications, sharing the results of the driving ability assessment and areas for improvement. The service provider can also provide the generated diagnostic reports to local governments and medical institutions. Based on the provided reports, local governments and medical institutions can assess the driving ability of elderly drivers and take necessary support and measures. The service provider can also provide the generated diagnostic reports through web applications and mobile applications. This allows elderly individuals, their families, local governments, and medical institutions to access the diagnostic reports and check the results of the driving ability assessment anytime, anywhere. The service provider can flexibly choose how to provide the diagnostic reports and provide information in the most optimal way according to user needs. For example, it is possible to provide reports not only through web applications and mobile applications, but also via email and printed materials. This allows the service provider to provide diagnostic reports in various ways and quickly provide appropriate information to elderly drivers and related parties. Furthermore, in addition to providing diagnostic reports, the service provider can collect feedback from users and use it to improve the content and delivery methods of the reports. For example, if the report content is difficult to understand or if users are dissatisfied with the delivery method, improvements will be made based on user feedback. This allows the service provider to consistently provide optimal information tailored to user needs, contributing to accident prevention among elderly drivers.

[0034] The Liaison Department will collaborate with local governments and families based on the diagnostic reports provided by the Service Provider Department. For example, the Liaison Department will collaborate with local governments and families based on the diagnostic reports to support the voluntary surrender of licenses. Specifically, based on the contents of the diagnostic report, if there are problems with the driving ability of elderly drivers, the Liaison Department will collaborate with local governments and families to encourage the voluntary surrender of licenses. The Liaison Department can also collaborate with local governments and families based on the diagnostic reports to support the license surrender process. They will prepare necessary documents and provide guidance on the procedures to ensure that the license surrender process proceeds smoothly. The Liaison Department can also collaborate with local governments and families based on the diagnostic reports to provide support for life after license surrender. If assistance is needed for securing transportation or daily life after license surrender, the Liaison Department will collaborate with local governments and families to provide appropriate support. For example, they will provide guidance on how to use public transportation and taxi services, and introduce shopping assistance services. In this way, the Liaison Department can support the lives of elderly drivers after license surrender and provide an environment in which they can live with peace of mind. Furthermore, the Liaison Department can strengthen collaboration with local governments and families and provide continuous support. For example, by establishing regular follow-ups and consultation services, a system will be put in place where elderly drivers and their families can seek advice at any time. This will allow the liaison department to contribute not only to accident prevention for elderly drivers but also to supporting their lives after surrendering their licenses, providing comprehensive support.

[0035] The data collection unit can collect data on changes in reaction speed and cognitive ability while driving. For example, the data collection unit can collect data on brake reaction time while driving. The data collection unit can also collect data on the speed of steering wheel operation while driving. The data collection unit can also collect data on eye movement while driving. By collecting data on changes in reaction speed and cognitive ability while driving, the accuracy of evaluating driving ability is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input brake reaction time while driving into AI and have AI analyze changes in reaction time.

[0036] The analysis unit can analyze the collected data and evaluate the driving ability of elderly people. For example, the analysis unit can evaluate changes in reaction speed and cognitive ability based on the collected data. For example, the analysis unit can also evaluate changes in judgment and attention while driving based on the collected data. For example, the analysis unit can evaluate changes in eye movement and reaction time while driving based on the collected data. In this way, by analyzing the collected data, the driving ability of elderly people can be accurately evaluated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform the evaluation of driving ability.

[0037] The generation unit can generate daily diagnostic reports based on the analysis results. For example, the generation unit can generate a diagnostic report that includes an evaluation of driving ability based on the analysis results. The generation unit can also generate a diagnostic report that includes areas for improvement and recommendations for driving ability based on the analysis results. The generation unit can also generate a diagnostic report that allows for real-time monitoring of changes in driving ability based on the analysis results. This allows for real-time monitoring of changes in driving ability by generating daily diagnostic reports based on the analysis results. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into AI and have AI generate the diagnostic report.

[0038] The service provider can provide the generated diagnostic report. The service provider can, for example, provide the generated diagnostic report to the elderly person or their family. The service provider can also, for example, provide the generated diagnostic report to local governments or medical institutions. The service provider can also, for example, provide the generated diagnostic report through a web application or a mobile application. This allows the elderly person or their family to check their driving ability by providing the generated diagnostic report. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated diagnostic report into an AI and have the AI ​​perform the task of providing the report.

[0039] The liaison department can collaborate with local governments and families based on the diagnostic report to support the voluntary surrender of a driver's license. For example, the liaison department can collaborate with local governments and families based on the diagnostic report to support the license surrender process. For example, the liaison department can also collaborate with local governments and families based on the diagnostic report to provide support for life after license surrender. For example, the liaison department can collaborate with local governments and families based on the diagnostic report to provide guidance on the procedures and necessary documents for license surrender. In this way, by collaborating with local governments and families based on the diagnostic report, the liaison department can support the voluntary surrender of a driver's license and improve traffic safety. Some or all of the above processes in the liaison department may be performed using AI, for example, or not using AI. For example, the liaison department can input the diagnostic report into AI and have the AI ​​perform the collaboration with local governments and families.

[0040] The data collection unit can analyze the elderly person's past driving history and select the optimal data collection method. For example, the data collection unit can collect data during the times when the elderly person frequently drove in the past. The data collection unit can also collect data at specific points based on the routes the elderly person has driven in the past. For example, the data collection unit can adjust the timing of data collection by considering the frequency and duration of driving based on the elderly person's past driving history. This allows for the selection of the optimal data collection method by analyzing the elderly person's past driving history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's past driving history data into AI and have the AI ​​select the optimal data collection method.

[0041] The data collection unit can filter driving data based on the elderly person's current health status and medication information. For example, if the elderly person is taking medication, the data collection unit will take the effects of the medication into consideration. For example, if the elderly person is in good health, the data collection unit can also collect detailed driving data. For example, if the elderly person is unwell, the data collection unit can refrain from collecting data and resume it after their health has recovered. This allows for the collection of more accurate driving data by filtering the data based on the elderly person's health status and medication information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's health status and medication information into the AI ​​and have the AI ​​perform the data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of elderly drivers when collecting driving data. For example, the data collection unit can collect data by considering the traffic conditions in the area where the elderly driver is driving. For example, the data collection unit can identify dangerous locations on the route the elderly driver is driving and prioritize the collection of data at those locations. For example, the data collection unit can collect data by considering the weather information in the area where the elderly driver is driving. This allows for the priority collection of highly relevant data by considering the geographical location information of elderly drivers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of elderly drivers into the AI ​​and have the AI ​​perform the priority collection of highly relevant data.

