system

Abstract

The system according to this embodiment aims to appropriately understand the driving conditions of senior drivers and share this information with their families and care managers. [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. The analysis unit analyzes the driving data collected by the collection unit. The generation unit generates a driving report based on the data analyzed 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, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the driving situation of senior drivers has not been adequately grasped and shared with family members and care managers, and there is room for improvement.

[0005] The system according to the embodiment aims to appropriately grasp the driving situation of senior drivers and share it with family members and care managers.

Means for Solving the Problems

[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. The analysis unit analyzes the driving data collected by the collection unit. The generation unit generates a driving report based on the data analyzed 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 appropriately understand the driving conditions of senior drivers and share this information with their families and care managers. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 driving analysis system according to an embodiment of the present invention is a system in which an AI analyzes driving data after a senior driver has driven and generates a driving report. The driving analysis system collects driving data from sensors and cameras mounted on the vehicle after the senior driver has finished driving. This data includes speed, frequency of brake use, steering operation, and distance between vehicles. Next, the collected driving data is transmitted to the AI, which analyzes it. Based on the driving data, the AI ​​evaluates the day's driving and identifies areas for improvement and points to pay attention to. For example, if there is a high frequency of sudden braking, the AI ​​generates advice such as, "You're braking suddenly a lot, so try to brake earlier." The generated driving report is not only sent to the senior driver's smartphone or computer, but is also shared with family members and care managers. This allows family members and care managers to understand the senior driver's driving situation in real time. For example, family members can provide specific advice such as, "Dad, it seems you braked suddenly a lot today, so be more careful next time." Through this mechanism, senior drivers can objectively evaluate their driving skills and learn specific areas for improvement. In addition, family members and care managers can monitor the senior driver's driving situation with peace of mind by understanding their driving situation. For example, family members can provide positive feedback such as, "Mom, your driving was really good today, keep it up!" Furthermore, the AI ​​can accumulate driving data and analyze long-term driving patterns. This allows for improvements in senior drivers' driving skills and the early detection of potential risks. For example, the AI ​​can provide long-term advice such as, "You've been driving more at night recently, so be careful of your declining eyesight." In this way, by analyzing senior drivers' driving and generating and sharing driving reports, the AI ​​can support safe driving for senior drivers and provide peace of mind to their families and care managers. Thus, the driving analysis system can support safe driving and provide peace of mind to families and care managers by analyzing senior drivers' driving data and generating and sharing driving reports.

[0029] The driving analysis system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a sharing unit. The data collection unit collects driving data. The data collection unit collects driving data from, for example, sensors and cameras mounted on the vehicle. The data collection unit can collect data such as speed, frequency of brake use, steering operation, and distance between vehicles. The data collection unit can collect vehicle acceleration data using, for example, an acceleration sensor. The data collection unit can also collect vehicle rotation data using a gyro sensor. Furthermore, the data collection unit can also collect video data of the vehicle's surroundings using a camera. The analysis unit analyzes the driving data collected by the data collection unit. The analysis unit evaluates the day's driving based on the collected driving data. The analysis unit can identify areas for improvement and points to pay attention to based on the driving data. For example, if there is a high frequency of sudden braking, the analysis unit can identify the cause of sudden braking. Furthermore, if there is a high frequency of speeding, the analysis unit can identify the cause of speeding. Furthermore, if there is a short distance between vehicles, the analysis unit can identify the cause of short distances between vehicles. The generation unit generates a driving report based on the data analyzed by the analysis unit. For example, if there is a high frequency of sudden braking, the generation unit will generate advice such as, "You are braking suddenly a lot, so try to brake earlier." The generation unit can generate driving reports in text format or graph format. For example, the generation unit can generate a driving report in text format and provide it to senior drivers. The generation unit can also generate a driving report in graph format and provide it to senior drivers. Furthermore, the generation unit can generate a driving report in audio format and provide it to senior drivers. The sharing unit shares the driving report generated by the generation unit with family members and care managers. For example, the sharing unit sends the driving report to the senior driver's smartphone or computer. The sharing unit can also share the driving report via email or app notification. For example, the sharing unit sends the driving report to senior drivers via email. The sharing unit can also send the driving report to senior drivers via app notification.Furthermore, the shared section can also save driving reports to cloud storage, making them accessible to senior drivers. This allows the driving analysis system according to the embodiment to analyze senior drivers' driving data, generate and share driving reports, thereby supporting safe driving and providing peace of mind to family members and care managers.

[0030] The data collection unit collects driving data. For example, it collects driving data from sensors and cameras mounted on the vehicle. Specifically, it can collect data such as vehicle speed, brake usage frequency, steering operation, and following distance. The data collection unit can also collect vehicle acceleration data using an acceleration sensor. The acceleration sensor detects the vehicle's forward, backward, and lateral movements with high precision, and records the timing of sudden acceleration and deceleration in detail. It can also collect vehicle rotation data using a gyro sensor. The gyro sensor detects the vehicle's tilt and turning motion, helping to understand behavior during sudden changes of direction and curves. Furthermore, it can collect video data of the vehicle's surroundings using a camera. The camera records front, rear, and side views in real time, acquiring information on other vehicles, pedestrians, and road signs. This allows the data collection unit to collect multifaceted data and understand driving conditions in detail. The collected data is temporarily stored in the vehicle's data storage and transmitted to a central data server via wireless communication. Data transmission may be in real time or batched at regular time intervals. This allows the data collection unit to comprehensively record all conditions during operation, providing the information necessary for subsequent analysis and report generation.

[0031] The analysis unit analyzes the driving data collected by the collection unit. For example, the analysis unit evaluates the day's driving based on the collected driving data. Specifically, it can identify areas for improvement and points to pay attention to based on the driving data. The analysis unit uses AI to analyze the data and detect driving patterns and abnormal behavior. For example, if sudden braking occurs frequently, it can identify the cause of sudden braking. The AI ​​analyzes the timing and intensity of braking operations to identify the situations in which sudden braking occurs. It can also identify the cause of speeding if speeding occurs frequently. The AI ​​compares speed data with road speed limit information to identify the locations and times in which speeding occurs. Furthermore, if the following distance is short, it can identify the cause of the short following distance. The AI ​​analyzes distance data to the vehicle in front to identify the situations in which the following distance becomes short. As a result, the analysis unit can quickly and accurately analyze driving data and reveal the driver's behavior patterns and risk factors. In addition, the analysis unit can utilize past driving data and statistical information to perform long-term driving trend and risk assessments. For example, based on past data, it can analyze the driving style and risk trends of a specific driver and propose future improvement measures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates driving reports based on data analyzed by the analysis unit. For example, if the frequency of sudden braking is high, the generation unit will generate advice such as, "You brake suddenly a lot, so try to brake earlier." The generation unit can generate driving reports in text or graph format. Specifically, it creates detailed reports based on driving data and presents drivers with specific areas for improvement and points to pay attention to. For example, it can visually display the frequency of sudden braking, the number of times speeding is exceeded, and fluctuations in following distance using graphs and charts, allowing drivers to understand their driving style at a glance. The generation unit can also generate driving reports in audio format and provide them to senior drivers. Audio reports are effective for senior drivers with impaired vision because they can convey information without relying on visual cues. Furthermore, the generation unit can customize driving reports. For example, it can adjust the content and expression of advice according to the driver's age and driving experience. This allows the generation unit to provide drivers with optimal feedback and support the improvement of their driving skills. The generated driving reports are provided not only to the driver themselves but also to their family and care managers, providing important information to support 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 send driving reports to senior drivers' smartphones or computers. Specifically, driving reports can be shared via email or app notifications. The sharing unit can also send driving reports to senior drivers via email or app notifications. Furthermore, the sharing unit can save driving reports to cloud storage, making them accessible to senior drivers. Reports stored in cloud storage can be accessed anytime from any internet-connected device, and family members and care managers can also access them. This allows for real-time monitoring of the driver's driving situation and the provision of appropriate advice and support as needed. Additionally, the sharing unit can regularly update driving reports, providing information based on the latest driving data. For example, it can automatically generate weekly or monthly driving reports and share them regularly to continuously support the driver's improvement of driving skills and risk management. The sharing unit can also customize the scope of driving report sharing. For example, it can be set to share reports only with specific family members or care managers, or to share them with all relevant parties, allowing for flexible sharing according to the driver's needs. This allows the shared area to support safe driving by the driver and provide peace of mind to family members and care managers.

