system

Abstract

The system according to the embodiment aims to analyze driving data of elderly drivers and support safe driving. According to an embodiment, the system includes a collection unit, an analysis unit, and a report unit. The collection unit collects driving data. The analysis unit analyzes the data collected by the collection unit. The report unit reports the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately provide means to properly assess the driving ability of elderly drivers and support their safe driving, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze driving data of elderly drivers and support safe driving. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a report unit. The collection unit collects driving data. The analysis unit analyzes the data collected by the collection unit. The report unit reports the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze driving data of elderly drivers and support safe driving. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A driving diagnosis system according to an embodiment of the present invention collects driving data from elderly drivers and evaluates the difficulty of safe driving due to declining physical abilities. This driving diagnosis system performs detailed analysis of changes in behavioral patterns and cognitive abilities while driving and reports daily driving diagnoses. For example, it collects data such as braking reaction time, lane change frequency, and speed fluctuations, and evaluates the elderly driver's driving ability based on this data. Furthermore, for elderly drivers who have reached their driving limits, the system also functions as a support tool to encourage them to surrender their driver's licenses in cooperation with local governments. This tool provides support for family members to objectively understand the elderly driver's driving ability and take appropriate measures. This allows the driving diagnosis system to provide a detailed evaluation of the elderly driver's driving ability and encourage appropriate measures.

[0029] A driving diagnosis system according to an embodiment includes a collection unit, an analysis unit, and a report unit. The collection unit collects driving data. The driving data includes, but is not limited to, speed, frequency of brake use, and number of lane changes. The collection unit collects driving data using, for example, a sensor mounted on a vehicle. The collection unit can also collect driving data in real time. For example, the collection unit collects vehicle position information using a GPS sensor and records speed and travel route. The collection unit can also record frequency of brake use using a brake pedal pressure sensor. The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on, for example, but is not limited to, the algorithm used and the purpose of the analysis. For example, the analysis unit can analyze driving data using a machine learning algorithm to evaluate the driving ability of an elderly driver. The analysis unit can also extract driving patterns using data mining technology to evaluate driving safety. The analysis unit can also identify trends in driving data and evaluate changes in driving ability using statistical analysis. The report unit reports the analysis results obtained by the analysis unit. The report is generated based on, for example, a report format and details of the report content, but is not limited to such examples. For example, the report unit visually displays the driving diagnosis results as graphs or charts. The report unit can also report the driving diagnosis results in detail in text format. Furthermore, the report unit can provide the driving diagnosis results via email or a web application. As a result, the driving diagnosis system according to the embodiment can consistently collect, analyze, and report driving data.

[0030] The collection unit can collect driving data such as brake reaction time, lane change frequency, and speed fluctuation. The collection unit, for example, uses a brake pedal pressure sensor to measure brake reaction time. For example, the collection unit measures the time from the moment the brake pedal is pressed until the vehicle stops and records the reaction time. The collection unit can also collect vehicle position information in real time to measure lane change frequency. For example, the collection unit uses a GPS sensor to record vehicle position information and measure the number of lane changes. The collection unit can also use a vehicle speed sensor to measure speed fluctuation. For example, the collection unit records the vehicle speed in real time and measures the range and frequency of speed fluctuations. This allows the collection unit to collect detailed driving data such as brake reaction time, lane change frequency, and speed fluctuations.

[0031] The analysis unit can evaluate the driving ability of drivers over a certain age based on the collected data. The analysis unit can, for example, evaluate the driving ability of an elderly driver based on the collected data. For example, the analysis unit can determine that a driver's driving ability is declining if the driver's braking reaction time is long. The analysis unit can also determine that a driver's driving stability is declining if the driver changes lanes frequently. Furthermore, the analysis unit can determine that a driver's driving consistency is declining if the driver's speed fluctuates greatly. This allows the analysis unit to perform a detailed evaluation of the elderly driver's driving ability based on the collected data.

[0032] The report unit can report the daily driving diagnosis. The report unit visually displays the results of the daily driving diagnosis as graphs or charts, for example. For example, the report unit can display changes in braking reaction time as a line graph, allowing changes in driving ability to be visually understood. The report unit can also display the frequency of lane changes as a bar graph, thereby evaluating driving stability. Furthermore, the report unit can display speed fluctuations as a histogram, thereby evaluating driving consistency. This allows the report unit to report the results of the daily driving diagnosis in detail.