[0043] The data collection unit can analyze the social media activities of elderly drivers and collect relevant data when collecting driving data. For example, the data collection unit can collect data based on driving experiences shared by elderly drivers on social media. The data collection unit can also collect data considering, for example, problems drivers mention on social media. The data collection unit can also estimate driving emotions and stress levels from the elderly drivers' social media activities and collect relevant data. In this way, relevant data can be collected by analyzing the social media activities of elderly drivers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly drivers' social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0044] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of driving data during the analysis. For example, the analysis unit can analyze the interrelationship between brake reaction time and steering speed. For example, the analysis unit can also analyze the interrelationship between eye movement and reaction speed during driving. For example, the analysis unit can also analyze the interrelationship between changes in attention and concentration during driving. By considering the interrelationships of driving data, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships of driving data into the AI ​​and have the AI ​​perform the improvement of the analysis accuracy.

[0045] The analysis unit can perform analysis while considering the elderly person's driving history and health data. For example, the analysis unit can analyze changes in driving ability based on the elderly person's past driving history. The analysis unit can also analyze changes in reaction speed and cognitive ability while driving, for example, by considering the elderly person's health data. The analysis unit can also analyze the interrelationship between the elderly person's driving history and health data. This allows for more accurate analysis by considering the elderly person's driving history and health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the elderly person's driving history and health data into AI and have the AI ​​perform the analysis.

[0046] The analysis unit can perform analysis while considering the geographical distribution of driving data. For example, the analysis unit can perform analysis while considering traffic conditions in areas where elderly people drive. For example, the analysis unit can identify dangerous locations on routes driven by elderly people and analyze data at those locations. For example, the analysis unit can perform analysis while considering weather information in areas where elderly people drive. This allows for more accurate analysis by considering the geographical distribution of driving data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of driving data into AI and have AI perform the analysis.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on driving data during the analysis process. For example, the analysis unit can refer to the latest research papers related to the analysis of driving data. The analysis unit can also perform analysis based on past research results on the driving ability of elderly people. The analysis unit can also improve its analysis algorithm by referring to relevant literature on driving data. This improves the accuracy of the analysis by referring to relevant literature on driving data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature on driving data into AI and have AI perform the analysis to improve accuracy.

[0048] The generation unit can adjust the level of detail in a diagnostic report based on the importance of the driving data. For example, the generation unit can generate a detailed report based on important driving data. The generation unit can also generate a concise report based on less important driving data. The generation unit can also adjust the level of detail in the report according to the importance of the driving data. This allows the necessary information to be appropriately provided by adjusting the level of detail in the report based on the importance of the driving data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the driving data into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the report.

[0049] The generation unit can apply different generation algorithms depending on the category of driving data when generating a diagnostic report. For example, the generation unit can apply a specific algorithm to data related to reaction speed to generate a report. For example, the generation unit can also apply a different algorithm to data related to cognitive ability to generate a report. The generation unit can also select the optimal generation algorithm depending on the category of driving data. By applying the optimal generation algorithm according to the category of driving data, a highly accurate report can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the categories of driving data into the AI ​​and have the AI ​​execute the application of the generation algorithm.

[0050] The generation unit can determine the priority of reports based on the timing of driving data collection when generating diagnostic reports. For example, the generation unit can prioritize generating reports based on the most recent driving data. The generation unit can also generate reports based on past driving data. The generation unit can also adjust the priority of reports according to the timing of driving data collection. This allows for the provision of the latest information by prioritizing reports based on the timing of driving data collection. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of driving data collection into the AI ​​and have the AI ​​determine the priority of reports.

[0051] The generation unit can adjust the order of reports based on the relevance of driving data when generating diagnostic reports. For example, the generation unit determines the order of reports based on important driving data. The generation unit can also, for example, prioritize the inclusion of highly relevant driving data in the reports. The generation unit can also adjust the order of reports according to the relevance of the driving data. This allows important information to be provided preferentially by adjusting the order of reports based on the relevance of the driving data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the driving data into the AI ​​and have the AI ​​perform the adjustment of the report order.

[0052] The service provider can select the optimal service delivery method by referring to the elderly person's past driving history when providing reports. For example, the service provider can prioritize the service delivery method that the elderly person has preferred to use in the past. For example, the service provider can also select the optimal delivery timing based on the elderly person's past driving history. For example, the service provider can adjust the frequency of report delivery based on the elderly person's past driving history. This allows the service provider to select the optimal service delivery method by referring to the elderly person's past driving history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the elderly person's past driving history data into AI and have the AI ​​select the optimal service delivery method.

[0053] The service provider can select the optimal delivery method when providing reports, taking into account the elderly person's device information. For example, if the elderly person is using a smartphone, the service provider can provide the report using a display method adapted to the screen size. If the elderly person is using a tablet, the service provider can also provide the report using a display method optimized for a larger screen. If the elderly person is using a personal computer, the service provider can also provide the report in a way that includes detailed information. This allows the service provider to provide reports in the most optimal way by taking into account the elderly person's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the elderly person's device information into AI and have the AI ​​select the optimal delivery method.

[0054] The collaboration unit can select the optimal collaboration method by referring to the elderly person's past driving history and health data during collaboration. For example, the collaboration unit can select the optimal collaboration timing based on the elderly person's past driving history. The collaboration unit can also select the optimal collaboration method by considering the elderly person's health data. The collaboration unit can also adjust the collaboration method based on the interrelationship between the elderly person's past driving history and health data. This allows the optimal collaboration method to be selected by referring to the elderly person's past driving history and health data. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the elderly person's past driving history and health data into AI and have the AI ​​select the optimal collaboration method.

[0055] The collaboration unit can select the optimal collaboration method by considering the geographical location information of the elderly person during the collaboration process. For example, the collaboration unit can collaborate with the local government in the area where the elderly person lives. The collaboration unit can also collaborate with family members who live near places that the elderly person frequently visits. For example, the collaboration unit can select the optimal collaboration method based on the geographical location information of the elderly person. This allows for the selection of the optimal collaboration method by considering the geographical location information of the elderly person. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the geographical location information of the elderly person into AI and have AI select the optimal collaboration method.

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

[0057] The elderly driver accident prevention system can collect and analyze not only driving data but also voice data while driving. For example, it can collect changes in the tone of voice and speaking style of elderly drivers to detect signs of stress and fatigue. The collection unit collects voice data while driving, and the analysis unit can analyze this data to help evaluate driving ability. The generation unit generates a diagnostic report that includes the results of the voice data analysis, and the provision unit provides this report to the elderly driver and their family. This allows for a more detailed understanding of the elderly driver's condition while driving by utilizing the voice data, which can help prevent accidents.

