system

Abstract

The system according to this embodiment aims to understand the driving conditions of senior drivers and provide appropriate advice. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a sharing unit. The collection unit collects driving data of senior drivers. The analysis unit analyzes the driving data collected by the collection unit. The generation unit generates a driving report based on the analysis results obtained by the analysis unit. The sharing unit shares the driving report generated by the generation unit with family members and care managers.

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 prior art, there is a problem that it is difficult to grasp the driving situation of a senior driver and provide appropriate advice.

[0005] The system according to the embodiment aims to grasp the driving situation of a senior driver and provide appropriate advice.

Means for Solving the Problems

[44] ]

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a sharing unit. The collection unit collects driving data of senior drivers. The analysis unit analyzes the driving data collected by the collection unit. The generation unit generates a driving report based on the analysis results obtained by the analysis unit. The sharing unit shares the driving report generated by the generation unit with family members and care managers. [Effects of the Invention]

[0007] The system according to this embodiment can understand the driving conditions of senior drivers and provide appropriate advice. [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 applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The safe driving agent system according to an embodiment of the present invention is a system that analyzes the driving data of senior drivers and generates a driving report that includes an evaluation of the driving, areas for improvement, and points to note. This system analyzes the driving data of senior drivers and generates a driving report that includes an evaluation of the driving, areas for improvement, and points to note. This report is shared with family members and care managers to understand the senior driver's driving situation and provide specific advice for continuing to drive safely. For example, the system collects the senior driver's driving data in real time. For example, it collects detailed data such as vehicle speed, frequency of brake use, and steering operation. For example, it includes the number of times sudden braking and sudden steering. This allows the system to understand the senior driver's driving habits. Next, the system uses AI to analyze the collected data. Based on the collected data, the AI ​​evaluates the driving style and identifies areas for improvement and points to note. For example, if there are many instances of sudden braking, the AI ​​generates feedback advising the driver to pay attention to their use of the brakes. This allows senior drivers to understand their own driving habits and make improvements if necessary. The generated driving report is automatically shared with family members and care managers. This allows family members and care managers to understand the senior driver's driving situation and provide necessary support. For example, if a senior driver feels anxious about driving, specific advice such as support for driving practice can be provided. This is expected to reduce the accident rate among senior drivers. Senior drivers can objectively assess their own driving skills and make improvements as needed. In addition, family members and care managers can monitor the senior driver's driving situation with peace of mind. This is expected to increase the senior driver's confidence and the family's sense of security. As a result, the safe driving agent system can analyze the senior driver's driving data, generate a driving report that includes driving evaluation, areas for improvement, and points to note, and share it with family members and care managers.

[0029] The safe driving agent system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a sharing unit. The collection unit collects driving data from senior drivers. The collection unit collects driving data such as vehicle speed, frequency of brake use, steering operation status, number of sudden brakings, and number of sudden steering maneuvers. The collection unit, for example, monitors the vehicle speed in real time and collects data. The collection unit can also record the frequency of brake use and count the number of sudden brakings. Furthermore, the collection unit can monitor the steering operation status and record the number of sudden steering maneuvers. For example, the collection unit collects speed data using a vehicle speed sensor. The frequency of brake use can be recorded using a brake pedal sensor. The steering operation status can be monitored using a steering wheel angle sensor. The analysis unit analyzes the driving data collected by the collection unit. The analysis unit, for example, evaluates the driving style based on the collected data and identifies areas for improvement and points to pay attention to. For example, if there are many sudden brakings, the analysis unit generates feedback to advise the driver to pay attention to how they use the brakes. Furthermore, the analysis unit can identify areas for improvement in steering if there are many instances of sudden steering. In addition, the analysis unit can comprehensively evaluate driving data and perform an overall evaluation of the driving style. For example, the analysis unit uses AI to analyze driving data and evaluate the driving style. The AI ​​evaluates the driving style based on the collected data and identifies areas for improvement and points to pay attention to. The generation unit generates a driving report based on the analysis results obtained by the analysis unit. The generation unit generates a driving report that includes, for example, an evaluation of the driving, areas for improvement, and points to pay attention to. The generation unit summarizes the evaluation results of the driving style in a report and specifically describes areas for improvement and points to pay attention to. The generation unit can also automatically generate driving reports and share them with family members and care managers. For example, the generation unit uses AI to generate driving reports and reflects the evaluation results and areas for improvement in the report. The sharing unit shares the driving reports generated by the generation unit with family members and care managers. The sharing unit shares the generated driving reports with family members and care managers, for example, via email or a dedicated app.The sharing unit can, for example, send driving reports via email to notify family members or care managers. It can also share driving reports using a dedicated app, allowing family members and care managers to monitor driving conditions in real time. For example, the sharing unit can automatically generate driving reports and share them with family members or care managers via email or a dedicated app. This enables the safe driving agent system according to the embodiment to collect, analyze, generate reports on, and share driving data from senior drivers.

[0030] The data collection unit collects driving data from senior drivers. For example, it collects driving data such as vehicle speed, brake usage frequency, steering operation status, number of sudden braking incidents, and number of sudden steering incidents. Specifically, it monitors and collects speed data in real time using a vehicle speed sensor. The speed sensor accurately measures the vehicle's speed and transmits this data to a central database. Brake usage frequency can be recorded using a sensor attached to the brake pedal. This sensor detects the number of times the brake pedal is pressed and the intensity of the press, and counts the number of sudden braking incidents. Steering operation status can be monitored using a steering wheel angle sensor. The angle sensor measures the steering wheel's rotation angle and rotation speed, and records the number of sudden steering incidents. This data is stored in a data logger within the vehicle and periodically transmitted to a central database. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the data collection frequency and accuracy, flexible responses to specific situations and conditions are possible. This allows the data collection unit to efficiently and effectively collect data, improving the overall system performance. Furthermore, the data collection unit can monitor the vehicle's driving conditions in real time and issue immediate warnings if any abnormalities are detected. For example, if the number of sudden braking or sudden steering maneuvers exceeds a certain threshold, it can display a warning to the driver to encourage safe driving. This enables the data collection unit to collect detailed driving data from senior drivers and provide support for safe driving.

[0031] The analysis unit analyzes the driving data collected by the data collection unit. For example, the analysis unit evaluates the driving style based on the collected data and identifies areas for improvement and points to pay attention to. Specifically, if there are many instances of sudden braking, it generates feedback advising the driver to pay attention to their brake usage. Similarly, if there are many instances of sudden steering, it can identify areas for improvement in steering. The analysis unit uses AI to analyze driving data and evaluate the driving style. The AI ​​evaluates the driving style based on the collected data and identifies areas for improvement and points to pay attention to. For example, the AI ​​analyzes the vehicle's speed data and analyzes the speed fluctuation patterns to evaluate the frequency of sudden acceleration and deceleration. It also analyzes the frequency of brake use and the number of sudden braking instances to identify areas for improvement regarding brake usage. Furthermore, it analyzes the steering situation and evaluates the number of sudden steering instances and the variation in steering wheel rotation angle to identify areas for improvement in steering. Based on these analysis results, the analysis unit can perform an overall evaluation of the driving style. For example, it scores the driving style evaluation results and provides the driver with a comprehensive evaluation. The analysis unit can also clarify the characteristics of senior drivers' driving styles by comparing them with past driving data and data from other drivers. This allows the analysis unit to quickly and accurately analyze the collected data, evaluate the driving style of senior drivers, and identify areas for improvement and points of caution. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The generation unit generates a driving report based on the analysis results obtained by the analysis unit. The generation unit generates a driving report that includes, for example, an evaluation of driving, areas for improvement, and points to note. Specifically, it summarizes the evaluation results of driving style in the report and describes areas for improvement and points to note in detail. The generation unit uses AI to generate the driving report and reflects the evaluation results and areas for improvement in the report. For example, the AI ​​automatically summarizes the evaluation results of driving style in the report based on the data provided by the analysis unit. The report includes detailed data such as an overall evaluation score of driving style, the number of sudden braking and sudden steering incidents, and speed fluctuation patterns. It also describes areas for improvement and points to note in detail, clearly indicating how the driver should improve their driving. The generation unit can also automatically generate driving reports and share them with family members and care managers. For example, the generation unit generates the driving report in PDF format and sends it to family members and care managers via email or a dedicated app. The generation unit can also save the driving report to the cloud so that family members and care managers can access it at any time. This allows the generation unit to thoroughly evaluate the driving style of senior drivers, generate a driving report that includes specific areas for improvement and points to note, and share it with family members and care managers. Furthermore, the generation unit can continuously update the content of the driving report, providing evaluation results based on the latest driving data. In this way, the generation unit can support the improvement of senior drivers' driving styles and contribute to promoting safe driving.