[0033] The driving diagnosis system includes a notification unit that generates a notification recommending the driver's license surrender based on the results of the driving ability evaluation. The notification unit generates the notification recommending the driver's license surrender based on the results of the driving ability evaluation, for example. For example, if the notification unit determines that the driver's driving ability has deteriorated, it generates a message recommending the driver's license surrender. The notification unit can also send the notification recommending the driver's license surrender via email or SMS. Furthermore, the notification unit can provide the notification recommending the driver's license surrender to family members or local governments. This allows the notification unit to generate a detailed notification recommending the driver's license surrender based on the results of the driving ability evaluation.

[0034] The driving diagnosis system includes a material providing unit that provides materials for a family meeting. The material providing unit provides, for example, materials for the family meeting. For example, the material providing unit provides materials that visually display the driving diagnosis results as graphs or charts. The material providing unit can also provide materials that report the driving diagnosis results in detail in text format. Furthermore, the material providing unit can also provide the driving diagnosis results via email or a web application. This allows the material providing unit to provide detailed materials for the family meeting.

[0035] The collection unit can analyze the driver's past driving history and select the optimal data collection method. The collection unit, for example, uses data mining technology to analyze the driver's past driving history. For example, the collection unit analyzes past driving data to identify trends in erratic driving during specific time periods. In addition, if driving becomes unstable under specific road conditions, the collection unit can strengthen data collection under those conditions. Furthermore, if driving becomes unstable under specific weather conditions, the collection unit can strengthen data collection under those weather conditions. This allows the collection unit to select the optimal data collection method based on the driver's past driving history.

[0036] When collecting driving data, the collection unit can filter the data based on the driver's current health condition and medication status. The collection unit, for example, uses health checkup results to evaluate the driver's current health condition. For example, the collection unit evaluates the driver's health condition based on the health checkup results and filters the data. The collection unit can also evaluate the health condition based on the driver's self-reporting. For example, if the driver reports poor health, the collection unit filters the data taking into account the impact of that. Furthermore, the collection unit can consider the type of medication being taken and how often it is taken to evaluate the driver's medication status. For example, if the driver is taking medication, the collection unit filters the data taking into account the impact of that medication. This allows the collection unit to filter the data in detail based on the driver's current health condition and medication status.

[0037] When collecting driving data, the collection unit can prioritize collection of highly relevant data taking into account the driver's geographical location information. The collection unit, for example, uses a GPS sensor to collect the driver's geographical location information. For example, the collection unit records vehicle location information in real time and uses it to collect driving data. The collection unit can also prioritize collection of data related to specific driving situations based on the geographical location information. For example, when driving in an urban area, the collection unit prioritizes collection of data on waiting at traffic lights and during traffic jams. Furthermore, when driving on a highway, the collection unit can prioritize collection of data on speed fluctuations and lane changes. This allows the collection unit to collect highly relevant data in detail based on the driver's geographical location information.

[0038] The collection unit can analyze the driver's social media activity and collect related data when collecting driving data. The collection unit can, for example, use natural language processing technology to analyze the driver's social media activity. For example, the collection unit can analyze the driver's social media posts and estimate the driver's emotions and stress level. The collection unit can also adjust the collection of driving data based on the social media activity. For example, if the driver posts that he or she is feeling stressed, the collection unit can collect data taking into account the impact of that. Furthermore, if the driver posts that he or she is tired, the collection unit can collect data taking into account the impact of that. This allows the collection unit to collect related data in detail based on the driver's social media activity.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the driving data. The analysis unit, for example, considers the impact of the data to evaluate the importance of the driving data. For example, if braking reaction time is important, the analysis unit analyzes the data in detail. In addition, if lane change frequency is important, the analysis unit can also analyze the data in detail. Furthermore, if speed fluctuations are important, the analysis unit can analyze the data in detail. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the driving data.

[0040] The analysis unit may apply different analysis algorithms depending on the category of driving data during analysis. For example, the analysis unit may apply different analysis algorithms depending on the category of driving data. For example, the analysis unit may apply an algorithm that evaluates reaction speed to braking reaction time. The analysis unit may also apply an algorithm that evaluates driving stability to lane change frequency. Furthermore, the analysis unit may apply an algorithm that evaluates driving consistency to speed fluctuations. This allows the analysis unit to apply different analysis algorithms in detail depending on the category of driving data.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the collection time of the driving data. For example, the analysis unit takes into account the collection timing of the data to evaluate the collection time of the driving data. For example, the analysis unit prioritizes the analysis of recently collected data. The analysis unit can also prioritize the analysis of data collected during a specific time period. Furthermore, the analysis unit can prioritize the analysis of data collected immediately after a specific event (e.g., an accident). This allows the analysis unit to determine the priority of analysis in detail based on the collection time of the driving data.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the driving data. The analysis unit, for example, considers correlations between data to evaluate the relevance of the driving data. For example, the analysis unit may perform analysis taking into account the relevance between braking reaction time and lane change frequency. The analysis unit may also perform analysis taking into account the relevance between speed fluctuations and driving stability. Furthermore, the analysis unit may also perform analysis taking into account the relevance between driving consistency and reaction speed. This allows the analysis unit to precisely adjust the order of analysis based on the relevance of the driving data.