[0058] The elderly driver accident prevention system can collect and analyze in-vehicle environmental data in addition to driving data. For example, it can collect data on the temperature, humidity, and lighting conditions inside the vehicle to identify factors that affect driving ability. The collection unit collects in-vehicle environmental data, and the analysis unit can analyze this data to help evaluate driving ability. The generation unit generates a diagnostic report that includes the analysis results of the in-vehicle environmental data, and the provision unit provides this report to the elderly driver or their family. By utilizing in-vehicle environmental data, it becomes possible to understand the condition of elderly drivers in more detail and use this information to help prevent accidents.

[0059] The elderly driver accident prevention system can collect and analyze not only driving data but also biometric data during driving. For example, it can collect biometric data such as heart rate, blood pressure, and body temperature to identify factors that affect driving ability. The collection unit collects biometric data, and the analysis unit analyzes that data to help evaluate driving ability. The generation unit generates a diagnostic report including the results of the biometric data analysis, and the provision unit provides this report to the elderly driver or their family. In this way, by utilizing biometric data, the condition of elderly drivers can be understood in more detail, which helps in accident prevention.

[0060] The elderly driver accident prevention system can collect and analyze vehicle data in addition to driving data. For example, it can collect data such as vehicle speed, acceleration, and brake usage frequency to identify factors that affect driving ability. The collection unit collects vehicle data, and the analysis unit analyzes that data to help evaluate driving ability. The generation unit generates a diagnostic report that includes the results of the vehicle data analysis, and the provision unit provides this report to the elderly driver or their family. This allows for a more detailed understanding of the elderly driver's condition while driving by utilizing vehicle data, which helps in accident prevention.

[0061] The system for preventing accidents involving elderly drivers can collect and analyze not only driving data but also road condition data while driving. For example, it can collect data such as road congestion, weather, and road surface conditions to identify factors that affect driving ability. The collection unit collects road condition data, and the analysis unit analyzes this data to help evaluate driving ability. The generation unit generates a diagnostic report that includes the analysis results of the road condition data, and the provision unit provides this report to the elderly driver or their family. This allows for a more detailed understanding of the elderly driver's condition while driving by utilizing road condition data, which helps in accident prevention.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects driving data from elderly drivers. For example, it collects data on changes in reaction speed and cognitive ability while driving, braking reaction time and steering speed, eye movement and attention changes, etc. Step 2: The analysis unit analyzes the data collected by the data collection unit to evaluate the driving ability of elderly drivers. For example, based on the collected data, it evaluates changes in reaction speed and cognitive ability, changes in judgment and attention while driving, and changes in eye movement and reaction time. Step 3: The generation unit generates a diagnostic report based on the evaluation results obtained by the analysis unit. For example, it generates a diagnostic report that includes daily diagnostic reports, driving performance evaluation results, areas for improvement, and recommendations based on the analysis results. Step 4: The supply unit provides the diagnostic report generated by the generation unit. For example, the generated diagnostic report can be provided to the elderly, their families, local governments, or medical institutions, and can also be provided through web applications or mobile applications. Step 5: The liaison department collaborates with local governments and families based on the diagnostic report provided by the service provider. For example, based on the diagnostic report, they collaborate with local governments and families to support voluntary surrender of the driver's license, assist with the license surrender process, and provide support for life after the license is surrendered.

[0064] (Example of form 2) The elderly driver accident prevention system according to an embodiment of the present invention is a system aimed at preventing accidents involving elderly drivers in a super-aging society. This system collects driving data from elderly drivers, and an AI analyzes this data to evaluate their driving ability. For example, changes in reaction speed and cognitive ability while driving are collected as data. This data is analyzed by the AI ​​to evaluate the elderly driver's driving ability. Next, a daily diagnostic report is generated based on the data analyzed by the AI. This report includes the evaluation results of the elderly driver's driving ability, allowing for real-time confirmation of driving safety. For example, if the reaction speed while driving is slowing down, this fact will be noted in the report. Furthermore, if the limit of driving ability is approaching, the AI ​​will cooperate with local governments and families to support the voluntary surrender of the driver's license. Specifically, when the AI ​​detects a decline in the elderly driver's driving ability, it notifies the local government and family of this information and supports the license surrender procedure. For example, information on the procedure for surrendering a license and the necessary documents will be provided. This system will help prevent accidents involving elderly drivers and improve traffic safety for society as a whole. For example, the risk of elderly drivers causing accidents while driving will be reduced. Furthermore, to ensure that elderly people can live safely even after surrendering their driver's licenses, they are advised to use public transportation and mobility assistance services. This helps maintain the quality of life for the elderly. As a result, the elderly driver accident prevention system can collect, analyze, evaluate, provide, and link elderly drivers' driving data, making their driving abilities visible and contributing to accident prevention.

[0065] The elderly driver accident prevention system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a coordination unit. The collection unit collects driving data of elderly drivers. For example, the collection unit collects data on changes in reaction speed and cognitive ability while driving. The collection unit can also collect data on brake reaction time and steering speed while driving. The collection unit can also collect data on changes in eye movement and attention while driving. The analysis unit analyzes the data collected by the collection unit and evaluates the elderly driver's driving ability. For example, the analysis unit evaluates changes in reaction speed and cognitive ability based on the collected data. The analysis unit can also evaluate changes in judgment and attention while driving based on the collected data. The analysis unit can also evaluate changes in eye movement and reaction time while driving based on the collected data. The generation unit generates a diagnostic report based on the evaluation results obtained by the analysis unit. For example, the generation unit generates a daily diagnostic report based on the analysis results. The generation unit can also generate a diagnostic report that includes an evaluation of driving ability based on the analysis results. The generation unit can, for example, generate a diagnostic report that includes areas for improvement in driving ability and recommendations based on the analysis results. The provision unit provides the diagnostic report generated by the generation unit. The provision unit provides the generated diagnostic report to, for example, the elderly person or their family. The provision unit can also provide the generated diagnostic report to, for example, local governments or medical institutions. The provision unit can also provide the generated diagnostic report through, for example, a web application or a mobile application. The collaboration unit collaborates with local governments and families based on the diagnostic report provided by the provision unit. The collaboration unit collaborates with local governments and families based on the diagnostic report to support the voluntary surrender of the driver's license. The collaboration unit can also collaborate with local governments and families based on the diagnostic report to support the license surrender process. The collaboration unit can also collaborate with local governments and families based on the diagnostic report to provide support for life after license surrender. Thus, the elderly driver accident prevention system according to this embodiment can visualize driving ability and contribute to accident prevention by collecting, analyzing, evaluating, providing, and collaborating on elderly people's driving data.