[0033] The sharing unit shares the driving reports generated by the generation unit with family members and care managers. For example, the sharing unit can share the generated driving reports with family members and care managers via email or a dedicated app. Specifically, it can generate driving reports in PDF format and send them via email. The email includes an overview of the driving report and important points, making it easy for family members and care managers to understand the content. The sharing unit can also share driving reports using a dedicated app, allowing family members and care managers to monitor driving status in real time. The app displays detailed driving reports, allowing family members and care managers to review the evaluation results and areas for improvement in driving style. Furthermore, the sharing unit can save the driving report content to the cloud, allowing family members and care managers to access it at any time. This enables the sharing unit to share the senior driver's driving status with family members and care managers, allowing for real-time monitoring. The sharing unit can also collect feedback from family members and care managers to continuously improve the content of the driving reports and the system's functionality. For example, based on feedback from family members and care managers, the format and content of the driving reports can be reviewed to provide clearer and more practical reports. This allows the shared system to share the senior driver's driving status with family and care managers, enabling them to provide support for safe driving. Furthermore, the shared system can reliably transmit information using multiple communication methods. For example, in addition to email, it can reliably deliver important information by using voice calls, SMS, and the notification function of a dedicated app. This allows the shared system to quickly and reliably share driving status with family and care managers, providing support for safe driving.

[0034] The data collection unit can collect driving data such as vehicle speed, brake usage frequency, steering operation status, number of sudden brakings, and number of sudden steering maneuvers. For example, the data collection unit can monitor the vehicle speed in real time and collect data. For example, the data collection unit can collect speed data using the vehicle's speed sensor. The data collection unit can also record brake usage frequency and count the number of sudden brakings. For example, the data collection unit can record brake usage frequency using a brake pedal sensor. Furthermore, the data collection unit can monitor steering operation status and record the number of sudden steering maneuvers. For example, the data collection unit can monitor steering operation status using a steering wheel angle sensor. This allows for the collection of detailed driving data, enabling an understanding of driving habits. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from the vehicle's speed sensor into a generating AI and have the generating AI perform analysis of the speed data.

[0035] The analysis unit can evaluate driving style based on collected driving data and identify areas for improvement and points to pay attention to. For example, if the number of sudden braking incidents is high, the analysis unit can generate feedback advising the driver to pay attention to their brake usage. For example, if the number of sudden braking incidents is high, the analysis unit can count the number of sudden braking incidents and generate feedback on brake usage based on the results. The analysis unit can also identify areas for improvement in steering if there are many sudden steering incidents. For example, if the analysis unit records the number of sudden steering incidents and identifies areas for improvement in steering based on the results. Furthermore, the analysis unit can comprehensively evaluate driving data and perform an overall evaluation of the driving style. For example, if the analysis unit performs an overall evaluation of the driving style based on collected data and identifies areas for improvement and points to pay attention to. This allows for driving improvement by evaluating driving style and identifying areas for improvement and points to pay attention to. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the collected driving data into the generating AI, which can then perform an evaluation of the driving style and identify areas for improvement.

[0036] The generation unit can generate a driving report that includes an evaluation of driving, areas for improvement, and points to note. For example, the generation unit can generate a driving report that includes an evaluation of driving, areas for improvement, and points to note. For example, the generation unit can summarize the results of the driving style evaluation in a report and specifically describe areas for improvement and points to note. For example, the generation unit can automatically generate a driving report based on the results of the driving style evaluation. The generation unit can also automatically generate a driving report and share it with family members and care managers. For example, the generation unit can automatically generate a driving report and share it with family members and care managers via email or a dedicated app. This makes it easier to understand the driving situation by generating a driving report. 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 results of the driving style evaluation into a generation AI and have the generation AI execute the generation of the driving report.

[0037] The sharing unit can share the generated driving report with family members and care managers via email or a dedicated app. For example, the sharing unit can send the generated driving report via email to notify family members and care managers. For example, the sharing unit can send the driving report via email so that family members and care managers can understand the driving situation. The sharing unit can also share the driving report using a dedicated app so that family members and care managers can understand the driving situation in real time. For example, the sharing unit can share the driving report via a dedicated app so that family members and care managers can understand the driving situation in real time. This allows family members and care managers to understand the driving situation by sharing the driving report. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the generated driving report into a generating AI and have the generating AI execute the method for sharing the report.

[0038] The shared unit can provide specific advice regarding driving. The shared unit can, for example, provide specific advice regarding driving. The shared unit can, for example, provide specific instructions on areas for improvement in driving style. The shared unit can, for example, provide specific advice on areas for improvement in driving style, and senior drivers can receive instructions on how to improve their driving. This allows for driving improvement by providing specific advice. Some or all of the above processing in the shared unit may be performed using AI, for example, or without AI. For example, the shared unit can input specific advice on areas for improvement in driving style into a generating AI and have the generating AI execute a method for providing the advice.

[0039] The data collection unit can analyze the senior driver's past driving data and select the optimal data collection method. For example, the data collection unit can identify time periods with frequent sudden braking from past driving data and focus data collection during those times. For example, the data collection unit can enhance data collection under specific road conditions based on past driving data. For example, the data collection unit can analyze past driving data and select a data collection method that suits the senior driver's driving style. This allows for the selection of the optimal data collection method by analyzing past driving data. 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 past driving data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter driving data based on the senior driver's current health condition and mood. For example, if the senior driver is in good health, the data collection unit will perform normal data collection. For example, if the senior driver is unwell, the data collection unit can temporarily suspend data collection. For example, if the senior driver is feeling unwell, the data collection unit can refrain from collecting data and prioritize driving safety. This allows for appropriate data collection by filtering based on health condition and mood. 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 senior driver's health condition and mood data into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of senior drivers when collecting driving data. For example, when a senior driver passes through a particular intersection, the data collection unit can prioritize the collection of data at that point. For example, when a senior driver is driving on a particular road, the data collection unit can prioritize the collection of data on that road. For example, when a senior driver is driving in a particular region, the data collection unit can prioritize the collection of data in that region. In this way, by considering geographical location information, highly relevant data can be prioritized. 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 senior drivers into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze the social media activities of senior drivers and collect relevant data when collecting driving data. For example, if a senior driver makes a post about driving on social media, the data collection unit can use the content of that post as a reference. For example, if a senior driver receives feedback about driving on social media, the data collection unit can use the content of that feedback as a reference. For example, if a senior driver receives advice about driving on social media, the data collection unit can use the content of that advice as a reference. In this way, relevant data can be collected by analyzing social media activities. 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 senior driver's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] 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 relationship between the frequency of brake use and speed to identify the cause of sudden braking. For example, the analysis unit can analyze the relationship between steering operation and speed to identify the cause of sudden steering. For example, the analysis unit can analyze the relationship between the number of sudden brakes and the number of sudden steerings to identify areas for improvement in driving style. By considering these interrelationships, 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 a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0044] The analysis unit can perform analysis while considering the attribute information of senior drivers. For example, the analysis unit can evaluate the driving style while considering the age of senior drivers. For example, the analysis unit can evaluate the driving style while considering the gender of senior drivers. For example, the analysis unit can evaluate the driving style while considering the driving experience of senior drivers. This makes it possible to perform appropriate analysis by considering attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the attribute information of senior drivers into a generating AI and have the generating AI perform the analysis.