[0043] When generating a report, the reporting unit can adjust the level of detail of the report based on the importance of the driving data. The reporting unit, for example, considers the impact of the data to evaluate the importance of the driving data. For example, if braking reaction time is important, the reporting unit can report the data in detail. Also, if lane change frequency is important, the reporting unit can report the data in detail. Furthermore, if speed fluctuations are important, the reporting unit can report the data in detail. This allows the reporting unit to adjust the level of detail of the report based on the importance of the driving data.

[0044] The reporting unit can apply different report formats depending on the category of driving data when generating a report. For example, the reporting unit applies different report formats depending on the category of driving data. For example, the reporting unit can apply a report format that evaluates reaction speed to braking reaction time. The reporting unit can also apply a report format that evaluates driving stability to lane change frequency. Furthermore, the reporting unit can apply a report format that evaluates driving consistency to speed fluctuations. In this way, the reporting unit can apply different report formats in detail depending on the category of driving data.

[0045] When generating a report, the reporting unit can determine the priority of the report based on the collection time of the driving data. For example, the reporting unit takes into account the collection timing of the data to evaluate the collection time of the driving data. For example, the reporting unit can prioritize reporting of recently collected data. The reporting unit can also prioritize reporting of data collected during a specific time period. Furthermore, the reporting unit can prioritize reporting of data collected immediately after a specific event (e.g., an accident). This allows the reporting unit to determine the priority of the report in detail based on the collection time of the driving data.

[0046] The reporting unit can adjust the order of reports based on the relevance of driving data when generating a report. The reporting unit, for example, considers correlations between data to evaluate the relevance of driving data. For example, the reporting unit can report taking into account the relevance between braking reaction time and lane change frequency. The reporting unit can also report taking into account the relevance between speed fluctuations and driving stability. Furthermore, the reporting unit can report taking into account the relevance between driving consistency and reaction speed. This allows the reporting unit to fine-tune the order of reports based on the relevance of driving data.

[0047] When generating a notification, the notification unit can adjust the level of detail of the notification based on the importance of the driving data. The notification unit, for example, considers the impact of the data to evaluate the importance of the driving data. For example, if braking reaction time is important, the notification unit notifies the data in detail. Furthermore, if lane change frequency is important, the notification unit can also notify the data in detail. Furthermore, if speed fluctuations are important, the notification unit can also notify the data in detail. This allows the notification unit to adjust the level of detail of the notification based on the importance of the driving data.

[0048] The notification unit may apply different notification formats depending on the category of driving data when generating a notification. For example, the notification unit may apply different notification formats depending on the category of driving data. For example, the notification unit may apply a notification format that evaluates reaction speed to braking reaction time. The notification unit may also apply a notification format that evaluates driving stability to lane change frequency. Furthermore, the notification unit may apply a notification format that evaluates driving consistency to speed fluctuations. This allows the notification unit to apply different notification formats in detail depending on the category of driving data.

[0049] When generating a notification, the notification unit can determine the priority of the notification based on the collection time of the driving data. For example, the notification unit takes into account the collection timing of the data to evaluate the collection time of the driving data. For example, the notification unit can prioritize notifying recently collected data. The notification unit can also prioritize notifying data collected during a specific time period. Furthermore, the notification unit can prioritize notifying data collected immediately after a specific event (e.g., an accident). This allows the notification unit to determine the priority of the notification in detail based on the collection time of the driving data.

[0050] The notification unit may adjust the order of notifications based on the relevance of driving data when generating notifications. For example, the notification unit may consider correlations between data to evaluate the relevance of driving data. For example, the notification unit may provide notifications by considering the relevance between braking reaction time and lane change frequency. The notification unit may also provide notifications by considering the relevance between speed fluctuations and driving stability. Furthermore, the notification unit may provide notifications by considering the relevance between driving consistency and reaction speed. This allows the notification unit to precisely adjust the order of notifications based on the relevance of driving data.

[0051] When providing the data, the data providing unit can adjust the level of detail of the data based on the importance of the driving data. For example, the data providing unit considers the degree of influence of the data to evaluate the importance of the driving data. For example, if braking reaction time is important, the data providing unit can describe the data in detail in the data. Furthermore, if lane change frequency is important, the data providing unit can also describe the data in detail in the data. Furthermore, if speed fluctuations are important, the data providing unit can also describe the data in detail in the data. This allows the data providing unit to adjust the level of detail of the data based on the importance of the driving data.