[0066] The data collection unit collects driving data from elderly drivers. For example, the unit collects data on changes in reaction speed and cognitive ability while driving. Specifically, it uses sensors and cameras mounted in the vehicle to monitor brake reaction time, steering speed, eye movement, and changes in attention in real time while driving. This data is recorded in a data logger inside the vehicle and transmitted to a central server via wireless communication. The data collection unit can collect data on brake reaction time and steering speed while driving. Brake reaction time is measured by the time it takes for the driver to press the brake pedal, and steering speed is detected by sensors that measure the rotation speed and angle of the steering wheel. This allows for evaluation of the driver's reaction speed and the accuracy of their actions. The data collection unit can also collect data on eye movement and changes in attention while driving. Eye movement is tracked by cameras installed inside the vehicle, and changes in attention are evaluated by analyzing the degree of focus and deviation of the gaze while driving. This allows for a detailed understanding of changes in the driver's attention and cognitive ability. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis unit analyzes the data collected by the collection unit to evaluate the driving ability of elderly drivers. For example, the analysis unit evaluates changes in reaction speed and cognitive ability based on the collected data. Specifically, it uses AI to analyze the collected data in real time and evaluate changes in the driver's reaction speed and cognitive ability. The AI ​​uses machine learning algorithms to detect changes in the driver's driving patterns and reactions by comparing them with past data. For example, if the brake reaction time is longer than usual or the steering becomes unstable, the AI ​​detects this as an abnormality and suggests that there may be a problem with the driver's driving ability. The analysis unit can also evaluate changes in judgment and attention while driving based on the collected data. The AI ​​analyzes eye movement data to evaluate how much attention the driver is paying to the road and surrounding conditions. For example, if the driver's gaze frequently wanders or if they concentrate in a particular direction for a long time, the AI ​​detects this as a decrease in attention. Furthermore, the analysis unit can also evaluate changes in eye movement and reaction time while driving based on the collected data. The AI ​​integrates eye movement data and reaction time data to comprehensively evaluate changes in the driver's cognitive ability and reaction speed. This allows the analysis unit to quickly and accurately analyze collected data and evaluate the driving abilities of elderly drivers in detail. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term fluctuations and trends in driving ability. For example, based on past driving data, it can predict fluctuations in driving ability at specific times or situations and assess future risks. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis unit can not only perform real-time driving ability evaluation but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0068] The generation unit generates diagnostic reports based on the evaluation results obtained by the analysis unit. For example, the generation unit generates daily diagnostic reports based on the analysis results. Specifically, it uses AI to automatically organize the analysis results and create detailed reports on the driver's driving ability. The reports include evaluation results of changes in reaction speed and cognitive ability, as well as judgment and attention, visualizing the current state of the driver's driving ability. The generation unit can also generate diagnostic reports that include driving ability evaluation results based on the analysis results. The reports include specific data such as brake reaction time, steering speed, and eye movement patterns during driving, providing a detailed evaluation of the driver's driving ability. The generation unit can also generate diagnostic reports that include areas for improvement and recommendations for driving ability based on the analysis results. The reports specifically describe areas for improvement and recommendations for driving ability, providing advice on how the driver can improve their driving ability. For example, if reaction speed is reduced, the reports will include training methods to improve reaction speed and advice on how to enhance attention. The generation unit can automatically generate these reports and provide them to the driver and relevant parties. This allows the generation unit to quickly and accurately generate detailed diagnostic reports based on analysis results, which can be used to evaluate and improve drivers' driving abilities. Furthermore, the generation unit can customize the content of the reports, creating reports tailored to specific needs and requirements. For example, reports for local governments and medical institutions may include not only driving ability evaluation results but also recommendations for license surrender and suggestions for lifestyle support. This enables the generation unit to provide appropriate information to various stakeholders and contribute to preventing accidents involving elderly drivers.

[0069] The service provider provides the diagnostic reports generated by the generation service provider. For example, the service provider provides the generated diagnostic reports to elderly individuals and their families. Specifically, it provides the diagnostic reports to elderly individuals and their families through web applications and mobile applications, sharing the results of the driving ability assessment and areas for improvement. The service provider can also provide the generated diagnostic reports to local governments and medical institutions. Based on the provided reports, local governments and medical institutions can assess the driving ability of elderly drivers and take necessary support and measures. The service provider can also provide the generated diagnostic reports through web applications and mobile applications. This allows elderly individuals, their families, local governments, and medical institutions to access the diagnostic reports and check the results of the driving ability assessment anytime, anywhere. The service provider can flexibly choose how to provide the diagnostic reports and provide information in the most optimal way according to user needs. For example, it is possible to provide reports not only through web applications and mobile applications, but also via email and printed materials. This allows the service provider to provide diagnostic reports in various ways and quickly provide appropriate information to elderly drivers and related parties. Furthermore, in addition to providing diagnostic reports, the service provider can collect feedback from users and use it to improve the content and delivery methods of the reports. For example, if the report content is difficult to understand or if users are dissatisfied with the delivery method, improvements will be made based on user feedback. This allows the service provider to consistently provide optimal information tailored to user needs, contributing to accident prevention among elderly drivers.

[0070] The Liaison Department will collaborate with local governments and families based on the diagnostic reports provided by the Service Provider Department. For example, the Liaison Department will collaborate with local governments and families based on the diagnostic reports to support the voluntary surrender of licenses. Specifically, based on the contents of the diagnostic report, if there are problems with the driving ability of elderly drivers, the Liaison Department will collaborate with local governments and families to encourage the voluntary surrender of licenses. The Liaison Department can also collaborate with local governments and families based on the diagnostic reports to support the license surrender process. They will prepare necessary documents and provide guidance on the procedures to ensure that the license surrender process proceeds smoothly. The Liaison Department can also collaborate with local governments and families based on the diagnostic reports to provide support for life after license surrender. If assistance is needed for securing transportation or daily life after license surrender, the Liaison Department will collaborate with local governments and families to provide appropriate support. For example, they will provide guidance on how to use public transportation and taxi services, and introduce shopping assistance services. In this way, the Liaison Department can support the lives of elderly drivers after license surrender and provide an environment in which they can live with peace of mind. Furthermore, the Liaison Department can strengthen collaboration with local governments and families and provide continuous support. For example, by establishing regular follow-ups and consultation services, a system will be put in place where elderly drivers and their families can seek advice at any time. This will allow the liaison department to contribute not only to accident prevention for elderly drivers but also to supporting their lives after surrendering their licenses, providing comprehensive support.

[0071] The data collection unit can collect data on changes in reaction speed and cognitive ability while driving. For example, the data collection unit can collect data on brake reaction time while driving. The data collection unit can also collect data on the speed of steering wheel operation while driving. The data collection unit can also collect data on eye movement while driving. By collecting data on changes in reaction speed and cognitive ability while driving, the accuracy of evaluating driving ability is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input brake reaction time while driving into AI and have AI analyze changes in reaction time.