[0045] The analysis unit can perform analysis while considering the geographical distribution of driving data. For example, the analysis unit can focus its analysis on driving data in a specific area. For example, the analysis unit can focus its analysis on driving data on a specific road. For example, the analysis unit can focus its analysis on driving data at a specific intersection. This allows for appropriate analysis by considering the geographical distribution. 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 geographical distribution of driving data into a generating AI and have the generating AI perform the analysis.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on driving data during the analysis. For example, the analysis unit can perform analysis by referring to the latest research on driving data. For example, the analysis unit can perform analysis by referring to past research on driving data. For example, the analysis unit can perform analysis by referring to relevant literature on driving data. This improves the accuracy of the analysis by referring to relevant literature. 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 relevant literature on driving data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0047] The generation unit can adjust the level of detail in the report based on the importance of the driving data when generating the driving report. For example, the generation unit can generate a detailed report based on important driving data. For example, the generation unit can generate a concise report based on general driving data. For example, the generation unit can generate a focused report based on specific driving data. By adjusting the level of detail based on importance, an appropriate report is 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 importance of the driving data into the generation AI and have the generation AI perform the adjustment of the level of detail in the report.

[0048] The generation unit can apply different generation algorithms depending on the category of driving data when generating a driving report. For example, the generation unit can apply a specific algorithm to data regarding the frequency of brake use. For example, the generation unit can apply a different algorithm to data regarding steering operation. For example, the generation unit can apply yet another algorithm to data regarding the number of sudden braking incidents. By applying the optimal generation algorithm according to the category, an appropriate report is 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 a generation AI and have the generation AI execute the application of the generation algorithm.

[0049] The generation unit can determine the priority of reports based on the submission timing of the driving data when generating driving reports. For example, the generation unit can prioritize reports based on the latest driving data. For example, the generation unit can generate reports with normal priority based on past driving data. For example, the generation unit can adjust the priority based on driving data collected over a specific period. This ensures that appropriate reports are generated by determining priority based on submission timing. 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 submission timing of the driving data into the generation AI and have the generation AI determine the priority of the reports.

[0050] The generation unit can adjust the order of reports based on the relevance of the driving data when generating driving reports. For example, the generation unit can generate a report that displays important driving data first. For example, the generation unit can generate a report that displays general driving data later. For example, the generation unit can generate a report with the order adjusted based on specific driving data. This ensures that an appropriate report is generated by adjusting the order based on relevance. 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 a generation AI and have the generation AI perform the adjustment of the report order.

[0051] The sharing unit can select the optimal sharing method by referring to past feedback from family members and care managers when sharing information. For example, if a family member previously requested sharing via email, the sharing unit will share the report via email. For example, if a care manager previously requested sharing via a dedicated app, the sharing unit can share the report via a dedicated app. The sharing unit can analyze feedback from family members and care managers to select the optimal sharing method. This allows the optimal sharing method to be selected by referring to past feedback. Some or all of the above-described processes in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input past feedback from family members and care managers into a generating AI and have the generating AI select the optimal sharing method.

[0052] The sharing unit can determine the sharing priority based on the importance of the driving reports when sharing them. For example, the sharing unit may prioritize the sharing of important driving reports. For example, the sharing unit may share general driving reports with normal priority. For example, the sharing unit may adjust the priority based on a specific driving report. This enables appropriate sharing by determining priority based on importance. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit may input the importance of the driving reports into a generating AI and have the generating AI perform the determination of the sharing priority.

[0053] The sharing function can select the optimal sharing method by considering the geographical location information of family members and care managers when sharing information. For example, if family members are far away, the sharing function can share the report via email. For example, if care managers are nearby, the sharing function can share the report via a dedicated app. The sharing function can analyze the geographical location information of family members and care managers to select the optimal sharing method. This allows for the selection of the optimal sharing method by considering geographical location information. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can input the geographical location information of family members and care managers into a generating AI and have the generating AI select the optimal sharing method.

[0054] The sharing unit can improve the accuracy of sharing by referring to relevant literature on driving reports during the sharing process. For example, the sharing unit can share by referring to the latest research on driving reports. For example, the sharing unit can share by referring to past research on driving reports. For example, the sharing unit can share by referring to relevant literature on driving reports. This improves the accuracy of sharing by referring to relevant literature. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input relevant literature on driving reports into a generating AI and have the generating AI perform the improvement of sharing accuracy.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The safe driving agent system can consider a senior driver's past driving history when analyzing their driving data. For example, it can identify specific driving patterns and tendencies based on past driving data and evaluate changes in driving style by comparing them with current driving data. Furthermore, it can identify areas for improvement and points to pay attention to in specific driving situations based on past driving history and provide specific advice. This allows senior drivers to understand changes in their driving style and make improvements as needed. In addition, family members and care managers can understand changes in the senior driver's driving style and provide appropriate support.

[0057] The data collection unit can collect external environmental data of the vehicle when collecting driving data from senior drivers. For example, it can collect data such as weather information, road conditions, and traffic volume, and analyze them in combination with driving data. This makes it possible to evaluate how senior drivers' driving styles are influenced by the external environment. For example, by analyzing driving data in rainy weather, it is possible to evaluate how senior drivers use the brakes. Also, by analyzing driving data in heavy traffic, it is possible to evaluate the steering behavior of senior drivers. In this way, by considering external environmental data, a more accurate evaluation of driving styles becomes possible.

[0058] The analysis unit can consider the driver's health data when analyzing senior drivers' driving data. For example, it can collect health data such as the driver's heart rate, blood pressure, and stress level, and analyze it in combination with driving data. This allows for an assessment of how the senior driver's health condition affects their driving style. For instance, by analyzing driving data when the heart rate is high, it's possible to evaluate how the senior driver uses the brakes. Similarly, by analyzing driving data when the stress level is high, it's possible to evaluate the senior driver's steering behavior. By considering health data, a more accurate assessment of driving style becomes possible.

[0059] The generation unit can consider the driving goals of senior drivers when generating driving reports. For example, it can generate driving reports based on driving goals set by senior drivers (e.g., reducing the number of sudden brakes, maintaining a constant speed, etc.). This allows senior drivers to understand their progress towards their driving goals and make improvements as needed. For example, if the goal is to reduce the number of sudden brakes, the generation unit can analyze data related to the number of sudden brakes intensively and include specific areas for improvement in the report. Also, if the goal is to maintain a constant speed, the generation unit can analyze speed data and provide feedback on speed fluctuations. By considering driving goals, it is expected that senior drivers' driving skills will improve.