[0052] The data providing unit may apply different data formats depending on the category of driving data when providing the data. For example, the data providing unit may apply different data formats depending on the category of driving data. For example, the data providing unit may apply a data format for evaluating reaction speed to braking reaction time. The data providing unit may also apply a data format for evaluating driving stability to lane change frequency. Furthermore, the data providing unit may apply a data format for evaluating driving consistency to speed fluctuation. In this way, the data providing unit may apply different data formats in detail depending on the category of driving data.

[0053] When providing the materials, the material providing unit can determine the priority of the materials based on the collection time of the driving data. For example, the material providing unit takes into account the collection timing of the data to evaluate the collection time of the driving data. For example, the material providing unit can prioritize recently collected data in the materials. The material providing unit can also prioritize data collected during a specific time period in the materials. Furthermore, the material providing unit can prioritize data collected immediately after a specific event (e.g., an accident) in the materials. This allows the material providing unit to determine the priority of the materials in detail based on the collection time of the driving data.

[0054] The data providing unit may adjust the order of data based on the relevance of the driving data when providing the data. For example, the data providing unit may consider correlations between data to evaluate the relevance of the driving data. For example, the data providing unit may consider the correlation between braking reaction time and lane change frequency and include it in the data. The data providing unit may also consider the correlation between speed fluctuations and driving stability and include it in the data. Furthermore, the data providing unit may consider the correlation between driving consistency and reaction speed and include it in the data. This allows the data providing unit to precisely adjust the order of data based on the relevance of the driving data.

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

[0056] The driving diagnosis system may further include a health monitoring unit that monitors the driver's health condition. The health monitoring unit may, for example, measure the driver's heart rate and blood pressure in real time and issue a warning if an abnormality is detected. The health monitoring unit may also adjust the collection of driving data based on the driver's health condition. For example, if the driver's heart rate suddenly increases, the health monitoring unit may temporarily suspend the collection of driving data and prompt the driver to take a break. Furthermore, the health monitoring unit may correct the evaluation result of the driving ability based on the driver's health condition. This allows the driving diagnosis system to evaluate the driving ability taking the driver's health condition into consideration.

[0057] The collection unit can collect gaze data of the driver while driving. For example, the collection unit can track the driver's gaze movements using a camera mounted on the vehicle and record the degree of gaze concentration and gaze movement patterns. The collection unit can also evaluate the driver's attention based on the gaze data. For example, if the driver's gaze is frequently averted, it can be determined that the driver's attention is declining. Furthermore, the collection unit can adjust the timing of collecting driving data based on the gaze data. For example, it can prioritize collecting driving data when the driver's gaze is concentrated. This allows the collection unit to collect detailed gaze data of the driver and use it to evaluate driving ability.

[0058] The report unit may have a sharing function for sharing the driving diagnosis results with family members or medical institutions. For example, the report unit may send the driving diagnosis results to family members or medical institutions via email or a dedicated app. The report unit may also store the driving diagnosis results on the cloud so that relevant parties can access them. Furthermore, the report unit may have a function for receiving feedback from family members or medical institutions based on the driving diagnosis results. This allows the report unit to share the driving diagnosis results with relevant parties and encourage them to take appropriate measures.

[0059] The driving diagnosis system may further include a learning unit that learns the driver's driving style. For example, the learning unit may analyze the driver's driving style based on past driving data and identify individual driving patterns. The learning unit may also optimize the method of collecting driving data based on the driver's driving style. For example, the learning unit may adjust the sensitivity of the sensor to suit a specific driving style. Furthermore, the learning unit may customize the evaluation criteria for driving ability based on the driver's driving style. This allows the driving diagnosis system to evaluate driving ability taking into account the driver's individual driving style.

[0060] The collection unit can collect biometric data of the driver while driving. For example, the collection unit can measure the driver's heart rate and electrodermal activity in real time to evaluate the stress level while driving. The collection unit can also estimate the driver's fatigue level based on the biometric data. For example, if the heart rate drops and the electrodermal activity decreases, it can be determined that fatigue is accumulating. Furthermore, the collection unit can adjust the method of collecting driving data based on the biometric data. For example, if fatigue is accumulating, it can increase the frequency of data collection. In this way, the collection unit can collect detailed biometric data of the driver and use it to evaluate driving ability.