[0034] The data collection unit can collect driving data from sensors and cameras mounted on the vehicle. For example, the data collection unit can collect driving data using an acceleration sensor mounted on the vehicle. The data collection unit can collect acceleration data of the vehicle using an acceleration sensor. The data collection unit can also collect driving data using a gyro sensor mounted on the vehicle. The data collection unit can collect rotation data of the vehicle using a gyro sensor. Furthermore, the data collection unit can also collect driving data using a camera mounted on the vehicle. The data collection unit can collect video data of the vehicle's surroundings using a camera. By collecting driving data from sensors and cameras mounted on the vehicle, accurate driving data can be obtained. 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 driving data acquired from sensors and cameras mounted on the vehicle into a generating AI and have the generating AI perform analysis of the driving data.

[0035] The analysis unit can evaluate the day's driving based on the collected driving data and identify areas for improvement and points to pay attention to. For example, the analysis unit can evaluate the driving based on the collected driving data. The analysis unit can identify areas for improvement and points to pay attention to based on the driving data. For example, if there is a high frequency of sudden braking, the analysis unit can identify the cause of sudden braking. Also, if there is a high frequency of speeding, the analysis unit can identify the cause of speeding. Furthermore, if there is a short following distance, the analysis unit can identify the cause of short following distances. In this way, by evaluating driving based on driving data and identifying areas for improvement and points to pay attention to, the analysis unit supports the improvement of senior drivers' driving skills. 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 collected driving data into a generating AI and have the generating AI perform the analysis of the driving data.

[0036] The generation unit can generate advice such as, "You're braking suddenly a lot, so try to brake earlier," when sudden braking occurs frequently. The generation unit can generate driving reports in text or graph format. For example, the generation unit can generate driving reports in text format and provide them to senior drivers. The generation unit can also generate driving reports in graph format and provide them to senior drivers. Furthermore, the generation unit can generate driving reports in audio format and provide them to senior drivers. This promotes improvement in the driving skills of senior drivers by providing specific advice when sudden braking occurs frequently. 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 use generation AI to generate advice such as, "You're braking suddenly a lot, so try to brake earlier," when sudden braking occurs frequently.

[0037] The sharing unit can send the generated driving report to the senior driver's smartphone or computer. For example, the sharing unit can send the driving report to the senior driver's smartphone. The sharing unit can also share the driving report via email or app notification. For example, the sharing unit can send the driving report to the senior driver via email. The sharing unit can also send the driving report to the senior driver via app notification. Furthermore, the sharing unit can save the driving report to cloud storage and make it accessible to the senior driver. This allows the senior driver to check their driving status by sending the driving report to their smartphone or computer. Some or all of the above processing in the sharing unit may be performed using AI, for example, or not using AI. For example, the sharing unit can use a generation AI to send the generated driving report to the senior driver's smartphone.

[0038] The sharing function can share the generated driving reports with family members and care managers. For example, the sharing function can send driving reports to family members and care managers via email. The sharing function can also share driving reports via email or app notifications. For example, the sharing function can send driving reports to family members and care managers via email. The sharing function can also send driving reports to family members and care managers via app notifications. Furthermore, the sharing function can save driving reports to cloud storage and make them accessible to family members and care managers. This allows for understanding the driving situation of senior drivers and providing appropriate support by sharing driving reports with family members and care managers. Some or all of the above processes in the sharing function may be performed using AI, for example, or not using AI. For example, the sharing function can use a generation AI to send the generated driving reports to family members and care managers.

[0039] The analysis unit can accumulate driving data and analyze long-term driving patterns. For example, the analysis unit can accumulate driving data and analyze long-term driving patterns. Based on the driving data, the analysis unit can identify long-term driving patterns. For example, if there is a high frequency of sudden braking, the analysis unit can identify long-term patterns of sudden braking. The analysis unit can also identify long-term patterns of speeding if there is a high frequency of speeding. Furthermore, if there is a short following distance, the analysis unit can identify long-term patterns of following distance. By accumulating driving data and analyzing long-term driving patterns, it is possible to improve the driving skills of senior drivers and detect potential risks early. 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 accumulated driving data into a generating AI and have the generating AI perform an analysis of long-term driving patterns.

[0040] The analysis unit can provide advice based on long-term driving patterns, such as, "You've been driving at night more often lately, so be careful of declining eyesight." The analysis unit can identify long-term driving patterns based on driving data and provide appropriate advice. For example, if there is a high frequency of sudden braking, the analysis unit can identify long-term patterns of sudden braking and provide appropriate advice. The analysis unit can also identify long-term patterns of speeding if there is a high frequency of speeding and provide appropriate advice. Furthermore, if the following distance is short, the analysis unit can identify long-term patterns of following distance and provide appropriate advice. In this way, by providing specific advice based on long-term driving patterns, it supports safe driving for senior drivers. 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 use generative AI to provide advice such as, "You've been driving at night more often lately, so be careful of declining eyesight," based on long-term driving patterns.

[0041] The data collection unit can optimize its data collection method based on weather and traffic conditions when collecting driving data. For example, in rainy weather, the data collection unit can focus on collecting data on brake usage frequency and following distance. In congested traffic, the data collection unit can focus on collecting data on idling time and number of stops. In sunny weather, the data collection unit can focus on collecting data on speed and steering operation. By optimizing the data collection method according to weather and traffic conditions, more accurate driving data can be collected. 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 use generative AI to optimize the data collection method based on weather and traffic conditions when collecting driving data.

[0042] The data collection unit can monitor the health status of senior drivers while collecting driving data and issue warnings if abnormalities are detected. For example, the data collection unit can issue a warning to stop driving if the heart rate suddenly increases. The data collection unit can also issue a warning to refrain from driving if the blood pressure is abnormally high. Furthermore, the data collection unit can continue normal driving data collection if the driver's health status is stable. This supports safe driving by monitoring the health status of senior drivers and issuing warnings if abnormalities are detected. 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 use generative AI to monitor the health status of senior drivers while collecting driving data and issue warnings if abnormalities are detected.

[0043] The data collection unit can adjust the types of data collected by referring to the senior driver's past driving history when collecting driving data. For example, if the senior driver frequently braked suddenly in the past, the data collection unit can focus on collecting data on brake usage frequency. If the senior driver frequently exceeded the speed limit in the past, the data collection unit can focus on collecting speed data. Furthermore, if the senior driver frequently maintained short following distances in the past, the data collection unit can focus on collecting data on following distances. In this way, by adjusting the types of data by referring to the senior driver's past driving history, more appropriate data can be collected. 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 past driving history into a generating AI and use the generating AI to adjust the types of data to be collected.

[0044] The data collection unit can analyze the driving style of senior drivers when collecting driving data, thereby improving the accuracy of the collected data. For example, if there is a lot of sudden acceleration, the data collection unit can collect acceleration sensor data with high accuracy. If there is a lot of sudden deceleration, the data collection unit can collect brake sensor data with high accuracy. In addition, if there is a stable driving style, the data collection unit can improve the overall accuracy of data collection. By analyzing the driving style of senior drivers and improving the accuracy of the collected data, more accurate data can be collected. 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 driving style of senior drivers into a generating AI and use the generating AI to improve the accuracy of the collected data.