[0060] The sharing function can consider feedback from family members and care managers when sharing generated driving reports. For example, it can adjust the content and format of driving reports based on feedback received in the past. This allows for reports that are easier for family members and care managers to understand. For instance, if a family member requests detailed information, the sharing function can include detailed data in the driving report. Similarly, if a care manager requests concise information, the sharing function can summarize the driving report concisely. Furthermore, the method of sharing the driving report (e.g., email, dedicated app) can be selected based on feedback from family members and care managers. This allows for more effective information sharing by considering feedback.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects driving data from senior drivers. For example, it collects driving data such as vehicle speed, frequency of brake use, steering wheel operation, number of sudden braking incidents, and number of sudden steering incidents. The data collection unit collects this data using a vehicle speed sensor, brake pedal sensor, and steering wheel angle sensor. Step 2: The analysis unit analyzes the driving data collected by the data collection unit. For example, it evaluates the driving style based on the collected data and identifies areas for improvement and points to pay attention to. If there are many instances of sudden braking, it generates feedback to advise the driver to pay attention to how they use the brakes, and if there are many instances of sudden steering, it identifies areas for improvement in steering. The analysis unit uses AI to analyze the driving data and evaluate the driving style. Step 3: The generation unit generates a driving report based on the analysis results obtained by the analysis unit. For example, it generates a driving report that includes driving evaluation, areas for improvement, and points to note, summarizing the driving style evaluation results in the report and specifically describing areas for improvement and points to note. The generation unit uses AI to generate the driving report and reflects the evaluation results and areas for improvement in the report. Step 4: The sharing unit shares the driving report generated by the generation unit with family members and care managers. For example, the generated driving report is shared with family members and care managers via email or a dedicated app, allowing them to understand the driving status in real time.

[0063] (Example of form 2) The safe driving agent system according to an embodiment of the present invention is a system that analyzes the driving data of senior drivers and generates a driving report that includes an evaluation of the driving, areas for improvement, and points to note. This system analyzes the driving data of senior drivers and generates a driving report that includes an evaluation of the driving, areas for improvement, and points to note. This report is shared with family members and care managers to understand the senior driver's driving situation and provide specific advice for continuing to drive safely. For example, the system collects the senior driver's driving data in real time. For example, it collects detailed data such as vehicle speed, frequency of brake use, and steering operation. For example, it includes the number of times sudden braking and sudden steering. This allows the system to understand the senior driver's driving habits. Next, the system uses AI to analyze the collected data. Based on the collected data, the AI ​​evaluates the driving style and identifies areas for improvement and points to note. For example, if there are many instances of sudden braking, the AI ​​generates feedback advising the driver to pay attention to their use of the brakes. This allows senior drivers to understand their own driving habits and make improvements if necessary. The generated driving report is automatically shared with family members and care managers. This allows family members and care managers to understand the senior driver's driving situation and provide necessary support. For example, if a senior driver feels anxious about driving, specific advice such as support for driving practice can be provided. This is expected to reduce the accident rate among senior drivers. Senior drivers can objectively assess their own driving skills and make improvements as needed. In addition, family members and care managers can monitor the senior driver's driving situation with peace of mind. This is expected to increase the senior driver's confidence and the family's sense of security. As a result, the safe driving agent system can analyze the senior driver's driving data, generate a driving report that includes driving evaluation, areas for improvement, and points to note, and share it with family members and care managers.

[0064] The safe driving agent system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a sharing unit. The collection unit collects driving data from senior drivers. The collection unit collects driving data such as vehicle speed, frequency of brake use, steering operation status, number of sudden brakings, and number of sudden steering maneuvers. The collection unit, for example, monitors the vehicle speed in real time and collects data. The collection unit can also record the frequency of brake use and count the number of sudden brakings. Furthermore, the collection unit can monitor the steering operation status and record the number of sudden steering maneuvers. For example, the collection unit collects speed data using a vehicle speed sensor. The frequency of brake use can be recorded using a brake pedal sensor. The steering operation status can be monitored using a steering wheel angle sensor. The analysis unit analyzes the driving data collected by the collection unit. The analysis unit, for example, evaluates the driving style based on the collected data and identifies areas for improvement and points to pay attention to. For example, if there are many sudden brakings, the analysis unit generates feedback to advise the driver to pay attention to how they use the brakes. Furthermore, the analysis unit can identify areas for improvement in steering if there are many instances of sudden steering. In addition, the analysis unit can comprehensively evaluate driving data and perform an overall evaluation of the driving style. For example, the analysis unit uses AI to analyze driving data and evaluate the driving style. The AI ​​evaluates the driving style based on the collected data and identifies areas for improvement and points to pay attention to. The generation unit generates a driving report based on the analysis results obtained by the analysis unit. The generation unit generates a driving report that includes, for example, an evaluation of the driving, areas for improvement, and points to pay attention to. The generation unit summarizes the evaluation results of the driving style in a report and specifically describes areas for improvement and points to pay attention to. The generation unit can also automatically generate driving reports and share them with family members and care managers. For example, the generation unit uses AI to generate driving reports and reflects the evaluation results and areas for improvement in the report. The sharing unit shares the driving reports generated by the generation unit with family members and care managers. The sharing unit shares the generated driving reports with family members and care managers, for example, via email or a dedicated app.The sharing unit can, for example, send driving reports via email to notify family members or care managers. It can also share driving reports using a dedicated app, allowing family members and care managers to monitor driving conditions in real time. For example, the sharing unit can automatically generate driving reports and share them with family members or care managers via email or a dedicated app. This enables the safe driving agent system according to the embodiment to collect, analyze, generate reports on, and share driving data from senior drivers.

[0065] The data collection unit collects driving data from senior drivers. For example, it collects driving data such as vehicle speed, brake usage frequency, steering operation status, number of sudden braking incidents, and number of sudden steering incidents. Specifically, it monitors and collects speed data in real time using a vehicle speed sensor. The speed sensor accurately measures the vehicle's speed and transmits this data to a central database. Brake usage frequency can be recorded using a sensor attached to the brake pedal. This sensor detects the number of times the brake pedal is pressed and the intensity of the press, and counts the number of sudden braking incidents. Steering operation status can be monitored using a steering wheel angle sensor. The angle sensor measures the steering wheel's rotation angle and rotation speed, and records the number of sudden steering incidents. This data is stored in a data logger within the vehicle and periodically transmitted to a central database. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the data collection frequency and accuracy, flexible responses to specific situations and conditions are possible. This allows the data collection unit to efficiently and effectively collect data, improving the overall system performance. Furthermore, the data collection unit can monitor the vehicle's driving conditions in real time and issue immediate warnings if any abnormalities are detected. For example, if the number of sudden braking or sudden steering maneuvers exceeds a certain threshold, it can display a warning to the driver to encourage safe driving. This enables the data collection unit to collect detailed driving data from senior drivers and provide support for safe driving.