[0061] The report unit can provide an interactive dashboard for visually displaying the driving diagnosis results. For example, the report unit can update the driving diagnosis results in real time and display them as graphs or charts. The report unit can also allow the user to select specific data and check the details. Furthermore, the report unit can have a function for displaying recommended actions based on the driving diagnosis results. For example, if driving ability is impaired, a message urging the user to take a break can be displayed. In this way, the report unit can provide the driving diagnosis results in an easy-to-understand visual format and support the user in taking appropriate actions.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection unit collects driving data. The driving data includes speed, frequency of braking, number of lane changes, etc. The collection unit collects driving data using sensors installed in the vehicle, and can also collect data in real time. For example, a GPS sensor can be used to collect vehicle location information and record speed and travel route. In addition, a brake pedal pressure sensor can be used to record frequency of braking. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on the algorithm used and the purpose of the analysis. For example, a machine learning algorithm can be used to analyze driving data and evaluate the driving ability of older drivers. Data mining technology can also be used to extract driving patterns and evaluate driving safety. Furthermore, statistical analysis can be used to identify trends in driving data and evaluate changes in driving ability. Step 3: The reporting unit reports the analysis results obtained by the analysis unit. The report is made based on the report format and details of the report content. For example, the driving diagnosis results may be displayed visually as graphs or charts. The driving diagnosis results may also be reported in detail in text format. Furthermore, the driving diagnosis results may be provided via email or a web application.

[0064] (Example 2) A driving diagnosis system according to an embodiment of the present invention collects driving data from elderly drivers and evaluates the difficulty of safe driving due to declining physical abilities. This driving diagnosis system performs detailed analysis of changes in behavioral patterns and cognitive abilities while driving and reports daily driving diagnoses. For example, it collects data such as braking reaction time, lane change frequency, and speed fluctuations, and evaluates the elderly driver's driving ability based on this data. Furthermore, for elderly drivers who have reached their driving limits, the system also functions as a support tool to encourage them to surrender their driver's licenses in cooperation with local governments. This tool provides support for family members to objectively understand the elderly driver's driving ability and take appropriate measures. This allows the driving diagnosis system to provide a detailed evaluation of the elderly driver's driving ability and encourage appropriate measures.

[0065] A driving diagnosis system according to an embodiment includes a collection unit, an analysis unit, and a report unit. The collection unit collects driving data. The driving data includes, but is not limited to, speed, frequency of brake use, and number of lane changes. The collection unit collects driving data using, for example, a sensor mounted on a vehicle. The collection unit can also collect driving data in real time. For example, the collection unit collects vehicle position information using a GPS sensor and records speed and travel route. The collection unit can also record frequency of brake use using a brake pedal pressure sensor. The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on, for example, but is not limited to, the algorithm used and the purpose of the analysis. For example, the analysis unit can analyze driving data using a machine learning algorithm to evaluate the driving ability of an elderly driver. The analysis unit can also extract driving patterns using data mining technology to evaluate driving safety. The analysis unit can also identify trends in driving data and evaluate changes in driving ability using statistical analysis. The report unit reports the analysis results obtained by the analysis unit. The report is generated based on, for example, a report format and details of the report content, but is not limited to such examples. For example, the report unit visually displays the driving diagnosis results as graphs or charts. The report unit can also report the driving diagnosis results in detail in text format. Furthermore, the report unit can provide the driving diagnosis results via email or a web application. As a result, the driving diagnosis system according to the embodiment can consistently collect, analyze, and report driving data.

[0066] The collection unit can collect driving data such as brake reaction time, lane change frequency, and speed fluctuation. The collection unit, for example, uses a brake pedal pressure sensor to measure brake reaction time. For example, the collection unit measures the time from the moment the brake pedal is pressed until the vehicle stops and records the reaction time. The collection unit can also collect vehicle position information in real time to measure lane change frequency. For example, the collection unit uses a GPS sensor to record vehicle position information and measure the number of lane changes. The collection unit can also use a vehicle speed sensor to measure speed fluctuation. For example, the collection unit records the vehicle speed in real time and measures the range and frequency of speed fluctuations. This allows the collection unit to collect detailed driving data such as brake reaction time, lane change frequency, and speed fluctuations.

[0067] The analysis unit can evaluate the driving ability of drivers over a certain age based on the collected data. The analysis unit can, for example, evaluate the driving ability of an elderly driver based on the collected data. For example, the analysis unit can determine that a driver's driving ability is declining if the driver's braking reaction time is long. The analysis unit can also determine that a driver's driving stability is declining if the driver changes lanes frequently. Furthermore, the analysis unit can determine that a driver's driving consistency is declining if the driver's speed fluctuates greatly. This allows the analysis unit to perform a detailed evaluation of the elderly driver's driving ability based on the collected data.