[0045] The analysis unit can detect abnormal values ​​in driving data during analysis and identify the cause of the abnormality. For example, if there is a high frequency of sudden braking, the analysis unit can identify the cause. If there is a high frequency of speeding, the analysis unit can identify the cause. Furthermore, if the following distance is too short, the analysis unit can identify the cause. In this way, by detecting abnormal values ​​in driving data and identifying the cause of the abnormality, it promotes improvement in the driving skills of senior drivers. 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 abnormal values ​​in driving data into a generating AI and use the generating AI to identify the cause of the abnormality.

[0046] The analysis unit can analyze driving data in real time during analysis and provide immediate feedback. For example, if sudden braking is detected, the analysis unit can provide immediate feedback. If speeding is detected, the analysis unit can provide immediate feedback. The analysis unit can also provide immediate feedback if the distance between vehicles is too short. This allows for rapid support in improving the driving skills of senior drivers by analyzing driving data in real time and providing immediate feedback. 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 use generative AI to analyze driving data in real time and provide immediate feedback.

[0047] The analysis unit can improve the accuracy of its analysis by referring to the senior driver's driving history during the analysis process. For example, the analysis unit can analyze the frequency of sudden braking based on past driving history. The analysis unit can also analyze the frequency of speeding based on past driving history. Furthermore, the analysis unit can analyze data on the distance between vehicles based on past driving history. This improves the accuracy of the analysis by referring to the senior driver's driving history. 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 senior driver's driving history into a generating AI and use the generating AI to improve the accuracy of the analysis.

[0048] The analysis unit can customize the analysis results based on the geographical information of the driving data during the analysis. For example, the analysis unit can analyze driving data in urban areas to identify the frequency of sudden braking on specific roads. The analysis unit can analyze driving data in suburban areas to identify the frequency of speeding. The analysis unit can also analyze driving data in mountainous areas to identify data on the distance between vehicles. By customizing the analysis results based on the geographical information of the driving data, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical information of the driving data into a generating AI and use the generating AI to customize the analysis results.

[0049] The generation unit can adjust the level of detail in a driving report based on the importance of the driving data. For example, if sudden braking occurs frequently, the generation unit can generate a report with detailed advice. If speeding occurs frequently, the generation unit can generate a report with detailed advice. The generation unit can also generate a report with detailed advice if the following distance is short. By adjusting the level of detail in the report based on the importance of the driving data, more appropriate feedback can be provided. 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 use generation AI to adjust the level of detail in the report based on the importance of the driving data.

[0050] The generation unit can apply different report generation algorithms depending on the category of driving data when generating driving reports. For example, the generation unit can apply an algorithm that analyzes the frequency of brake use to data on sudden braking. For data on speeding, the generation unit can apply an algorithm that analyzes fluctuations in speed. Furthermore, the generation unit can apply an algorithm that analyzes fluctuations in following distance to data on following distance. By applying different report generation algorithms depending on the category of driving data, more appropriate feedback can be provided. 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 use generation AI to apply different report generation algorithms depending on the category of driving data.

[0051] The generation unit can determine the priority of reports based on the timing of the submission of operating data when generating operating reports. For example, the generation unit can prioritize reports based on the most recent operating data. The generation unit can prioritize reports based on operating data within a specific period. The generation unit can also prioritize reports based on the urgency of the operating data. This allows for more appropriate feedback to be provided by prioritizing reports based on the timing of the submission of operating data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use a generation AI to determine the priority of reports based on the timing of the submission of operating data.

[0052] The generation unit can adjust the order of driving reports based on the relevance of the driving data when generating them. For example, the generation unit can display data on sudden braking first, data on speeding next, and data on following distance last. By adjusting the order of reports based on the relevance of the driving data, more appropriate feedback can be provided. 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 use generation AI to adjust the order of reports based on the relevance of the driving data.

[0053] The shared section can collect feedback from family members and care managers when sharing driving reports and incorporate it into the next report generation. For example, the shared section can reflect improvements in the next report based on feedback provided by family members. The shared section can also reflect points to be careful about in the next report based on feedback provided by care managers. Furthermore, the shared section can comprehensively analyze the feedback from family members and care managers and incorporate it into the next report. This allows for the provision of more appropriate feedback by collecting feedback from family members and care managers and incorporating it into the next report generation. Some or all of the above processing in the shared section may be performed using AI, for example, or not. For example, the shared section can use a generation AI to collect feedback from family members and care managers and incorporate it into the next report generation.

[0054] The sharing unit can select the optimal sharing method based on the recipient's device information when sharing driving reports. For example, if a family member is using a smartphone, the sharing unit can share the report in a format optimized for smartphones. If a care manager is using a computer, the sharing unit can share the report in a format optimized for computers. Furthermore, if a family member or care manager is using a tablet, the sharing unit can share the report in a format optimized for tablets. This allows for more appropriate feedback to be provided by selecting the optimal sharing method based on the recipient's device information. 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 recipient's device information into a generating AI and use the generating AI to select the optimal sharing method.

[0055] The sharing function can customize the content of driving reports based on the concerns of family members and care managers when sharing reports. For example, the sharing function can focus on including items of concern to family members (e.g., frequency of sudden braking) in the report. The sharing function can also focus on including items of concern to care managers (e.g., frequency of speeding) in the report. Furthermore, the sharing function can comprehensively analyze the concerns of family members and care managers and customize the content of the report. This allows for more appropriate feedback to be provided by customizing the report content based on the concerns of family members and care managers. Some or all of the above processing in the sharing function may be performed using AI, for example, or not. For example, the sharing function can input the concerns of family members and care managers into a generating AI and use the generating AI to customize the content of the report.

[0056] The sharing unit can select the optimal sharing method based on the geographical information of the recipient when sharing driving reports. For example, if family members live far away, the sharing unit can share the report online. If care managers live nearby, the sharing unit can share the report in person. The sharing unit can also comprehensively analyze the geographical information of family members and care managers to select the optimal sharing method. This allows for more appropriate feedback to be provided by selecting the optimal sharing method based on the geographical information of the recipient. Some or all of the above processing in the sharing unit may be performed using AI, for example, or not. For example, the sharing unit can input the geographical information of the recipient into a generating AI and use the generating AI to select the optimal sharing method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The driving analysis system can also incorporate a function to monitor the health status of senior drivers during the collection of driving data. For example, the data collection unit can collect vital data such as heart rate and blood pressure, and issue a warning if an abnormality is detected. Specifically, if the heart rate suddenly increases, it can issue a warning to stop driving. Similarly, if blood pressure is abnormally high, it can issue a warning to refrain from driving. Furthermore, if the driver's health status is stable, the system can continue collecting normal driving data. This allows the system to monitor the health status of senior drivers and issue warnings if abnormalities are detected, thereby supporting safe driving.

[0059] The driving analysis system can further optimize its data collection methods based on weather and traffic conditions. For example, the data collection unit can focus on collecting brake usage frequency and following distance during rainy weather. During traffic congestion, it can focus on collecting idling time and the number of stops. In sunny weather, it can focus on collecting speed and steering operation data. By optimizing the data collection method according to weather and traffic conditions, more accurate driving data can be collected.

[0060] The driving analysis system can further adjust the types of data collected by referencing the senior driver's past driving history. For example, if the driver frequently braked suddenly in the past, the system can focus on collecting data on brake usage frequency. If the driver frequently exceeded the speed limit in the past, the system can focus on collecting speed data. Similarly, if the driver frequently maintained short following distances in the past, the system can focus on collecting data on following distances. By adjusting the types of data based on the senior driver's past driving history, the system can collect more relevant data.