[0066] The analysis unit analyzes the driving data collected by the data collection unit. For example, the analysis unit evaluates the driving style based on the collected data and identifies areas for improvement and points to pay attention to. Specifically, if there are many instances of sudden braking, it generates feedback advising the driver to pay attention to their brake usage. Similarly, if there are many instances of sudden steering, it can identify areas for improvement in steering. The analysis unit uses AI to analyze driving data and evaluate the driving style. The AI ​​evaluates the driving style based on the collected data and identifies areas for improvement and points to pay attention to. For example, the AI ​​analyzes the vehicle's speed data and analyzes the speed fluctuation patterns to evaluate the frequency of sudden acceleration and deceleration. It also analyzes the frequency of brake use and the number of sudden braking instances to identify areas for improvement regarding brake usage. Furthermore, it analyzes the steering situation and evaluates the number of sudden steering instances and the variation in steering wheel rotation angle to identify areas for improvement in steering. Based on these analysis results, the analysis unit can perform an overall evaluation of the driving style. For example, it scores the driving style evaluation results and provides the driver with a comprehensive evaluation. The analysis unit can also clarify the characteristics of senior drivers' driving styles by comparing them with past driving data and data from other drivers. This allows the analysis unit to quickly and accurately analyze the collected data, evaluate the driving style of senior drivers, and identify areas for improvement and points of caution. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0067] The generation unit generates a driving report based on the analysis results obtained by the analysis unit. The generation unit generates a driving report that includes, for example, an evaluation of driving, areas for improvement, and points to note. Specifically, it summarizes the evaluation results of driving style in the report and describes areas for improvement and points to note in detail. The generation unit uses AI to generate the driving report and reflects the evaluation results and areas for improvement in the report. For example, the AI ​​automatically summarizes the evaluation results of driving style in the report based on the data provided by the analysis unit. The report includes detailed data such as an overall evaluation score of driving style, the number of sudden braking and sudden steering incidents, and speed fluctuation patterns. It also describes areas for improvement and points to note in detail, clearly indicating how the driver should improve their driving. The generation unit can also automatically generate driving reports and share them with family members and care managers. For example, the generation unit generates the driving report in PDF format and sends it to family members and care managers via email or a dedicated app. The generation unit can also save the driving report to the cloud so that family members and care managers can access it at any time. This allows the generation unit to thoroughly evaluate the driving style of senior drivers, generate a driving report that includes specific areas for improvement and points to note, and share it with family members and care managers. Furthermore, the generation unit can continuously update the content of the driving report, providing evaluation results based on the latest driving data. In this way, the generation unit can support the improvement of senior drivers' driving styles and contribute to promoting safe driving.

[0068] The sharing unit shares the driving reports generated by the generation unit with family members and care managers. For example, the sharing unit can share the generated driving reports with family members and care managers via email or a dedicated app. Specifically, it can generate driving reports in PDF format and send them via email. The email includes an overview of the driving report and important points, making it easy for family members and care managers to understand the content. The sharing unit can also share driving reports using a dedicated app, allowing family members and care managers to monitor driving status in real time. The app displays detailed driving reports, allowing family members and care managers to review the evaluation results and areas for improvement in driving style. Furthermore, the sharing unit can save the driving report content to the cloud, allowing family members and care managers to access it at any time. This enables the sharing unit to share the senior driver's driving status with family members and care managers, allowing for real-time monitoring. The sharing unit can also collect feedback from family members and care managers to continuously improve the content of the driving reports and the system's functionality. For example, based on feedback from family members and care managers, the format and content of the driving reports can be reviewed to provide clearer and more practical reports. This allows the shared system to share the senior driver's driving status with family and care managers, enabling them to provide support for safe driving. Furthermore, the shared system can reliably transmit information using multiple communication methods. For example, in addition to email, it can reliably deliver important information by using voice calls, SMS, and the notification function of a dedicated app. This allows the shared system to quickly and reliably share driving status with family and care managers, providing support for safe driving.

[0069] The data collection unit can collect driving data such as vehicle speed, brake usage frequency, steering operation status, number of sudden brakings, and number of sudden steering maneuvers. For example, the data collection unit can monitor the vehicle speed in real time and collect data. For example, the data collection unit can collect speed data using the vehicle's speed sensor. The data collection unit can also record brake usage frequency and count the number of sudden brakings. For example, the data collection unit can record brake usage frequency using a brake pedal sensor. Furthermore, the data collection unit can monitor steering operation status and record the number of sudden steering maneuvers. For example, the data collection unit can monitor steering operation status using a steering wheel angle sensor. This allows for the collection of detailed driving data, enabling an understanding of driving habits. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from the vehicle's speed sensor into a generating AI and have the generating AI perform analysis of the speed data.

[0070] The analysis unit can evaluate driving style based on collected driving data and identify areas for improvement and points to pay attention to. For example, if the number of sudden braking incidents is high, the analysis unit can generate feedback advising the driver to pay attention to their brake usage. For example, if the number of sudden braking incidents is high, the analysis unit can count the number of sudden braking incidents and generate feedback on brake usage based on the results. The analysis unit can also identify areas for improvement in steering if there are many sudden steering incidents. For example, if the analysis unit records the number of sudden steering incidents and identifies areas for improvement in steering based on the results. Furthermore, the analysis unit can comprehensively evaluate driving data and perform an overall evaluation of the driving style. For example, if the analysis unit performs an overall evaluation of the driving style based on collected data and identifies areas for improvement and points to pay attention to. This allows for driving improvement by evaluating driving style and identifying areas for improvement and points to pay attention to. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the collected driving data into the generating AI, which can then perform an evaluation of the driving style and identify areas for improvement.

[0071] The generation unit can generate a driving report that includes an evaluation of driving, areas for improvement, and points to note. For example, the generation unit can generate a driving report that includes an evaluation of driving, areas for improvement, and points to note. For example, the generation unit can summarize the results of the driving style evaluation in a report and specifically describe areas for improvement and points to note. For example, the generation unit can automatically generate a driving report based on the results of the driving style evaluation. The generation unit can also automatically generate a driving report and share it with family members and care managers. For example, the generation unit can automatically generate a driving report and share it with family members and care managers via email or a dedicated app. This makes it easier to understand the driving situation by generating a driving report. 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 results of the driving style evaluation into a generation AI and have the generation AI execute the generation of the driving report.

[0072] The sharing unit can share the generated driving report with family members and care managers via email or a dedicated app. For example, the sharing unit can send the generated driving report via email to notify family members and care managers. For example, the sharing unit can send the driving report via email so that family members and care managers can understand the driving situation. The sharing unit can also share the driving report using a dedicated app so that family members and care managers can understand the driving situation in real time. For example, the sharing unit can share the driving report via a dedicated app so that family members and care managers can understand the driving situation in real time. This allows family members and care managers to understand the driving situation by sharing the driving report. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the generated driving report into a generating AI and have the generating AI execute the method for sharing the report.

[0073] The shared unit can provide specific advice regarding driving. The shared unit can, for example, provide specific advice regarding driving. The shared unit can, for example, provide specific instructions on areas for improvement in driving style. The shared unit can, for example, provide specific advice on areas for improvement in driving style, and senior drivers can receive instructions on how to improve their driving. This allows for driving improvement by providing specific advice. Some or all of the above processing in the shared unit may be performed using AI, for example, or without AI. For example, the shared unit can input specific advice on areas for improvement in driving style into a generating AI and have the generating AI execute a method for providing the advice.

[0074] The data collection unit can estimate the emotions of senior drivers and adjust the timing of driving data collection based on the estimated emotions. For example, if a senior driver is tense, the data collection unit can pause data collection until the driver relaxes. For example, if a senior driver is relaxed, the data collection unit can perform normal data collection. For example, if a senior driver is tired, the data collection unit can perform data collection frequently and record changes in driving in detail. This allows for appropriate data collection by adjusting the collection timing based on emotions. 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 using AI. For example, the data collection unit can input senior driver emotion data into a generative AI and have the generative AI adjust the collection timing.