[0072] The analysis unit can analyze the collected data and evaluate the driving ability of elderly people. For example, the analysis unit can evaluate changes in reaction speed and cognitive ability based on the collected data. For example, the analysis unit can also evaluate changes in judgment and attention while driving based on the collected data. For example, the analysis unit can evaluate changes in eye movement and reaction time while driving based on the collected data. In this way, by analyzing the collected data, the driving ability of elderly people can be accurately evaluated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform the evaluation of driving ability.

[0073] The generation unit can generate daily diagnostic reports based on the analysis results. For example, the generation unit can generate a diagnostic report that includes an evaluation of driving ability based on the analysis results. The generation unit can also generate a diagnostic report that includes areas for improvement and recommendations for driving ability based on the analysis results. The generation unit can also generate a diagnostic report that allows for real-time monitoring of changes in driving ability based on the analysis results. This allows for real-time monitoring of changes in driving ability by generating daily diagnostic reports based on the analysis results. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into AI and have AI generate the diagnostic report.

[0074] The service provider can provide the generated diagnostic report. The service provider can, for example, provide the generated diagnostic report to the elderly person or their family. The service provider can also, for example, provide the generated diagnostic report to local governments or medical institutions. The service provider can also, for example, provide the generated diagnostic report through a web application or a mobile application. This allows the elderly person or their family to check their driving ability by providing the generated diagnostic report. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated diagnostic report into an AI and have the AI ​​perform the task of providing the report.

[0075] The liaison department can collaborate with local governments and families based on the diagnostic report to support the voluntary surrender of a driver's license. For example, the liaison department can collaborate with local governments and families based on the diagnostic report to support the license surrender process. For example, the liaison department can also collaborate with local governments and families based on the diagnostic report to provide support for life after license surrender. For example, the liaison department can collaborate with local governments and families based on the diagnostic report to provide guidance on the procedures and necessary documents for license surrender. In this way, by collaborating with local governments and families based on the diagnostic report, the liaison department can support the voluntary surrender of a driver's license and improve traffic safety. Some or all of the above processes in the liaison department may be performed using AI, for example, or not using AI. For example, the liaison department can input the diagnostic report into AI and have the AI ​​perform the collaboration with local governments and families.

[0076] The data collection unit can estimate the emotions of elderly individuals and adjust the timing of driving data collection based on the estimated emotions. For example, if an elderly individual is feeling stressed, the data collection unit can temporarily stop collecting driving data and resume it once they are relaxed. For example, if an elderly individual is relaxed, the data collection unit can collect driving data more frequently to obtain more detailed data. For example, if an elderly individual is tired, the data collection unit can refrain from collecting driving data and resume it after they have rested. This allows for the collection of more accurate data by adjusting the timing of driving data collection based on the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input elderly individuals' emotional data into an AI and have the AI ​​adjust the timing of driving data collection.

[0077] The data collection unit can analyze the elderly person's past driving history and select the optimal data collection method. For example, the data collection unit can collect data during the times when the elderly person frequently drove in the past. The data collection unit can also collect data at specific points based on the routes the elderly person has driven in the past. For example, the data collection unit can adjust the timing of data collection by considering the frequency and duration of driving based on the elderly person's past driving history. This allows for the selection of the optimal data collection method by analyzing the elderly person's past driving history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's past driving history data into AI and have the AI ​​select the optimal data collection method.

[0078] The data collection unit can filter driving data based on the elderly person's current health status and medication information. For example, if the elderly person is taking medication, the data collection unit will take the effects of the medication into consideration. For example, if the elderly person is in good health, the data collection unit can also collect detailed driving data. For example, if the elderly person is unwell, the data collection unit can refrain from collecting data and resume it after their health has recovered. This allows for the collection of more accurate driving data by filtering the data based on the elderly person's health status and medication information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's health status and medication information into the AI ​​and have the AI ​​perform the data filtering.

[0079] The data collection unit can estimate the emotions of elderly individuals and determine the priority of data to collect based on the estimated emotions. For example, if an elderly individual is tense, the data collection unit may prioritize collecting reaction speed and brake reaction time. If an elderly individual is relaxed, the data collection unit may also prioritize collecting steering speed and eye movement while driving. If an elderly individual is tired, the data collection unit may also prioritize collecting changes in attention and concentration while driving. This allows for the priority collection of important data by determining the priority of data to collect based on the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input elderly individuals' emotional data into an AI and have the AI ​​determine the priority of data to collect.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of elderly drivers when collecting driving data. For example, the data collection unit can collect data by considering the traffic conditions in the area where the elderly driver is driving. For example, the data collection unit can identify dangerous locations on the route the elderly driver is driving and prioritize the collection of data at those locations. For example, the data collection unit can collect data by considering the weather information in the area where the elderly driver is driving. This allows for the priority collection of highly relevant data by considering the geographical location information of elderly drivers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of elderly drivers into the AI ​​and have the AI ​​perform the priority collection of highly relevant data.

[0081] The data collection unit can analyze the social media activities of elderly drivers and collect relevant data when collecting driving data. For example, the data collection unit can collect data based on driving experiences shared by elderly drivers on social media. The data collection unit can also collect data considering, for example, problems drivers mention on social media. The data collection unit can also estimate driving emotions and stress levels from the elderly drivers' social media activities and collect relevant data. In this way, relevant data can be collected by analyzing the social media activities of elderly drivers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly drivers' social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0082] The analysis unit can estimate the emotions of elderly individuals and adjust the analysis criteria based on the estimated emotions. For example, if an elderly individual is tense, the analysis unit can tighten the analysis criteria for reaction speed and brake reaction time. For example, if an elderly individual is relaxed, the analysis unit can also relax the analysis criteria for steering speed and eye movement while driving. For example, if an elderly individual is tired, the analysis unit can also adjust the analysis criteria for changes in attention and concentration while driving. By adjusting the analysis criteria based on the emotions of elderly individuals, more accurate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input elderly individuals' emotion data into an AI and have the AI ​​perform the adjustment of the analysis criteria.

[0083] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of driving data during the analysis. For example, the analysis unit can analyze the interrelationship between brake reaction time and steering speed. For example, the analysis unit can also analyze the interrelationship between eye movement and reaction speed during driving. For example, the analysis unit can also analyze the interrelationship between changes in attention and concentration during driving. By considering the interrelationships of driving data, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships of driving data into the AI ​​and have the AI ​​perform the improvement of the analysis accuracy.

[0084] The analysis unit can perform analysis while considering the elderly person's driving history and health data. For example, the analysis unit can analyze changes in driving ability based on the elderly person's past driving history. The analysis unit can also analyze changes in reaction speed and cognitive ability while driving, for example, by considering the elderly person's health data. The analysis unit can also analyze the interrelationship between the elderly person's driving history and health data. This allows for more accurate analysis by considering the elderly person's driving history and health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the elderly person's driving history and health data into AI and have the AI ​​perform the analysis.