[0061] The driving analysis system can further improve the accuracy of collected data by analyzing the driving style of senior drivers during the data collection process. For example, if there is frequent rapid acceleration, the system can collect acceleration sensor data with high accuracy. If there is frequent rapid deceleration, it can collect brake sensor data with high accuracy. In addition, if there is a stable driving style, the system can improve the overall accuracy of data collection. By analyzing the driving style of senior drivers and improving the accuracy of the collected data, more accurate data can be obtained.

[0062] The driving analysis system can further detect anomalies in driving data during analysis and identify the causes of these anomalies. For example, it can identify the cause of frequent sudden braking, frequent speeding, and short following distances. By detecting anomalies in driving data and identifying the causes of these anomalies, it can promote improvements in the driving skills of senior drivers.

[0063] The driving analysis system can further adjust the level of detail in driving reports based on the importance of the driving data. For example, if sudden braking occurs frequently, a report with detailed advice can be generated. If speeding occurs frequently, a report with detailed advice can be generated. Similarly, if the following distance is short, a report with detailed advice can be generated. This allows for more appropriate feedback by adjusting the level of detail in the report based on the importance of the driving data.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects driving data. The data collection unit collects driving data from, for example, sensors and cameras mounted on the vehicle. The data collection unit can collect data such as speed, brake usage frequency, steering operation, and distance between vehicles. The data collection unit can collect vehicle acceleration data using, for example, an acceleration sensor. The data collection unit can also collect vehicle rotation data using a gyro sensor. Furthermore, the data collection unit can collect video data of the vehicle's surroundings using a camera. Step 2: The analysis unit analyzes the driving data collected by the collection unit. For example, the analysis unit evaluates the day's driving based on the collected driving data. Based on the driving data, the analysis unit can identify areas for improvement and points to pay attention to. For example, if there is a high frequency of sudden braking, the analysis unit can identify the cause of sudden braking. Also, if there is a high frequency of speeding, the analysis unit can identify the cause of speeding. Furthermore, if there is a short following distance, the analysis unit can identify the cause of short following distances. Step 3: The generation unit generates a driving report based on the data analyzed by the analysis unit. For example, if there is a high frequency of sudden braking, the generation unit will generate advice such as, "You are braking suddenly a lot, so try to brake earlier." The generation unit can generate the driving report in text format or graph format. For example, the generation unit can generate a driving report in text format and provide it to senior drivers. The generation unit can also generate a driving report in graph format and provide it to senior drivers. Furthermore, the generation unit can generate a driving report in audio format and provide it to senior drivers. Step 4: The sharing unit shares the driving report generated by the generation unit with family members and care managers. The sharing unit, for example, sends the driving report to the senior driver's smartphone or computer. The sharing unit can also share the driving report via email or app notification. For example, the sharing unit sends the driving report to the senior driver via email. The sharing unit can also send the driving report to the senior driver via app notification. Furthermore, the sharing unit can save the driving report to cloud storage and make it accessible to the senior driver.

[0066] (Example of form 2) The driving analysis system according to an embodiment of the present invention is a system in which an AI analyzes driving data after a senior driver has driven and generates a driving report. The driving analysis system collects driving data from sensors and cameras mounted on the vehicle after the senior driver has finished driving. This data includes speed, frequency of brake use, steering operation, and distance between vehicles. Next, the collected driving data is transmitted to the AI, which analyzes it. Based on the driving data, the AI ​​evaluates the day's driving and identifies areas for improvement and points to pay attention to. For example, if there is a high frequency of sudden braking, the AI ​​generates advice such as, "You're braking suddenly a lot, so try to brake earlier." The generated driving report is not only sent to the senior driver's smartphone or computer, but is also shared with family members and care managers. This allows family members and care managers to understand the senior driver's driving situation in real time. For example, family members can provide specific advice such as, "Dad, it seems you braked suddenly a lot today, so be more careful next time." Through this mechanism, senior drivers can objectively evaluate their driving skills and learn specific areas for improvement. In addition, family members and care managers can monitor the senior driver's driving situation with peace of mind by understanding their driving situation. For example, family members can provide positive feedback such as, "Mom, your driving was really good today, keep it up!" Furthermore, the AI ​​can accumulate driving data and analyze long-term driving patterns. This allows for improvements in senior drivers' driving skills and the early detection of potential risks. For example, the AI ​​can provide long-term advice such as, "You've been driving more at night recently, so be careful of your declining eyesight." In this way, by analyzing senior drivers' driving and generating and sharing driving reports, the AI ​​can support safe driving for senior drivers and provide peace of mind to their families and care managers. Thus, the driving analysis system can support safe driving and provide peace of mind to families and care managers by analyzing senior drivers' driving data and generating and sharing driving reports.

[0067] The driving analysis system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a sharing unit. The data collection unit collects driving data. The data collection unit collects driving data from, for example, sensors and cameras mounted on the vehicle. The data collection unit can collect data such as speed, frequency of brake use, steering operation, and distance between vehicles. The data collection unit can collect vehicle acceleration data using, for example, an acceleration sensor. The data collection unit can also collect vehicle rotation data using a gyro sensor. Furthermore, the data collection unit can also collect video data of the vehicle's surroundings using a camera. The analysis unit analyzes the driving data collected by the data collection unit. The analysis unit evaluates the day's driving based on the collected driving data. The analysis unit can identify areas for improvement and points to pay attention to based on the driving data. For example, if there is a high frequency of sudden braking, the analysis unit can identify the cause of sudden braking. Furthermore, if there is a high frequency of speeding, the analysis unit can identify the cause of speeding. Furthermore, if there is a short distance between vehicles, the analysis unit can identify the cause of short distances between vehicles. The generation unit generates a driving report based on the data analyzed by the analysis unit. For example, if there is a high frequency of sudden braking, the generation unit will generate advice such as, "You are braking suddenly a lot, so try to brake earlier." The generation unit can generate driving reports in text format or graph format. For example, the generation unit can generate a driving report in text format and provide it to senior drivers. The generation unit can also generate a driving report in graph format and provide it to senior drivers. Furthermore, the generation unit can generate a driving report in audio format and provide it to senior drivers. The sharing unit shares the driving report generated by the generation unit with family members and care managers. For example, the sharing unit sends the driving report to the senior driver's smartphone or computer. The sharing unit can also share the driving report via email or app notification. For example, the sharing unit sends the driving report to senior drivers via email. The sharing unit can also send the driving report to senior drivers via app notification.Furthermore, the shared section can also save driving reports to cloud storage, making them accessible to senior drivers. This allows the driving analysis system according to the embodiment to analyze senior drivers' driving data, generate and share driving reports, thereby supporting safe driving and providing peace of mind to family members and care managers.

[0068] The data collection unit collects driving data. For example, it collects driving data from sensors and cameras mounted on the vehicle. Specifically, it can collect data such as vehicle speed, brake usage frequency, steering operation, and following distance. The data collection unit can also collect vehicle acceleration data using an acceleration sensor. The acceleration sensor detects the vehicle's forward, backward, and lateral movements with high precision, and records the timing of sudden acceleration and deceleration in detail. It can also collect vehicle rotation data using a gyro sensor. The gyro sensor detects the vehicle's tilt and turning motion, helping to understand behavior during sudden changes of direction and curves. Furthermore, it can collect video data of the vehicle's surroundings using a camera. The camera records front, rear, and side views in real time, acquiring information on other vehicles, pedestrians, and road signs. This allows the data collection unit to collect multifaceted data and understand driving conditions in detail. The collected data is temporarily stored in the vehicle's data storage and transmitted to a central data server via wireless communication. Data transmission may be in real time or batched at regular time intervals. This allows the data collection unit to comprehensively record all conditions during operation, providing the information necessary for subsequent analysis and report generation.