[0068] The report unit can report the daily driving diagnosis. The report unit visually displays the results of the daily driving diagnosis as graphs or charts, for example. For example, the report unit can display changes in braking reaction time as a line graph, allowing changes in driving ability to be visually understood. The report unit can also display the frequency of lane changes as a bar graph, thereby evaluating driving stability. Furthermore, the report unit can display speed fluctuations as a histogram, thereby evaluating driving consistency. This allows the report unit to report the results of the daily driving diagnosis in detail.

[0069] The driving diagnosis system includes a notification unit that generates a notification recommending the driver's license surrender based on the results of the driving ability evaluation. The notification unit generates the notification recommending the driver's license surrender based on the results of the driving ability evaluation, for example. For example, if the notification unit determines that the driver's driving ability has deteriorated, it generates a message recommending the driver's license surrender. The notification unit can also send the notification recommending the driver's license surrender via email or SMS. Furthermore, the notification unit can provide the notification recommending the driver's license surrender to family members or local governments. This allows the notification unit to generate a detailed notification recommending the driver's license surrender based on the results of the driving ability evaluation.

[0070] The driving diagnosis system includes a material providing unit that provides materials for a family meeting. The material providing unit provides, for example, materials for the family meeting. For example, the material providing unit provides materials that visually display the driving diagnosis results as graphs or charts. The material providing unit can also provide materials that report the driving diagnosis results in detail in text format. Furthermore, the material providing unit can also provide the driving diagnosis results via email or a web application. This allows the material providing unit to provide detailed materials for the family meeting.

[0071] The collection unit can estimate the driver's emotions and adjust the timing of collecting driving data based on the estimated driver's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the driver's emotions. For example, the collection unit uses a camera to capture the driver's facial expressions and estimates the emotions using an emotion estimation algorithm. The collection unit can also estimate the driver's emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the driver's voice to estimate the emotions. Furthermore, the collection unit can estimate the driver's emotions using a biosensor. For example, the collection unit measures the heart rate and electrodermal activity to estimate the emotions. This allows the collection unit to estimate the driver's emotions in detail and adjust the timing of collecting driving data based on the estimated emotions.

[0072] The collection unit can analyze the driver's past driving history and select the optimal data collection method. The collection unit, for example, uses data mining technology to analyze the driver's past driving history. For example, the collection unit analyzes past driving data to identify trends in erratic driving during specific time periods. In addition, if driving becomes unstable under specific road conditions, the collection unit can strengthen data collection under those conditions. Furthermore, if driving becomes unstable under specific weather conditions, the collection unit can strengthen data collection under those weather conditions. This allows the collection unit to select the optimal data collection method based on the driver's past driving history.

[0073] When collecting driving data, the collection unit can filter the data based on the driver's current health condition and medication status. The collection unit, for example, uses health checkup results to evaluate the driver's current health condition. For example, the collection unit evaluates the driver's health condition based on the health checkup results and filters the data. The collection unit can also evaluate the health condition based on the driver's self-reporting. For example, if the driver reports poor health, the collection unit filters the data taking into account the impact of that. Furthermore, the collection unit can consider the type of medication being taken and how often it is taken to evaluate the driver's medication status. For example, if the driver is taking medication, the collection unit filters the data taking into account the impact of that medication. This allows the collection unit to filter the data in detail based on the driver's current health condition and medication status.

[0074] The collection unit can estimate the driver's emotions and determine the priority of driving data to be collected based on the estimated driver's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the driver's emotions. For example, the collection unit captures the driver's facial expressions using a camera and estimates the emotions using an emotion estimation algorithm. The collection unit can also estimate the driver's emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the driver's voice to estimate the emotions. Furthermore, the collection unit can estimate the driver's emotions using a biosensor. For example, the collection unit measures the heart rate and electrodermal activity to estimate the emotions. This allows the collection unit to estimate the driver's emotions in detail and determine the priority of driving data to be collected based on the estimated emotions.

[0075] When collecting driving data, the collection unit can prioritize collection of highly relevant data taking into account the driver's geographical location information. The collection unit, for example, uses a GPS sensor to collect the driver's geographical location information. For example, the collection unit records vehicle location information in real time and uses it to collect driving data. The collection unit can also prioritize collection of data related to specific driving situations based on the geographical location information. For example, when driving in an urban area, the collection unit prioritizes collection of data on waiting at traffic lights and during traffic jams. Furthermore, when driving on a highway, the collection unit can prioritize collection of data on speed fluctuations and lane changes. This allows the collection unit to collect highly relevant data in detail based on the driver's geographical location information.