[0085] The analysis unit can estimate the emotions of elderly individuals and adjust the display method of the analysis results based on the estimated emotions. For example, if an elderly individual is tense, the analysis unit provides a simple and highly visible display method. For example, if an elderly individual is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if an elderly individual is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the emotions of elderly individuals, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the emotional data of elderly individuals into an AI and have the AI ​​adjust the display method of the analysis results.

[0086] The analysis unit can perform analysis while considering the geographical distribution of driving data. For example, the analysis unit can perform analysis while considering traffic conditions in areas where elderly people drive. For example, the analysis unit can identify dangerous locations on routes driven by elderly people and analyze data at those locations. For example, the analysis unit can perform analysis while considering weather information in areas where elderly people drive. This allows for more accurate analysis by considering the geographical distribution of driving data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of driving data into AI and have AI perform the analysis.

[0087] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on driving data during the analysis process. For example, the analysis unit can refer to the latest research papers related to the analysis of driving data. The analysis unit can also perform analysis based on past research results on the driving ability of elderly people. The analysis unit can also improve its analysis algorithm by referring to relevant literature on driving data. This improves the accuracy of the analysis by referring to relevant literature on driving data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature on driving data into AI and have AI perform the analysis to improve accuracy.

[0088] The generation unit can estimate the emotions of elderly individuals and adjust the presentation of the diagnostic report based on the estimated emotions. For example, if an elderly individual is tense, the generation unit can generate a simple and easy-to-read report. If an elderly individual is relaxed, the generation unit can also generate a report containing detailed information. If an elderly individual is in a hurry, the generation unit can also generate a concise report. By adjusting the presentation of the diagnostic report based on the emotions of the elderly individual, a more appropriate report can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the emotional data of elderly individuals into an AI and have the AI ​​adjust the presentation of the diagnostic report.

[0089] The generation unit can adjust the level of detail in a diagnostic report based on the importance of the driving data. For example, the generation unit can generate a detailed report based on important driving data. The generation unit can also generate a concise report based on less important driving data. The generation unit can also adjust the level of detail in the report according to the importance of the driving data. This allows the necessary information to be appropriately provided by adjusting the level of detail in the report based on the importance of the driving data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the driving data into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the report.

[0090] The generation unit can apply different generation algorithms depending on the category of driving data when generating a diagnostic report. For example, the generation unit can apply a specific algorithm to data related to reaction speed to generate a report. For example, the generation unit can also apply a different algorithm to data related to cognitive ability to generate a report. The generation unit can also select the optimal generation algorithm depending on the category of driving data. By applying the optimal generation algorithm according to the category of driving data, a highly accurate report can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the categories of driving data into the AI ​​and have the AI ​​execute the application of the generation algorithm.

[0091] The generation unit can estimate the emotions of elderly individuals and adjust the length of the diagnostic report based on the estimated emotions. For example, if an elderly individual is tense, the generation unit can generate a short, concise report. If an elderly individual is relaxed, the generation unit can also generate a longer report with detailed explanations. If an elderly individual is in a hurry, the generation unit can also generate a concise, easy-to-read report. This allows for the provision of more appropriate reports by adjusting the length of the diagnostic report based on the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the emotional data of elderly individuals into an AI and have the AI ​​adjust the length of the diagnostic report.

[0092] The generation unit can determine the priority of reports based on the timing of driving data collection when generating diagnostic reports. For example, the generation unit can prioritize generating reports based on the most recent driving data. The generation unit can also generate reports based on past driving data. The generation unit can also adjust the priority of reports according to the timing of driving data collection. This allows for the provision of the latest information by prioritizing reports based on the timing of driving data collection. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of driving data collection into the AI ​​and have the AI ​​determine the priority of reports.

[0093] The generation unit can adjust the order of reports based on the relevance of driving data when generating diagnostic reports. For example, the generation unit determines the order of reports based on important driving data. The generation unit can also, for example, prioritize the inclusion of highly relevant driving data in the reports. The generation unit can also adjust the order of reports according to the relevance of the driving data. This allows important information to be provided preferentially by adjusting the order of reports based on the relevance of the driving data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the driving data into the AI ​​and have the AI ​​perform the adjustment of the report order.

[0094] The service provider can estimate the emotions of elderly individuals and adjust the report delivery method based on the estimated emotions. For example, if an elderly individual is tense, the service provider may provide a report in a simple and easy-to-read manner. If an elderly individual is relaxed, the service provider may also provide a report in a more detailed manner. If an elderly individual is in a hurry, the service provider may also provide a report in a concise manner. By adjusting the report delivery method based on the emotions of elderly individuals, the service provider can deliver reports in a more appropriate manner. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input elderly individuals' emotion data into an AI and have the AI ​​adjust the report delivery method.

[0095] The service provider can select the optimal service delivery method by referring to the elderly person's past driving history when providing reports. For example, the service provider can prioritize the service delivery method that the elderly person has preferred to use in the past. For example, the service provider can also select the optimal delivery timing based on the elderly person's past driving history. For example, the service provider can adjust the frequency of report delivery based on the elderly person's past driving history. This allows the service provider to select the optimal service delivery method by referring to the elderly person's past driving history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the elderly person's past driving history data into AI and have the AI ​​select the optimal service delivery method.

[0096] The service provider can estimate the emotions of elderly individuals and adjust the frequency of report delivery based on the estimated emotions. For example, if an elderly individual is tense, the service provider can reduce the frequency of report delivery to alleviate stress. For example, if an elderly individual is relaxed, the service provider can increase the frequency of report delivery to provide more detailed information. For example, if an elderly individual is in a hurry, the service provider can deliver reports at the minimum necessary frequency. This allows for the provision of necessary information while reducing stress by adjusting the frequency of report delivery based on the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI or not using AI. For example, the service provider can input elderly individuals' emotion data into an AI and have the AI ​​adjust the frequency of report delivery.

[0097] The service provider can select the optimal delivery method when providing reports, taking into account the elderly person's device information. For example, if the elderly person is using a smartphone, the service provider can provide the report using a display method adapted to the screen size. If the elderly person is using a tablet, the service provider can also provide the report using a display method optimized for a larger screen. If the elderly person is using a personal computer, the service provider can also provide the report in a way that includes detailed information. This allows the service provider to provide reports in the most optimal way by taking into account the elderly person's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the elderly person's device information into AI and have the AI ​​select the optimal delivery method.

[0098] The collaboration unit can estimate the emotions of elderly individuals and adjust its collaboration methods with local governments and families based on the estimated emotions. For example, if an elderly individual is tense, the collaboration unit may collaborate in a simple and easily understandable manner. If an elderly individual is relaxed, the collaboration unit may collaborate in a manner that includes detailed information. If an elderly individual is in a hurry, the collaboration unit may collaborate in a manner that gets straight to the point. By adjusting the collaboration method based on the emotions of elderly individuals, more appropriate collaboration becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the collaboration unit may be performed using AI, or not using AI. For example, the collaboration unit can input elderly individuals' emotional data into an AI and have the AI ​​perform the adjustment of the collaboration method.