[0069] The analysis unit analyzes the driving data collected by the collection unit. For example, the analysis unit evaluates the day's driving based on the collected driving data. Specifically, it can identify areas for improvement and points to pay attention to based on the driving data. The analysis unit uses AI to analyze the data and detect driving patterns and abnormal behavior. For example, if sudden braking occurs frequently, it can identify the cause of sudden braking. The AI ​​analyzes the timing and intensity of braking operations to identify the situations in which sudden braking occurs. It can also identify the cause of speeding if speeding occurs frequently. The AI ​​compares speed data with road speed limit information to identify the locations and times in which speeding occurs. Furthermore, if the following distance is short, it can identify the cause of the short following distance. The AI ​​analyzes distance data to the vehicle in front to identify the situations in which the following distance becomes short. As a result, the analysis unit can quickly and accurately analyze driving data and reveal the driver's behavior patterns and risk factors. In addition, the analysis unit can utilize past driving data and statistical information to perform long-term driving trend and risk assessments. For example, based on past data, it can analyze the driving style and risk trends of a specific driver and propose future improvement measures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0070] The generation unit generates driving reports based on data analyzed by the analysis unit. For example, if the frequency of sudden braking is high, the generation unit will generate advice such as, "You brake suddenly a lot, so try to brake earlier." The generation unit can generate driving reports in text or graph format. Specifically, it creates detailed reports based on driving data and presents drivers with specific areas for improvement and points to pay attention to. For example, it can visually display the frequency of sudden braking, the number of times speeding is exceeded, and fluctuations in following distance using graphs and charts, allowing drivers to understand their driving style at a glance. The generation unit can also generate driving reports in audio format and provide them to senior drivers. Audio reports are effective for senior drivers with impaired vision because they can convey information without relying on visual cues. Furthermore, the generation unit can customize driving reports. For example, it can adjust the content and expression of advice according to the driver's age and driving experience. This allows the generation unit to provide drivers with optimal feedback and support the improvement of their driving skills. The generated driving reports are provided not only to the driver themselves but also to their family and care managers, providing important information to support safe driving.

[0071] The sharing unit shares the driving reports generated by the generation unit with family members and care managers. For example, the sharing unit can send driving reports to senior drivers' smartphones or computers. Specifically, driving reports can be shared via email or app notifications. The sharing unit can also send driving reports to senior drivers via email or app notifications. Furthermore, the sharing unit can save driving reports to cloud storage, making them accessible to senior drivers. Reports stored in cloud storage can be accessed anytime from any internet-connected device, and family members and care managers can also access them. This allows for real-time monitoring of the driver's driving situation and the provision of appropriate advice and support as needed. Additionally, the sharing unit can regularly update driving reports, providing information based on the latest driving data. For example, it can automatically generate weekly or monthly driving reports and share them regularly to continuously support the driver's improvement of driving skills and risk management. The sharing unit can also customize the scope of driving report sharing. For example, it can be set to share reports only with specific family members or care managers, or to share them with all relevant parties, allowing for flexible sharing according to the driver's needs. This allows the shared area to support safe driving by the driver and provide peace of mind to family members and care managers.

[0072] The data collection unit can collect driving data from sensors and cameras mounted on the vehicle. For example, the data collection unit can collect driving data using an acceleration sensor mounted on the vehicle. The data collection unit can collect acceleration data of the vehicle using an acceleration sensor. The data collection unit can also collect driving data using a gyro sensor mounted on the vehicle. The data collection unit can collect rotation data of the vehicle using a gyro sensor. Furthermore, the data collection unit can also collect driving data using a camera mounted on the vehicle. The data collection unit can collect video data of the vehicle's surroundings using a camera. By collecting driving data from sensors and cameras mounted on the vehicle, accurate driving data can be obtained. 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 driving data acquired from sensors and cameras mounted on the vehicle into a generating AI and have the generating AI perform analysis of the driving data.

[0073] The analysis unit can evaluate the day's driving based on the collected driving data and identify areas for improvement and points to pay attention to. For example, the analysis unit can evaluate the driving based on the collected driving data. The analysis unit can identify areas for improvement and points to pay attention to based on the driving data. For example, if there is a high frequency of sudden braking, the analysis unit can identify the cause of sudden braking. Also, if there is a high frequency of speeding, the analysis unit can identify the cause of speeding. Furthermore, if there is a short following distance, the analysis unit can identify the cause of short following distances. In this way, by evaluating driving based on driving data and identifying areas for improvement and points to pay attention to, the analysis unit supports the improvement of senior drivers' driving skills. 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 collected driving data into a generating AI and have the generating AI perform the analysis of the driving data.

[0074] The generation unit can generate advice such as, "You're braking suddenly a lot, so try to brake earlier," when sudden braking occurs frequently. The generation unit can generate driving reports in text or graph format. For example, the generation unit can generate driving reports in text format and provide them to senior drivers. The generation unit can also generate driving reports in graph format and provide them to senior drivers. Furthermore, the generation unit can generate driving reports in audio format and provide them to senior drivers. This promotes improvement in the driving skills of senior drivers by providing specific advice when sudden braking occurs frequently. 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 use generation AI to generate advice such as, "You're braking suddenly a lot, so try to brake earlier," when sudden braking occurs frequently.

[0075] The sharing unit can send the generated driving report to the senior driver's smartphone or computer. For example, the sharing unit can send the driving report to the senior driver's smartphone. The sharing unit can also share the driving report via email or app notification. For example, the sharing unit can send the driving report to the senior driver via email. The sharing unit can also send the driving report to the senior driver via app notification. Furthermore, the sharing unit can save the driving report to cloud storage and make it accessible to the senior driver. This allows the senior driver to check their driving status by sending the driving report to their smartphone or computer. Some or all of the above processing in the sharing unit may be performed using AI, for example, or not using AI. For example, the sharing unit can use a generation AI to send the generated driving report to the senior driver's smartphone.

[0076] The sharing function can share the generated driving reports with family members and care managers. For example, the sharing function can send driving reports to family members and care managers via email. The sharing function can also share driving reports via email or app notifications. For example, the sharing function can send driving reports to family members and care managers via email. The sharing function can also send driving reports to family members and care managers via app notifications. Furthermore, the sharing function can save driving reports to cloud storage and make them accessible to family members and care managers. This allows for understanding the driving situation of senior drivers and providing appropriate support by sharing driving reports with family members and care managers. Some or all of the above processes in the sharing function may be performed using AI, for example, or not using AI. For example, the sharing function can use a generation AI to send the generated driving reports to family members and care managers.

[0077] The analysis unit can accumulate driving data and analyze long-term driving patterns. For example, the analysis unit can accumulate driving data and analyze long-term driving patterns. Based on the driving data, the analysis unit can identify long-term driving patterns. For example, if there is a high frequency of sudden braking, the analysis unit can identify long-term patterns of sudden braking. The analysis unit can also identify long-term patterns of speeding if there is a high frequency of speeding. Furthermore, if there is a short following distance, the analysis unit can identify long-term patterns of following distance. By accumulating driving data and analyzing long-term driving patterns, it is possible to improve the driving skills of senior drivers and detect potential risks early. 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 accumulated driving data into a generating AI and have the generating AI perform an analysis of long-term driving patterns.

[0078] The analysis unit can provide advice based on long-term driving patterns, such as, "You've been driving at night more often lately, so be careful of declining eyesight." The analysis unit can identify long-term driving patterns based on driving data and provide appropriate advice. For example, if there is a high frequency of sudden braking, the analysis unit can identify long-term patterns of sudden braking and provide appropriate advice. The analysis unit can also identify long-term patterns of speeding if there is a high frequency of speeding and provide appropriate advice. Furthermore, if the following distance is short, the analysis unit can identify long-term patterns of following distance and provide appropriate advice. In this way, by providing specific advice based on long-term driving patterns, it supports safe driving for senior drivers. 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 use generative AI to provide advice such as, "You've been driving at night more often lately, so be careful of declining eyesight," based on long-term driving patterns.