[0075] The data collection unit can analyze the senior driver's past driving data and select the optimal data collection method. For example, the data collection unit can identify time periods with frequent sudden braking from past driving data and focus data collection during those times. For example, the data collection unit can enhance data collection under specific road conditions based on past driving data. For example, the data collection unit can analyze past driving data and select a data collection method that suits the senior driver's driving style. This allows for the selection of the optimal data collection method by analyzing past driving data. 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 past driving data into a generating AI and have the generating AI select the optimal data collection method.

[0076] The data collection unit can filter driving data based on the senior driver's current health condition and mood. For example, if the senior driver is in good health, the data collection unit will perform normal data collection. For example, if the senior driver is unwell, the data collection unit can temporarily suspend data collection. For example, if the senior driver is feeling unwell, the data collection unit can refrain from collecting data and prioritize driving safety. This allows for appropriate data collection by filtering based on health condition and mood. 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 senior driver's health condition and mood data into a generating AI and have the generating AI perform the filtering.

[0077] The data collection unit can estimate the emotions of senior drivers and determine the priority of driving data to collect based on the estimated emotions. For example, if a senior driver is tense, the data collection unit may prioritize collecting data on the frequency of brake use. If a senior driver is relaxed, the data collection unit may prioritize collecting data on steering wheel operation. If a senior driver is tired, the data collection unit may prioritize collecting data on the number of sudden braking incidents. This allows for the priority collection of important data by determining priorities based on emotions. 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 senior driver emotion data into a generative AI and have the generative AI determine the priority of the data to be collected.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of senior drivers when collecting driving data. For example, when a senior driver passes through a particular intersection, the data collection unit can prioritize the collection of data at that point. For example, when a senior driver is driving on a particular road, the data collection unit can prioritize the collection of data on that road. For example, when a senior driver is driving in a particular region, the data collection unit can prioritize the collection of data in that region. In this way, by considering geographical location information, highly relevant data can be prioritized. 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 senior drivers into a generating AI and have the generating AI perform the collection of highly relevant data.

[0079] The data collection unit can analyze the social media activities of senior drivers and collect relevant data when collecting driving data. For example, if a senior driver makes a post about driving on social media, the data collection unit can use the content of that post as a reference. For example, if a senior driver receives feedback about driving on social media, the data collection unit can use the content of that feedback as a reference. For example, if a senior driver receives advice about driving on social media, the data collection unit can use the content of that advice as a reference. In this way, relevant data can be collected by analyzing social media activities. 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 senior driver's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0080] The analysis unit can estimate the emotions of senior drivers and adjust the driving style evaluation criteria based on the estimated emotions. For example, if a senior driver is tense, the analysis unit can relax the evaluation criteria to reduce stress. For example, if a senior driver is relaxed, the analysis unit can apply the normal evaluation criteria. For example, if a senior driver is tired, the analysis unit can tighten the evaluation criteria to draw attention to the driver. This allows for appropriate evaluation by adjusting the evaluation criteria based on emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input senior driver emotion data into the generative AI and have the generative AI perform the adjustment of the evaluation criteria.

[0081] 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 relationship between the frequency of brake use and speed to identify the cause of sudden braking. For example, the analysis unit can analyze the relationship between steering operation and speed to identify the cause of sudden steering. For example, the analysis unit can analyze the relationship between the number of sudden brakes and the number of sudden steerings to identify areas for improvement in driving style. By considering these interrelationships, 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 a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0082] The analysis unit can perform analysis while considering the attribute information of senior drivers. For example, the analysis unit can evaluate the driving style while considering the age of senior drivers. For example, the analysis unit can evaluate the driving style while considering the gender of senior drivers. For example, the analysis unit can evaluate the driving style while considering the driving experience of senior drivers. This makes it possible to perform appropriate analysis by considering attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the attribute information of senior drivers into a generating AI and have the generating AI perform the analysis.

[0083] The analysis unit can estimate the emotions of senior drivers and adjust the display order of the analysis results based on the estimated emotions. For example, if a senior driver is tense, the analysis unit can display important information first. For example, if a senior driver is relaxed, the analysis unit can display detailed information first. For example, if a senior driver is tired, the analysis unit can display concise information first. In this way, important information can be displayed preferentially by adjusting the display order based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input senior driver emotion data into a generative AI and have the generative AI perform the adjustment of the display order.

[0084] The analysis unit can perform analysis while considering the geographical distribution of driving data. For example, the analysis unit can focus its analysis on driving data in a specific area. For example, the analysis unit can focus its analysis on driving data on a specific road. For example, the analysis unit can focus its analysis on driving data at a specific intersection. This allows for appropriate analysis by considering the geographical distribution. 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 geographical distribution of driving data into a generating AI and have the generating AI perform the analysis.

[0085] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on driving data during the analysis. For example, the analysis unit can perform analysis by referring to the latest research on driving data. For example, the analysis unit can perform analysis by referring to past research on driving data. For example, the analysis unit can perform analysis by referring to relevant literature on driving data. This improves the accuracy of the analysis by referring to relevant literature. 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 relevant literature on driving data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0086] The generation unit can estimate the emotions of senior drivers and adjust the presentation of the driving report based on the estimated emotions. For example, if a senior driver is tense, the generation unit can use a concise and easy-to-understand presentation. For example, if a senior driver is relaxed, the generation unit can use a presentation that includes detailed information. For example, if a senior driver is tired, the generation unit can use a presentation that highlights important information. By adjusting the presentation based on emotions, an appropriate report is generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 processing in the generation unit may be performed using AI or not. For example, the generation unit can input senior driver emotion data into the generation AI and have the generation AI perform the adjustment of the presentation.

[0087] The generation unit can adjust the level of detail in the report based on the importance of the driving data when generating the driving report. For example, the generation unit can generate a detailed report based on important driving data. For example, the generation unit can generate a concise report based on general driving data. For example, the generation unit can generate a focused report based on specific driving data. By adjusting the level of detail based on importance, an appropriate report is 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 importance of the driving data into the generation AI and have the generation AI perform the adjustment of the level of detail in the report.

[0088] The generation unit can apply different generation algorithms depending on the category of driving data when generating a driving report. For example, the generation unit can apply a specific algorithm to data regarding the frequency of brake use. For example, the generation unit can apply a different algorithm to data regarding steering operation. For example, the generation unit can apply yet another algorithm to data regarding the number of sudden braking incidents. By applying the optimal generation algorithm according to the category, an appropriate report is 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 a generation AI and have the generation AI execute the application of the generation algorithm.

[0089] The generation unit can estimate the senior driver's emotions and adjust the length of the driving report based on the estimated emotions. For example, if the senior driver is tense, the generation unit can generate a short, concise report. For example, if the senior driver is relaxed, the generation unit can generate a longer report with detailed explanations. For example, if the senior driver is tired, the generation unit can generate a concise report that highlights important information. By adjusting the report length based on emotions, an appropriate report is generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 processing in the generation unit may be performed using AI or not. For example, the generation unit can input the senior driver's emotion data into the generation AI and have the generation AI adjust the report length.