[0099] The collaboration unit can select the optimal collaboration method by referring to the elderly person's past driving history and health data during collaboration. For example, the collaboration unit can select the optimal collaboration timing based on the elderly person's past driving history. The collaboration unit can also select the optimal collaboration method by considering the elderly person's health data. The collaboration unit can also adjust the collaboration method based on the interrelationship between the elderly person's past driving history and health data. This allows the optimal collaboration method to be selected by referring to the elderly person's past driving history and health data. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the elderly person's past driving history and health data into AI and have the AI ​​select the optimal collaboration method.

[0100] The collaboration unit can estimate the emotions of elderly individuals and determine the priority of collaboration based on the estimated emotions. For example, if an elderly individual is tense, the collaboration unit will prioritize important collaboration items. If an elderly individual is relaxed, the collaboration unit may also prioritize detailed collaboration items. If an elderly individual is in a hurry, the collaboration unit may also prioritize concise collaboration items. This allows for prioritizing important collaboration items by determining the priority of collaboration based on the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input elderly individuals' emotion data into an AI and have the AI ​​determine the priority of collaboration.

[0101] The collaboration unit can select the optimal collaboration method by considering the geographical location information of the elderly person during the collaboration process. For example, the collaboration unit can collaborate with the local government in the area where the elderly person lives. The collaboration unit can also collaborate with family members who live near places that the elderly person frequently visits. For example, the collaboration unit can select the optimal collaboration method based on the geographical location information of the elderly person. This allows for the selection of the optimal collaboration method by considering the geographical location information of the elderly person. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the geographical location information of the elderly person into AI and have AI select the optimal collaboration method.

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

[0103] The elderly driver accident prevention system can collect and analyze not only driving data but also voice data while driving. For example, it can collect changes in the tone of voice and speaking style of elderly drivers to detect signs of stress and fatigue. The collection unit collects voice data while driving, and the analysis unit can analyze this data to help evaluate driving ability. The generation unit generates a diagnostic report that includes the results of the voice data analysis, and the provision unit provides this report to the elderly driver and their family. This allows for a more detailed understanding of the elderly driver's condition while driving by utilizing the voice data, which can help prevent accidents.

[0104] The elderly driver accident prevention system can collect and analyze in-vehicle environmental data in addition to driving data. For example, it can collect data on the temperature, humidity, and lighting conditions inside the vehicle to identify factors that affect driving ability. The collection unit collects in-vehicle environmental data, and the analysis unit can analyze this data to help evaluate driving ability. The generation unit generates a diagnostic report that includes the analysis results of the in-vehicle environmental data, and the provision unit provides this report to the elderly driver or their family. By utilizing in-vehicle environmental data, it becomes possible to understand the condition of elderly drivers in more detail and use this information to help prevent accidents.

[0105] The elderly driver accident prevention system can collect and analyze not only driving data but also biometric data during driving. For example, it can collect biometric data such as heart rate, blood pressure, and body temperature to identify factors that affect driving ability. The collection unit collects biometric data, and the analysis unit analyzes that data to help evaluate driving ability. The generation unit generates a diagnostic report including the results of the biometric data analysis, and the provision unit provides this report to the elderly driver or their family. In this way, by utilizing biometric data, the condition of elderly drivers can be understood in more detail, which helps in accident prevention.

[0106] The elderly driver accident prevention system can collect and analyze vehicle data in addition to driving data. For example, it can collect data such as vehicle speed, acceleration, and brake usage frequency to identify factors that affect driving ability. The collection unit collects vehicle data, and the analysis unit analyzes that data to help evaluate driving ability. The generation unit generates a diagnostic report that includes the results of the vehicle data analysis, and the provision unit provides this report to the elderly driver or their family. This allows for a more detailed understanding of the elderly driver's condition while driving by utilizing vehicle data, which helps in accident prevention.

[0107] The system for preventing accidents involving elderly drivers can collect and analyze not only driving data but also road condition data while driving. For example, it can collect data such as road congestion, weather, and road surface conditions to identify factors that affect driving ability. The collection unit collects road condition data, and the analysis unit analyzes this data to help evaluate driving ability. The generation unit generates a diagnostic report that includes the analysis results of the road condition data, and the provision unit provides this report to the elderly driver or their family. This allows for a more detailed understanding of the elderly driver's condition while driving by utilizing road condition data, which helps in accident prevention.

[0108] The elderly driver accident prevention system can collect driving data, estimate the emotions of elderly drivers while they are driving, and provide driving assistance based on those estimated emotions. For example, if an elderly driver is feeling anxious, the system can provide advice to help them relax. The collection unit collects emotional data from elderly drivers, and the analysis unit analyzes that data to estimate their emotions. The provision unit then provides appropriate driving assistance based on the estimated emotions. By providing driving assistance based on the emotions of elderly drivers, this system can reduce stress while driving and help prevent accidents.

[0109] The elderly driver accident prevention system can, in addition to collecting driving data, estimate the emotions of elderly drivers while they are driving and adjust the analysis method of the driving data based on the estimated emotions. For example, if the elderly driver is relaxed, a detailed data analysis is performed, while if they are tense, a simpler analysis is performed. The data collection unit collects emotional data from the elderly driver, and the analysis unit analyzes this data to estimate their emotions. The analysis unit then adjusts the analysis method based on the estimated emotions. This allows for a more appropriate evaluation of driving ability by adjusting the analysis method based on the emotions of the elderly driver.

[0110] The elderly driver accident prevention system can, in addition to collecting driving data, estimate the emotions of elderly drivers while they are driving and adjust the content of the diagnostic report based on those estimated emotions. For example, if an elderly person is tense, it can generate a simple and easy-to-read report; if they are relaxed, it can generate a detailed report. The collection unit collects emotional data from elderly drivers, and the analysis unit analyzes that data to estimate their emotions. The generation unit adjusts the content of the diagnostic report based on the estimated emotions. This allows for the provision of more appropriate information by adjusting the content of the diagnostic report based on the emotions of elderly drivers.

[0111] The elderly driver accident prevention system can, in addition to collecting driving data, estimate the emotions of elderly drivers while they are driving and adjust the frequency of data collection based on these estimated emotions. For example, if an elderly person is tense, the frequency of data collection is reduced, and if they are relaxed, the frequency is increased. The collection unit collects emotional data from elderly drivers, and the analysis unit analyzes this data to estimate their emotions. The collection unit then adjusts the frequency of data collection based on the estimated emotions. This allows for more accurate data collection by adjusting the frequency of data collection based on the emotions of elderly drivers.