[0079] 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 will focus on collecting data immediately after the start of driving. If a senior driver is relaxed, the data collection unit can periodically collect data during driving. Also, if a senior driver is tired, the data collection unit can focus on collecting data immediately before the end of driving. This allows for the collection of more appropriate data by adjusting the timing of driving data collection according to the emotions of senior drivers. 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can use generative AI to estimate the emotions of senior drivers and adjust the timing of driving data collection based on the estimated emotions.

[0080] The data collection unit can optimize its data collection method based on weather and traffic conditions when collecting driving data. For example, in rainy weather, the data collection unit can focus on collecting data on brake usage frequency and following distance. In congested traffic, the data collection unit can focus on collecting data on idling time and number of stops. In sunny weather, the data collection unit can focus on collecting data on speed and steering operation. By optimizing the data collection method according to weather and traffic conditions, more accurate driving data can be collected. 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 use generative AI to optimize the data collection method based on weather and traffic conditions when collecting driving data.

[0081] The data collection unit can monitor the health status of senior drivers while collecting driving data and issue warnings if abnormalities are detected. For example, the data collection unit can issue a warning to stop driving if the heart rate suddenly increases. The data collection unit can also issue a warning to refrain from driving if the blood pressure is abnormally high. Furthermore, the data collection unit can continue normal driving data collection if the driver's health status is stable. This supports safe driving by monitoring the health status of senior drivers and issuing warnings if abnormalities are detected. 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 use generative AI to monitor the health status of senior drivers while collecting driving data and issue warnings if abnormalities are detected.

[0082] The data collection unit can estimate the emotions of senior drivers and determine the priority of 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 brake usage frequency. If a senior driver is relaxed, the data collection unit may prioritize collecting data on speed and steering. Furthermore, if a senior driver is tired, the data collection unit may prioritize collecting data on following distance. This allows for the priority collection of important data by determining the priority of data to collect according to the emotions of senior drivers. Emotion estimation is achieved using an emotion estimation function, for example, using 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, for example, or not using AI. For example, the data collection unit can use generative AI to estimate the emotions of senior drivers and determine the priority of data to collect based on the estimated emotions.

[0083] The data collection unit can adjust the types of data collected by referring to the senior driver's past driving history when collecting driving data. For example, if the senior driver frequently braked suddenly in the past, the data collection unit can focus on collecting data on brake usage frequency. If the senior driver frequently exceeded the speed limit in the past, the data collection unit can focus on collecting speed data. Furthermore, if the senior driver frequently maintained short following distances in the past, the data collection unit can focus on collecting data on following distances. In this way, by adjusting the types of data by referring to the senior driver's past driving history, more appropriate data can be collected. 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 past driving history into a generating AI and use the generating AI to adjust the types of data to be collected.

[0084] The data collection unit can analyze the driving style of senior drivers when collecting driving data, thereby improving the accuracy of the collected data. For example, if there is a lot of sudden acceleration, the data collection unit can collect acceleration sensor data with high accuracy. If there is a lot of sudden deceleration, the data collection unit can collect brake sensor data with high accuracy. In addition, if there is a stable driving style, the data collection unit can improve the overall accuracy of data collection. By analyzing the driving style of senior drivers and improving the accuracy of the collected data, more accurate data can be collected. 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 driving style of senior drivers into a generating AI and use the generating AI to improve the accuracy of the collected data.

[0085] The analysis unit can estimate the emotions of senior drivers and adjust the method of analyzing driving data based on the estimated emotions. For example, if a senior driver is tense, the analysis unit can focus on analyzing the frequency of brake use. If a senior driver is relaxed, the analysis unit can focus on analyzing data on speed and steering. If a senior driver is tired, the analysis unit can also focus on analyzing data on the distance between vehicles. By adjusting the method of analyzing driving data according to the emotions of senior drivers, more appropriate analysis results can be obtained. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to estimate the emotions of senior drivers and adjust the method of analyzing driving data based on the estimated emotions.

[0086] The analysis unit can detect abnormal values ​​in driving data during analysis and identify the cause of the abnormality. For example, if there is a high frequency of sudden braking, the analysis unit can identify the cause. If there is a high frequency of speeding, the analysis unit can identify the cause. Furthermore, if the following distance is too short, the analysis unit can identify the cause. In this way, by detecting abnormal values ​​in driving data and identifying the cause of the abnormality, it promotes improvement in the driving skills of senior drivers. 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 abnormal values ​​in driving data into a generating AI and use the generating AI to identify the cause of the abnormality.

[0087] The analysis unit can analyze driving data in real time during analysis and provide immediate feedback. For example, if sudden braking is detected, the analysis unit can provide immediate feedback. If speeding is detected, the analysis unit can provide immediate feedback. The analysis unit can also provide immediate feedback if the distance between vehicles is too short. This allows for rapid support in improving the driving skills of senior drivers by analyzing driving data in real time and providing immediate feedback. 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 use generative AI to analyze driving data in real time and provide immediate feedback.

[0088] The analysis unit can estimate the emotions of senior drivers and adjust the display method of the analysis results based on the estimated emotions. For example, if a senior driver is tense, the analysis unit can provide a simple and highly visible display method. If a senior driver is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if a senior driver is in a hurry, the analysis unit can provide a concise display method. This allows for more appropriate feedback to be provided by adjusting the display method of the analysis results according to the emotions of senior drivers. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can use generative AI to estimate the emotions of senior drivers and adjust the display method of the analysis results based on the estimated emotions.

[0089] The analysis unit can improve the accuracy of its analysis by referring to the senior driver's driving history during the analysis process. For example, the analysis unit can analyze the frequency of sudden braking based on past driving history. The analysis unit can also analyze the frequency of speeding based on past driving history. Furthermore, the analysis unit can analyze data on the distance between vehicles based on past driving history. This improves the accuracy of the analysis by referring to the senior driver's driving history. 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 senior driver's driving history into a generating AI and use the generating AI to improve the accuracy of the analysis.

[0090] The analysis unit can customize the analysis results based on the geographical information of the driving data during the analysis. For example, the analysis unit can analyze driving data in urban areas to identify the frequency of sudden braking on specific roads. The analysis unit can analyze driving data in suburban areas to identify the frequency of speeding. The analysis unit can also analyze driving data in mountainous areas to identify data on the distance between vehicles. By customizing the analysis results based on the geographical information of the driving data, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical information of the driving data into a generating AI and use the generating AI to customize the analysis results.

[0091] 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 generate a simple and easy-to-read report. If a senior driver is relaxed, the generation unit can generate a report with detailed information. Furthermore, if a senior driver is in a hurry, the generation unit can generate a concise report. This allows for more appropriate feedback by adjusting the presentation of the driving report according to the senior driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the generation unit may be performed using AI or not. For example, the generation unit can use a generative AI to estimate the emotions of senior drivers and adjust the presentation of the driving report based on the estimated emotions.

[0092] The generation unit can adjust the level of detail in a driving report based on the importance of the driving data. For example, if sudden braking occurs frequently, the generation unit can generate a report with detailed advice. If speeding occurs frequently, the generation unit can generate a report with detailed advice. The generation unit can also generate a report with detailed advice if the following distance is short. By adjusting the level of detail in the report based on the importance of the driving data, more appropriate feedback can be provided. 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 use generation AI to adjust the level of detail in the report based on the importance of the driving data.