[0076] The collection unit can analyze the driver's social media activity and collect related data when collecting driving data. The collection unit can, for example, use natural language processing technology to analyze the driver's social media activity. For example, the collection unit can analyze the driver's social media posts and estimate the driver's emotions and stress level. The collection unit can also adjust the collection of driving data based on the social media activity. For example, if the driver posts that he or she is feeling stressed, the collection unit can collect data taking into account the impact of that. Furthermore, if the driver posts that he or she is tired, the collection unit can collect data taking into account the impact of that. This allows the collection unit to collect related data in detail based on the driver's social media activity.

[0077] The analysis unit can estimate the driver's emotions and adjust the way the analysis is presented based on the estimated driver's emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the driver's emotions. For example, the analysis unit uses a camera to capture the driver's facial expressions and estimates the emotions using an emotion estimation algorithm. The analysis unit can also estimate the driver's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the driver's voice to estimate the emotions. Furthermore, the analysis unit can estimate the driver's emotions using a biosensor. For example, the analysis unit measures the heart rate and electrodermal activity to estimate the emotions. This allows the analysis unit to estimate the driver's emotions in detail and adjust the way the analysis is presented based on the estimated emotions.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the driving data. The analysis unit, for example, considers the impact of the data to evaluate the importance of the driving data. For example, if braking reaction time is important, the analysis unit analyzes the data in detail. In addition, if lane change frequency is important, the analysis unit can also analyze the data in detail. Furthermore, if speed fluctuations are important, the analysis unit can analyze the data in detail. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the driving data.

[0079] The analysis unit may apply different analysis algorithms depending on the category of driving data during analysis. For example, the analysis unit may apply different analysis algorithms depending on the category of driving data. For example, the analysis unit may apply an algorithm that evaluates reaction speed to braking reaction time. The analysis unit may also apply an algorithm that evaluates driving stability to lane change frequency. Furthermore, the analysis unit may apply an algorithm that evaluates driving consistency to speed fluctuations. This allows the analysis unit to apply different analysis algorithms in detail depending on the category of driving data.

[0080] The analysis unit can estimate the driver's emotions and adjust the length of the analysis based on the estimated driver's emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the driver's emotions. For example, the analysis unit uses a camera to capture the driver's facial expressions and estimates the emotions using an emotion estimation algorithm. The analysis unit can also estimate the driver's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the driver's voice to estimate the emotions. Furthermore, the analysis unit can estimate the driver's emotions using a biosensor. For example, the analysis unit measures the heart rate and electrodermal activity to estimate the emotions. This allows the analysis unit to estimate the driver's emotions in detail and adjust the length of the analysis based on the estimated emotions.

[0081] During analysis, the analysis unit can determine the priority of analysis based on the collection time of the driving data. For example, the analysis unit takes into account the collection timing of the data to evaluate the collection time of the driving data. For example, the analysis unit prioritizes the analysis of recently collected data. The analysis unit can also prioritize the analysis of data collected during a specific time period. Furthermore, the analysis unit can prioritize the analysis of data collected immediately after a specific event (e.g., an accident). This allows the analysis unit to determine the priority of analysis in detail based on the collection time of the driving data.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the driving data. The analysis unit, for example, considers correlations between data to evaluate the relevance of the driving data. For example, the analysis unit may perform analysis taking into account the relevance between braking reaction time and lane change frequency. The analysis unit may also perform analysis taking into account the relevance between speed fluctuations and driving stability. Furthermore, the analysis unit may also perform analysis taking into account the relevance between driving consistency and reaction speed. This allows the analysis unit to precisely adjust the order of analysis based on the relevance of the driving data.

[0083] The reporting unit can estimate the driver's emotions and adjust the way the report is presented based on the estimated driver's emotions. The reporting unit uses, for example, facial expression recognition technology to estimate the driver's emotions. For example, the reporting unit uses a camera to capture the driver's facial expressions and estimates the emotions using an emotion estimation algorithm. The reporting unit can also estimate the driver's emotions using voice analysis technology. For example, the reporting unit analyzes the tone and speed of the driver's voice to estimate the emotions. Furthermore, the reporting unit can estimate the driver's emotions using a biosensor. For example, the reporting unit measures the heart rate and electrodermal activity to estimate the emotions. This allows the reporting unit to estimate the driver's emotions in detail and adjust the way the report is presented based on the estimated emotions.