[0112] The elderly driver accident prevention system can, in addition to collecting driving data, estimate the emotions of elderly drivers while they are driving and adjust the method of providing analysis results based on those estimated emotions. For example, if an elderly person is tense, the analysis results can be provided in a simple and easy-to-understand manner, while if they are relaxed, they can be provided in a way that includes detailed information. The collection unit collects emotional data from elderly drivers, and the analysis unit analyzes that data to estimate their emotions. The provision unit adjusts the method of providing analysis results based on the estimated emotions. This allows for the provision of more appropriate information by adjusting the method of providing analysis results based on the emotions of elderly drivers.

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

[0114] Step 1: The data collection unit collects driving data from elderly drivers. For example, it collects data on changes in reaction speed and cognitive ability while driving, braking reaction time and steering speed, eye movement and attention changes, etc. Step 2: The analysis unit analyzes the data collected by the data collection unit to evaluate the driving ability of elderly drivers. For example, based on the collected data, it evaluates changes in reaction speed and cognitive ability, changes in judgment and attention while driving, and changes in eye movement and reaction time. Step 3: The generation unit generates a diagnostic report based on the evaluation results obtained by the analysis unit. For example, it generates a diagnostic report that includes daily diagnostic reports, driving performance evaluation results, areas for improvement, and recommendations based on the analysis results. Step 4: The supply unit provides the diagnostic report generated by the generation unit. For example, the generated diagnostic report can be provided to the elderly, their families, local governments, or medical institutions, and can also be provided through web applications or mobile applications. Step 5: The liaison department collaborates with local governments and families based on the diagnostic report provided by the service provider. For example, based on the diagnostic report, they collaborate with local governments and families to support voluntary surrender of the driver's license, assist with the license surrender process, and provide support for life after the license is surrendered.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects driving data using the camera 42 and microphone 38B of the smart device 14 and processes the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate driving ability. The generation unit is implemented in the specific processing unit 290 of the data processing device 12 and generates a diagnostic report based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides the generated diagnostic report to the elderly person or their family. The collaboration unit is implemented in the specific processing unit 290 of the data processing device 12 and collaborates with local governments and families based on the diagnostic report to support voluntary surrender of the driver's license. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and collaboration unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects driving data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to evaluate driving ability. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a diagnostic report based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides the generated diagnostic report to the elderly person or their family. The collaboration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and collaborates with local governments and families based on the diagnostic report to support voluntary surrender of the driver's license. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

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

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

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects driving data using the camera 42 and microphone 238 of the headset terminal 314 and processes the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate driving ability. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a diagnostic report based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides the generated diagnostic report to the elderly person or their family. The collaboration unit is implemented in the specific processing unit 290 of the data processing unit 12 and collaborates with local governments and families based on the diagnostic report to support voluntary surrender of the driver's license. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

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

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and coordination unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects driving data using the camera 42 and microphone 238 of the robot 414 and processes the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to evaluate driving ability. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a diagnostic report based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the generated diagnostic report to the elderly person or their family. The coordination unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and coordinates with local governments and families based on the diagnostic report to support the voluntary surrender of the license. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

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

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

[0177] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0186] (Note 1) A data collection unit that collects driving data from elderly drivers, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the driving performance, A generation unit that generates a diagnostic report based on the evaluation results obtained by the analysis unit, A providing unit that provides the diagnostic report generated by the generation unit, The system includes a liaison unit that collaborates with local governments and families based on the diagnostic report provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on changes in reaction speed and cognitive ability while driving. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data will be analyzed to evaluate the driving abilities of elderly drivers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate daily diagnostic reports based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides the generated diagnostic report. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, Based on the diagnostic report, we will work with local governments and families to support voluntary surrender of driver's licenses. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the emotions of elderly drivers and adjusts the timing of driving data collection based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the past driving history of elderly drivers 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 When collecting driving data, filtering is performed based on the current health status and medication information of elderly drivers. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate the emotions of older adults and prioritize 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 When collecting driving data, the collection of highly relevant data will be prioritized, taking into account the geographical location information of elderly drivers. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting driving data, analyze the social media activity of elderly drivers and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the emotions of older adults and adjust the analysis criteria based on the estimated emotions of older adults. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by considering the interrelationships of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the driving history and health data of elderly drivers will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the emotions of elderly individuals and adjusts the display method of the analysis results based on the estimated emotions of the elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the geographical distribution of the driving data will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature on driving data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the emotions of elderly individuals and adjusts the presentation of diagnostic reports based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating diagnostic reports, adjust the level of detail in the report based on the importance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating diagnostic reports, different generation algorithms are applied depending on the category of driving data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is The system estimates the emotions of older adults and adjusts the length of the diagnostic report based on the estimated emotions of the older adults. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating diagnostic reports, the report prioritization is determined based on when the driving data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating diagnostic reports, the order of reports is adjusted based on the relevance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate the emotions of older adults and adjust the way reports are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing reports, the optimal method of delivery is selected by referring to the elderly person's past driving history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the emotions of older adults and adjusts the frequency of report delivery based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing reports, the optimal method of delivery will be selected, taking into account the device information of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, The system estimates the emotions of elderly individuals and adjusts the methods of collaboration with local governments and families based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned linkage unit is, During the integration process, the system will select the optimal integration method by referring to the elderly person's past driving history and health data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned linkage unit is, The system estimates the emotions of elderly individuals and determines the priority of collaboration based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, When collaborating, the optimal collaboration method will be selected considering the geographical location information of the elderly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A data collection unit that collects driving data from elderly drivers, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the driving performance, A generation unit that generates a diagnostic report based on the evaluation results obtained by the analysis unit, A providing unit that provides the diagnostic report generated by the generation unit, The system includes a liaison unit that collaborates with local governments and families based on the diagnostic report provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on changes in reaction speed and cognitive ability while driving. The system according to feature 1.

3. The aforementioned analysis unit, The collected data will be analyzed to evaluate the driving abilities of elderly drivers. The system according to feature 1.

4. The generating unit is Generate daily diagnostic reports based on the analysis results. The system according to feature 1.

5. The aforementioned supply unit is, Provides the generated diagnostic report. The system according to feature 1.

6. The aforementioned linkage unit is, Based on the diagnostic report, we will work with local governments and families to support voluntary surrender of driver's licenses. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the emotions of elderly drivers and adjusts the timing of driving data collection based on these estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the past driving history of elderly drivers and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting driving data, filtering is performed based on the current health status and medication information of elderly drivers. The system according to feature 1.

10. The aforementioned collection unit is We estimate the emotions of older adults and prioritize the data to collect based on those estimated emotions. The system according to feature 1.

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