[0093] The generation unit can apply different report generation algorithms depending on the category of driving data when generating driving reports. For example, the generation unit can apply an algorithm that analyzes the frequency of brake use to data on sudden braking. For data on speeding, the generation unit can apply an algorithm that analyzes fluctuations in speed. Furthermore, the generation unit can apply an algorithm that analyzes fluctuations in following distance to data on following distance. By applying different report generation algorithms depending on the category of driving data, more appropriate feedback can be provided. 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 use generation AI to apply different report generation algorithms depending on the category of driving data.

[0094] The generation unit can estimate the emotions of senior drivers and adjust the length of the driving report based on the estimated emotions. For example, if a senior driver is tense, the generation unit can generate a short, concise report. If a senior driver is relaxed, the generation unit can generate a longer report with detailed explanations. Furthermore, if a senior driver is in a hurry, the generation unit can generate a short, quick-to-read report. This allows for more appropriate feedback by adjusting the length of the driving report according to the senior driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the generation unit may be performed using AI or not. For example, the generation unit can use a generative AI to estimate the emotions of senior drivers and adjust the length of the driving report based on the estimated emotions.

[0095] The generation unit can determine the priority of reports based on the timing of the submission of operating data when generating operating reports. For example, the generation unit can prioritize reports based on the most recent operating data. The generation unit can prioritize reports based on operating data within a specific period. The generation unit can also prioritize reports based on the urgency of the operating data. This allows for more appropriate feedback to be provided by prioritizing reports based on the timing of the submission of operating data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use a generation AI to determine the priority of reports based on the timing of the submission of operating data.

[0096] The generation unit can adjust the order of driving reports based on the relevance of the driving data when generating them. For example, the generation unit can display data on sudden braking first, data on speeding next, and data on following distance last. By adjusting the order of reports based on the relevance of the driving data, more appropriate feedback can be provided. 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 use generation AI to adjust the order of reports based on the relevance of the driving data.

[0097] 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 tense, the sharing unit can share the report in a simple format. If the senior driver is relaxed, the sharing unit can share the report in a detailed format. Also, if the senior driver is in a hurry, the sharing unit can share the report in a concise format. This allows for more appropriate feedback to be provided by adjusting how the driving report is shared according to the senior driver's 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 use generative AI to estimate the senior driver's emotions and adjust how the driving report is shared based on the estimated emotions.

[0098] The shared section can collect feedback from family members and care managers when sharing driving reports and incorporate it into the next report generation. For example, the shared section can reflect improvements in the next report based on feedback provided by family members. The shared section can also reflect points to be careful about in the next report based on feedback provided by care managers. Furthermore, the shared section can comprehensively analyze the feedback from family members and care managers and incorporate it into the next report. This allows for the provision of more appropriate feedback by collecting feedback from family members and care managers and incorporating it into the next report generation. Some or all of the above processing in the shared section may be performed using AI, for example, or not. For example, the shared section can use a generation AI to collect feedback from family members and care managers and incorporate it into the next report generation.

[0099] The sharing unit can select the optimal sharing method based on the recipient's device information when sharing driving reports. For example, if a family member is using a smartphone, the sharing unit can share the report in a format optimized for smartphones. If a care manager is using a computer, the sharing unit can share the report in a format optimized for computers. Furthermore, if a family member or care manager is using a tablet, the sharing unit can share the report in a format optimized for tablets. This allows for more appropriate feedback to be provided by selecting the optimal sharing method based on the recipient's device information. 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 recipient's device information into a generating AI and use the generating AI to select the optimal sharing method.

[0100] 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 to alleviate stress. If the senior driver is relaxed, the sharing unit can increase the sharing frequency to provide more detailed feedback. Furthermore, if the senior driver is in a hurry, the sharing unit can adjust the sharing frequency to share reports at the appropriate time. This allows for more appropriate feedback by adjusting the frequency of sharing driving reports according to the senior driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the sharing unit may be performed using AI or not. For example, the sharing unit can use generative AI to estimate the senior driver's emotions and adjust the frequency of sharing driving reports based on the estimated emotions.

[0101] The sharing function can customize the content of driving reports based on the concerns of family members and care managers when sharing reports. For example, the sharing function can focus on including items of concern to family members (e.g., frequency of sudden braking) in the report. The sharing function can also focus on including items of concern to care managers (e.g., frequency of speeding) in the report. Furthermore, the sharing function can comprehensively analyze the concerns of family members and care managers and customize the content of the report. This allows for more appropriate feedback to be provided by customizing the report content based on the concerns of family members and care managers. Some or all of the above processing in the sharing function may be performed using AI, for example, or not. For example, the sharing function can input the concerns of family members and care managers into a generating AI and use the generating AI to customize the content of the report.

[0102] The sharing unit can select the optimal sharing method based on the geographical information of the recipient when sharing driving reports. For example, if family members live far away, the sharing unit can share the report online. If care managers live nearby, the sharing unit can share the report in person. The sharing unit can also comprehensively analyze the geographical information of family members and care managers to select the optimal sharing method. This allows for more appropriate feedback to be provided by selecting the optimal sharing method based on the geographical information of the recipient. Some or all of the above processing in the sharing unit may be performed using AI, for example, or not. For example, the sharing unit can input the geographical information of the recipient into a generating AI and use the generating AI to select the optimal sharing method.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The driving analysis system can also incorporate a function to monitor the health status of senior drivers during the collection of driving data. For example, the data collection unit can collect vital data such as heart rate and blood pressure, and issue a warning if an abnormality is detected. Specifically, if the heart rate suddenly increases, it can issue a warning to stop driving. Similarly, if blood pressure is abnormally high, it can issue a warning to refrain from driving. Furthermore, if the driver's health status is stable, the system can continue collecting normal driving data. This allows the system to monitor the health status of senior drivers and issue warnings if abnormalities are detected, thereby supporting safe driving.

[0105] The driving analysis system can further optimize its data collection methods based on weather and traffic conditions. For example, the data collection unit can focus on collecting brake usage frequency and following distance during rainy weather. During traffic congestion, it can focus on collecting idling time and the number of stops. In sunny weather, it can focus on collecting speed and steering operation data. By optimizing the data collection method according to weather and traffic conditions, more accurate driving data can be collected.

[0106] The driving analysis system can further adjust the types of data collected by referencing the senior driver's past driving history. For example, if the driver frequently braked suddenly in the past, the system can focus on collecting data on brake usage frequency. If the driver frequently exceeded the speed limit in the past, the system can focus on collecting speed data. Similarly, if the driver frequently maintained short following distances in the past, the system can focus on collecting data on following distances. By adjusting the types of data based on the senior driver's past driving history, the system can collect more relevant data.

[0107] The driving analysis system can further improve the accuracy of collected data by analyzing the driving style of senior drivers during the data collection process. For example, if there is frequent rapid acceleration, the system can collect acceleration sensor data with high accuracy. If there is frequent rapid deceleration, it can collect brake sensor data with high accuracy. In addition, if there is a stable driving style, the system can improve the overall accuracy of data collection. By analyzing the driving style of senior drivers and improving the accuracy of the collected data, more accurate data can be obtained.

[0108] The driving analysis system can further detect anomalies in driving data during analysis and identify the causes of these anomalies. For example, it can identify the cause of frequent sudden braking, frequent speeding, and short following distances. By detecting anomalies in driving data and identifying the causes of these anomalies, it can promote improvements in the driving skills of senior drivers.

[0109] The driving analysis system can further estimate the senior driver's emotions during the analysis of driving data and adjust the analysis method based on the estimated emotions. For example, if the senior driver is tense, the system can focus on analyzing the frequency of brake use. If the senior driver is relaxed, the system can focus on analyzing data such as speed and steering. If the senior driver is tired, the system can focus on analyzing data such as the distance between vehicles. By adjusting the analysis method according to the senior driver's emotions, more appropriate analysis results can be obtained.