[0090] The generation unit can determine the priority of reports based on the submission timing of the driving data when generating driving reports. For example, the generation unit can prioritize reports based on the latest driving data. For example, the generation unit can generate reports with normal priority based on past driving data. For example, the generation unit can adjust the priority based on driving data collected over a specific period. This ensures that appropriate reports are generated by determining priority based on submission timing. 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 submission timing of the driving data into the generation AI and have the generation AI determine the priority of the reports.

[0091] The generation unit can adjust the order of reports based on the relevance of the driving data when generating driving reports. For example, the generation unit can generate a report that displays important driving data first. For example, the generation unit can generate a report that displays general driving data later. For example, the generation unit can generate a report with the order adjusted based on specific driving data. This ensures that an appropriate report is generated by adjusting the order based on relevance. 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 a generation AI and have the generation AI perform the adjustment of the report order.

[0092] The sharing unit can estimate the senior driver's emotions and adjust how the driving report is shared based on the estimated emotions. For example, if the senior driver is stressed, the sharing unit can share a concise report via email. If the senior driver is relaxed, the sharing unit can share a detailed report via a dedicated app. If the senior driver is tired, the sharing unit can share a report highlighting important information. This allows for appropriate sharing by adjusting the sharing method based on emotions. 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 sharing unit may be performed using AI or not. For example, the sharing unit can input the senior driver's emotion data into the generative AI and have the generative AI adjust the sharing method.

[0093] The sharing unit can select the optimal sharing method by referring to past feedback from family members and care managers when sharing information. For example, if a family member previously requested sharing via email, the sharing unit will share the report via email. For example, if a care manager previously requested sharing via a dedicated app, the sharing unit can share the report via a dedicated app. The sharing unit can analyze feedback from family members and care managers to select the optimal sharing method. This allows the optimal sharing method to be selected by referring to past feedback. Some or all of the above-described processes in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input past feedback from family members and care managers into a generating AI and have the generating AI select the optimal sharing method.

[0094] The sharing unit can determine the sharing priority based on the importance of the driving reports when sharing them. For example, the sharing unit may prioritize the sharing of important driving reports. For example, the sharing unit may share general driving reports with normal priority. For example, the sharing unit may adjust the priority based on a specific driving report. This enables appropriate sharing by determining priority based on importance. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit may input the importance of the driving reports into a generating AI and have the generating AI perform the determination of the sharing priority.

[0095] The sharing unit can estimate the senior driver's emotions and adjust the frequency of sharing driving reports based on the estimated emotions. For example, if the senior driver is tense, the sharing unit can reduce the sharing frequency. For example, if the senior driver is relaxed, the sharing unit can maintain the normal sharing frequency. For example, if the senior driver is tired, the sharing unit can increase the sharing frequency and record changes in driving in detail. This allows for appropriate sharing by adjusting the sharing frequency based on emotions. 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 processing in the sharing unit may be performed using AI or not using AI. For example, the sharing unit can input the senior driver's emotion data into the generative AI and have the generative AI adjust the sharing frequency.

[0096] The sharing function can select the optimal sharing method by considering the geographical location information of family members and care managers when sharing information. For example, if family members are far away, the sharing function can share the report via email. For example, if care managers are nearby, the sharing function can share the report via a dedicated app. The sharing function can analyze the geographical location information of family members and care managers to select the optimal sharing method. This allows for the selection of the optimal sharing method by considering geographical location information. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can input the geographical location information of family members and care managers into a generating AI and have the generating AI select the optimal sharing method.

[0097] The sharing unit can improve the accuracy of sharing by referring to relevant literature on driving reports during the sharing process. For example, the sharing unit can share by referring to the latest research on driving reports. For example, the sharing unit can share by referring to past research on driving reports. For example, the sharing unit can share by referring to relevant literature on driving reports. This improves the accuracy of sharing by referring to relevant literature. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input relevant literature on driving reports into a generating AI and have the generating AI perform the improvement of sharing accuracy.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The safe driving agent system can consider a senior driver's past driving history when analyzing their driving data. For example, it can identify specific driving patterns and tendencies based on past driving data and evaluate changes in driving style by comparing them with current driving data. Furthermore, it can identify areas for improvement and points to pay attention to in specific driving situations based on past driving history and provide specific advice. This allows senior drivers to understand changes in their driving style and make improvements as needed. In addition, family members and care managers can understand changes in the senior driver's driving style and provide appropriate support.

[0100] The data collection unit can collect external environmental data of the vehicle when collecting driving data from senior drivers. For example, it can collect data such as weather information, road conditions, and traffic volume, and analyze them in combination with driving data. This makes it possible to evaluate how senior drivers' driving styles are influenced by the external environment. For example, by analyzing driving data in rainy weather, it is possible to evaluate how senior drivers use the brakes. Also, by analyzing driving data in heavy traffic, it is possible to evaluate the steering behavior of senior drivers. In this way, by considering external environmental data, a more accurate evaluation of driving styles becomes possible.

[0101] The analysis unit can consider the driver's health data when analyzing senior drivers' driving data. For example, it can collect health data such as the driver's heart rate, blood pressure, and stress level, and analyze it in combination with driving data. This allows for an assessment of how the senior driver's health condition affects their driving style. For instance, by analyzing driving data when the heart rate is high, it's possible to evaluate how the senior driver uses the brakes. Similarly, by analyzing driving data when the stress level is high, it's possible to evaluate the senior driver's steering behavior. By considering health data, a more accurate assessment of driving style becomes possible.

[0102] The generation unit can consider the driving goals of senior drivers when generating driving reports. For example, it can generate driving reports based on driving goals set by senior drivers (e.g., reducing the number of sudden brakes, maintaining a constant speed, etc.). This allows senior drivers to understand their progress towards their driving goals and make improvements as needed. For example, if the goal is to reduce the number of sudden brakes, the generation unit can analyze data related to the number of sudden brakes intensively and include specific areas for improvement in the report. Also, if the goal is to maintain a constant speed, the generation unit can analyze speed data and provide feedback on speed fluctuations. By considering driving goals, it is expected that senior drivers' driving skills will improve.

[0103] The sharing function can consider feedback from family members and care managers when sharing generated driving reports. For example, it can adjust the content and format of driving reports based on feedback received in the past. This allows for reports that are easier for family members and care managers to understand. For instance, if a family member requests detailed information, the sharing function can include detailed data in the driving report. Similarly, if a care manager requests concise information, the sharing function can summarize the driving report concisely. Furthermore, the method of sharing the driving report (e.g., email, dedicated app) can be selected based on feedback from family members and care managers. This allows for more effective information sharing by considering feedback.

[0104] The data collection unit can estimate the emotions of senior drivers and adjust the method of collecting driving data based on the estimated emotions. For example, if a senior driver is tense, data collection can be kept to a minimum, prioritizing driving safety. If a senior driver is relaxed, the data collection unit can perform normal data collection. If a senior driver is tired, the data collection unit can perform data collection more frequently, recording changes in driving in detail. This allows for appropriate data collection by adjusting the collection method based on emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input senior driver emotion data into a generative AI and have the generative AI adjust the collection method.

[0105] The analysis unit can estimate the emotions of senior drivers and adjust the driving style evaluation criteria based on the estimated emotions. For example, if a senior driver is tense, the evaluation criteria can be relaxed to reduce stress. If a senior driver is relaxed, the analysis unit can apply the normal evaluation criteria. If a senior driver is tired, the analysis unit can tighten the evaluation criteria to draw attention to the driver. This allows for appropriate evaluation by adjusting the evaluation criteria based on emotions. 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 analysis unit may be performed using AI or not. For example, the analysis unit can input senior driver emotion data into a generative AI and have the generative AI adjust the evaluation criteria.