[0110] The driving analysis system can further estimate the senior driver's emotions during the analysis of driving data and adjust the display method of the analysis results based on the estimated emotions. For example, if the senior driver is tense, a simple and highly visible display method can be provided. If the senior driver is relaxed, a display method including detailed information can be provided. Also, if the senior driver is in a hurry, a display method that focuses on the essentials can be provided. In this way, by adjusting the display method of the analysis results according to the senior driver's emotions, more appropriate feedback can be provided.

[0111] The driving analysis system can also estimate the senior driver's emotions when generating driving reports and adjust the presentation of the reports based on these estimates. For example, if the senior driver is tense, it can generate a simple and easy-to-read report. If the senior driver is relaxed, it can generate a report with more detailed information. Furthermore, if the senior driver is in a hurry, it can generate a concise report. This allows for more appropriate feedback by adjusting the presentation of driving reports according to the senior driver's emotions.

[0112] The driving analysis system can further adjust the level of detail in driving reports based on the importance of the driving data. For example, if sudden braking occurs frequently, a report with detailed advice can be generated. If speeding occurs frequently, a report with detailed advice can be generated. Similarly, if the following distance is short, a report with detailed advice can be generated. This allows for more appropriate feedback by adjusting the level of detail in the report based on the importance of the driving data.

[0113] The driving analysis system can also estimate the senior driver's emotions when sharing driving reports and adjust how the reports are shared based on those emotions. For example, if the senior driver is tense, the report can be shared in a simple format. If the senior driver is relaxed, the report can be shared in a detailed format. If the senior driver is in a hurry, the report can be shared in a concise format. This allows for more appropriate feedback to be provided by adjusting how driving reports are shared according to the senior driver's emotions.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The data collection unit collects driving data. The data collection unit collects driving data from, for example, sensors and cameras mounted on the vehicle. The data collection unit can collect data such as speed, brake usage frequency, steering operation, and distance between vehicles. The data collection unit can collect vehicle acceleration data using, for example, an acceleration sensor. The data collection unit can also collect vehicle rotation data using a gyro sensor. Furthermore, the data collection unit can collect video data of the vehicle's surroundings using a camera. Step 2: The analysis unit analyzes the driving data collected by the collection unit. For example, the analysis unit evaluates the day's driving based on the collected driving data. Based on the driving data, the analysis unit can identify areas for improvement and points to pay attention to. For example, if there is a high frequency of sudden braking, the analysis unit can identify the cause of sudden braking. Also, if there is a high frequency of speeding, the analysis unit can identify the cause of speeding. Furthermore, if there is a short following distance, the analysis unit can identify the cause of short following distances. Step 3: The generation unit generates a driving report based on the data analyzed by the analysis unit. For example, if there is a high frequency of sudden braking, the generation unit will generate advice such as, "You are braking suddenly a lot, so try to brake earlier." The generation unit can generate the driving report in text format or graph format. For example, the generation unit can generate a driving report in text format and provide it to senior drivers. The generation unit can also generate a driving report in graph format and provide it to senior drivers. Furthermore, the generation unit can generate a driving report in audio format and provide it to senior drivers. Step 4: The sharing unit shares the driving report generated by the generation unit with family members and care managers. The sharing unit, for example, sends the driving report to the senior driver's smartphone or computer. The sharing unit can also share the driving report via email or app notification. For example, the sharing unit sends the driving report to the senior driver via email. The sharing unit can also send the driving report to the senior driver via app notification. Furthermore, the sharing unit can save the driving report to cloud storage and make it accessible to the senior driver.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and sharing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects driving data using the camera 42 and sensors of the smart device 14 and processes the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected driving data. The generation unit generates a driving report using the specific processing unit 290 of the data processing unit 12, and the sharing unit shares the driving report with family members or care managers using the control unit 46A 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 can be modified in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 glasses 214 and the data processing unit 12. For example, the collection unit collects driving data using the camera 42 and sensors of the smart glasses 214 and processes the data with the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected driving data. The generation unit generates a driving report with the specific processing unit 290 of the data processing unit 12, and the sharing unit shares the driving report with family members or care managers with the control unit 46A of the smart glasses 214, for example. 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.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and sharing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects driving data using the camera 42 and sensors of the headset terminal 314 and processes the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected driving data. The generation unit generates a driving report using the specific processing unit 290 of the data processing unit 12, and the sharing unit shares the driving report with family members and care managers using the control unit 46A 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 can be modified in various ways.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and sharing unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects driving data using the camera 42 and sensors of the robot 414 and processes the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected driving data. The generation unit generates a driving report with, for example, the specific processing unit 290 of the data processing unit 12, and the sharing unit shares the driving report with family members and care managers with, for example, the control unit 46A 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Note 1) A data collection unit that collects driving data, An analysis unit analyzes the operating data collected by the aforementioned collection unit, A generation unit that generates an operation report based on the data analyzed 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 vehicle collects driving data from sensors and cameras mounted on the vehicle. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected driving data, the day's driving is evaluated to 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 If sudden braking is frequent, the system will generate advice such as, "You brake suddenly a lot, so try to brake earlier." The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned shared portion is, The generated driving report is sent to the senior driver's smartphone or computer. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned shared portion is, Share the generated driving report with family members and care managers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, Accumulate driving data and analyze long-term driving patterns. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Based on long-term driving patterns, the system provides advice such as, "You've been driving more at night recently, so be careful of declining eyesight." The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is When collecting driving data, the data collection method is optimized based on weather and traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During the collection of driving data, the system monitors the health status of senior drivers and issues warnings if any abnormalities are detected. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is The system estimates the emotions of senior drivers and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting driving data, we adjust the types of data collected by referring to the senior driver's past driving history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting driving data, we analyze the driving styles of senior drivers to improve the accuracy of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, We estimate the emotions of senior drivers and adjust the analysis method of driving data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, abnormal values ​​in the operating data are detected, and the cause of the abnormality is identified. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, operating data is analyzed in real time, and feedback is provided immediately. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system estimates the emotions of senior drivers and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the driving history of senior drivers is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the analysis results are customized based on the geographical information of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) 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 23) The generating unit is When generating driving reports, different report generation algorithms are applied depending on the category of driving data. The system described in Appendix 1, characterized by the features described herein. (Note 24) 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 25) 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 26) 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 27) 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 28) The aforementioned shared portion is, When sharing driving reports, collect feedback from family members and care managers and incorporate it into the generation of future reports. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned shared portion is, When sharing driving reports, the system selects the optimal sharing method based on the recipient's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) 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 31) The aforementioned shared portion is, When sharing driving reports, customize the report content based on the interests of family members and care managers. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned shared portion is, When sharing driving reports, the optimal sharing method is selected based on the geographical information of the recipient. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0188] 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, An analysis unit analyzes the operating data collected by the aforementioned collection unit, A generation unit that generates an operation report based on the data analyzed 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 vehicle collects driving data from sensors and cameras mounted on the vehicle. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected driving data, the day's driving is evaluated to identify areas for improvement and points to pay attention to. The system according to feature 1.

4. The generating unit is Generate advice when sudden braking is frequent. The system according to feature 1.

5. The aforementioned shared portion is, The generated driving report is sent to the senior driver's smartphone or computer. The system according to feature 1.

6. The aforementioned shared portion is, Share the generated driving report with family members and care managers. The system according to feature 1.

7. The aforementioned analysis unit, Accumulate driving data and analyze long-term driving patterns. The system according to feature 1.

8. The aforementioned analysis unit, We provide advice based on long-term driving patterns. The system according to feature 1.

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