[0106] The generation unit can estimate the senior driver's emotions and adjust the way the driving report is presented based on the estimated emotions. For example, if the senior driver is tense, a concise and easy-to-understand presentation is used. If the senior driver is relaxed, the generation unit can use a presentation that includes detailed information. If the senior driver is tired, the generation unit can use a presentation that highlights important information. By adjusting the presentation based on emotions, an appropriate report is generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 processing in the generation unit may be performed using AI or not. For example, the generation unit can input the senior driver's emotion data into the generation AI and have the generation AI perform the adjustment of the presentation.

[0107] The sharing unit can estimate the senior driver's emotions and adjust how driving reports are shared based on the estimated emotions. For example, if the senior driver is stressed, a concise report can be shared via email. If the senior driver is relaxed, the sharing unit can share a detailed report via a dedicated app. If the senior driver is tired, the sharing unit can share a report highlighting important information. This allows for appropriate sharing by adjusting the sharing method based on emotions. 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 sharing unit may be performed using AI or not. For example, the sharing unit can input the senior driver's emotion data into a generative AI and have the generative AI adjust the sharing method.

[0108] The data collection unit can estimate the emotions of senior drivers and determine the priority of driving data to collect based on the estimated emotions. For example, if a senior driver is tense, the frequency of brake use may be prioritized for collection. If a senior driver is relaxed, the data collection unit may prioritize the collection of steering wheel operation data. If a senior driver is tired, the data collection unit may prioritize the collection of the number of sudden brakes. This allows for the priority collection of important data by determining priorities based on emotions. 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 senior driver emotion data into a generative AI and have the generative AI determine the priority of the data to be collected.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects driving data from senior drivers. For example, it collects driving data such as vehicle speed, frequency of brake use, steering wheel operation, number of sudden braking incidents, and number of sudden steering incidents. The data collection unit collects this data using a vehicle speed sensor, brake pedal sensor, and steering wheel angle sensor. Step 2: The analysis unit analyzes the driving data collected by the data collection unit. For example, it evaluates the driving style based on the collected data and identifies areas for improvement and points to pay attention to. If there are many instances of sudden braking, it generates feedback to advise the driver to pay attention to how they use the brakes, and if there are many instances of sudden steering, it identifies areas for improvement in steering. The analysis unit uses AI to analyze the driving data and evaluate the driving style. Step 3: The generation unit generates a driving report based on the analysis results obtained by the analysis unit. For example, it generates a driving report that includes driving evaluation, areas for improvement, and points to note, summarizing the driving style evaluation results in the report and specifically describing areas for improvement and points to note. The generation unit uses AI to generate the driving report and reflects the evaluation results and areas for improvement in the report. Step 4: The sharing unit shares the driving report generated by the generation unit with family members and care managers. For example, the generated driving report is shared with family members and care managers via email or a dedicated app, allowing them to understand the driving status in real time.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and sharing unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects driving data such as vehicle speed, brake usage frequency, and steering operation status using the sensors of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and evaluates the driving style. The generation unit generates a driving report using the specific processing unit 290 of the data processing unit 12, and the sharing unit allows the driving report to be shared with family members or care managers using the communication function of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements, including the collection unit, analysis unit, generation unit, and sharing unit described above, is implemented in, for example, at least one of the smart glasses 2214 and the data processing unit 12. For example, the collection unit uses the sensors of the smart glasses 2214 to collect driving data such as vehicle speed, brake usage frequency, and steering wheel operation status. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to evaluate the driving style. The generation unit generates a driving report using the identification processing unit 290 of the data processing unit 12, and the sharing unit allows the driving report to be shared with family members or care managers using the communication function of the smart glasses 2214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements, including the collection unit, analysis unit, generation unit, and sharing unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the sensors of the headset terminal 314 to collect driving data such as vehicle speed, brake usage frequency, and steering wheel operation. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to evaluate the driving style. The generation unit generates a driving report using the specific processing unit 290 of the data processing unit 12, and the sharing unit allows the driving report to be shared with family members or care managers using the communication function of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements, including the collection unit, analysis unit, generation unit, and sharing unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects driving data such as vehicle speed, brake usage frequency, and steering operation status using the sensors of the robot 414. For example, the analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and evaluates the driving style. For example, the generation unit generates a driving report by the specific processing unit 290 of the data processing unit 12, and the sharing unit allows the driving report to be shared with family members or care managers using the communication function of the robot 414. 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A data collection unit that collects driving data from senior drivers, An analysis unit analyzes the operating data collected by the aforementioned collection unit, A generation unit generates an operation report based on the analysis results obtained by the analysis unit, The system includes a sharing unit that shares the operation report generated by the generation unit with family members and care managers. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects driving data such as vehicle speed, frequency of brake use, steering behavior, number of sudden braking incidents, and number of sudden steering incidents. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected driving data, we evaluate driving style and identify areas for improvement and points to pay attention to. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate a driving report that includes driving evaluation, areas for improvement, and points to note. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned shared portion is, The generated driving report is shared with family and care managers via email or a dedicated app. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned shared portion is, Provide specific advice regarding driving. 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 senior drivers and adjusts the timing of driving data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We will analyze the past driving data of senior 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 senior driver's current health status and mood. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the emotions of senior drivers and prioritizes the driving 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 system prioritizes collecting highly relevant data by considering the geographical location information of senior drivers. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting driving data, we analyze the social media activity of senior drivers and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the emotions of senior drivers and adjusts the driving style evaluation criteria based on those estimated emotions. 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, attribute information of senior 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 senior drivers and adjusts the display order of the analysis results based on the estimated emotions. 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 senior drivers and adjusts the way driving reports are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating driving 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 driving 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 It estimates the emotions of senior drivers and adjusts the length of the driving report based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating driving reports, the report priority is determined based on when the driving data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating driving 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 shared portion is, It estimates the emotions of senior drivers and adjusts how driving reports are shared based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned shared portion is, When sharing information, refer to past feedback from family members and care managers to select the most suitable sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned shared portion is, When sharing, the sharing priority is determined based on the importance of the driving report. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned shared portion is, It estimates the emotions of senior drivers and adjusts the frequency of sharing driving reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned shared portion is, When sharing information, the optimal sharing method is selected considering the geographical location of family members and care managers. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned shared portion is, When sharing, refer to relevant literature in the driving report to improve the accuracy of the sharing process. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 senior drivers, An analysis unit analyzes the operating data collected by the aforementioned collection unit, A generation unit generates an operation report based on the analysis results obtained by the analysis unit, The system includes a sharing unit that shares the operation report generated by the generation unit with family members and care managers. A system characterized by the following features.

2. The aforementioned collection unit is The system collects driving data such as vehicle speed, frequency of brake use, steering behavior, number of sudden braking incidents, and number of sudden steering incidents. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected driving data, we evaluate driving style and identify areas for improvement and points to pay attention to. The system according to feature 1.

4. The generating unit is Generate a driving report that includes driving evaluation, areas for improvement, and points to note. The system according to feature 1.

5. The aforementioned shared portion is, The generated driving report is shared with family and care managers via email or a dedicated app. The system according to feature 1.

6. The aforementioned shared portion is, Provide specific advice regarding driving. The system according to feature 1.

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

8. The aforementioned collection unit is We will analyze the past driving data of senior drivers and select the optimal data collection method. The system according to feature 1.

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