[0084] When generating a report, the reporting unit can adjust the level of detail of the report based on the importance of the driving data. The reporting unit, for example, considers the impact of the data to evaluate the importance of the driving data. For example, if braking reaction time is important, the reporting unit can report the data in detail. Also, if lane change frequency is important, the reporting unit can report the data in detail. Furthermore, if speed fluctuations are important, the reporting unit can report the data in detail. This allows the reporting unit to adjust the level of detail of the report based on the importance of the driving data.

[0085] The reporting unit can apply different report formats depending on the category of driving data when generating a report. For example, the reporting unit applies different report formats depending on the category of driving data. For example, the reporting unit can apply a report format that evaluates reaction speed to braking reaction time. The reporting unit can also apply a report format that evaluates driving stability to lane change frequency. Furthermore, the reporting unit can apply a report format that evaluates driving consistency to speed fluctuations. In this way, the reporting unit can apply different report formats in detail depending on the category of driving data.

[0086] The reporting unit can estimate the driver's emotions and adjust the length of the report based on the estimated driver's emotions. The reporting unit can use, for example, facial expression recognition technology to estimate the driver's emotions. For example, the reporting unit can capture the driver's facial expressions using a camera and estimate the emotions using an emotion estimation algorithm. The reporting unit can also estimate the driver's emotions using voice analysis technology. For example, the reporting unit can analyze the tone and speed of the driver's voice to estimate the emotions. Furthermore, the reporting unit can estimate the driver's emotions using a biosensor. For example, the reporting unit can measure the heart rate and electrodermal activity to estimate the emotions. This allows the reporting unit to estimate the driver's emotions in detail and adjust the length of the report based on the estimated emotions.

[0087] When generating a report, the reporting unit can determine the priority of the report based on the collection time of the driving data. For example, the reporting unit takes into account the collection timing of the data to evaluate the collection time of the driving data. For example, the reporting unit can prioritize reporting of recently collected data. The reporting unit can also prioritize reporting of data collected during a specific time period. Furthermore, the reporting unit can prioritize reporting of data collected immediately after a specific event (e.g., an accident). This allows the reporting unit to determine the priority of the report in detail based on the collection time of the driving data.

[0088] The reporting unit can adjust the order of reports based on the relevance of driving data when generating a report. The reporting unit, for example, considers correlations between data to evaluate the relevance of driving data. For example, the reporting unit can report taking into account the relevance between braking reaction time and lane change frequency. The reporting unit can also report taking into account the relevance between speed fluctuations and driving stability. Furthermore, the reporting unit can report taking into account the relevance between driving consistency and reaction speed. This allows the reporting unit to fine-tune the order of reports based on the relevance of driving data.

[0089] The notification unit can estimate the driver's emotion and adjust the notification expression method based on the estimated driver's emotion. The notification unit uses, for example, facial expression recognition technology to estimate the driver's emotion. For example, the notification unit captures the driver's facial expression using a camera and estimates the emotion using an emotion estimation algorithm. The notification unit can also estimate the driver's emotion using voice analysis technology. For example, the notification unit analyzes the tone and speed of the driver's voice to estimate the emotion. Furthermore, the notification unit can estimate the driver's emotion using a biosensor. For example, the notification unit measures the heart rate and electrodermal activity to estimate the emotion. This allows the notification unit to estimate the driver's emotion in detail and adjust the notification expression method based on the estimated emotion.

[0090] When generating a notification, the notification unit can adjust the level of detail of the notification based on the importance of the driving data. The notification unit, for example, considers the impact of the data to evaluate the importance of the driving data. For example, if braking reaction time is important, the notification unit notifies the data in detail. Furthermore, if lane change frequency is important, the notification unit can also notify the data in detail. Furthermore, if speed fluctuations are important, the notification unit can also notify the data in detail. This allows the notification unit to adjust the level of detail of the notification based on the importance of the driving data.

[0091] The notification unit may apply different notification formats depending on the category of driving data when generating a notification. For example, the notification unit may apply different notification formats depending on the category of driving data. For example, the notification unit may apply a notification format that evaluates reaction speed to braking reaction time. The notification unit may also apply a notification format that evaluates driving stability to lane change frequency. Furthermore, the notification unit may apply a notification format that evaluates driving consistency to speed fluctuations. This allows the notification unit to apply different notification formats in detail depending on the category of driving data.

[0092] The notification unit can estimate the driver's emotion and adjust the length of the notification based on the estimated driver's emotion. The notification unit, for example, uses facial expression recognition technology to estimate the driver's emotion. For example, the notification unit uses a camera to capture the driver's facial expression and estimates the emotion using an emotion estimation algorithm. The notification unit can also estimate the driver's emotion using voice analysis technology. For example, the notification unit analyzes the tone and speed of the driver's voice to estimate the emotion. Furthermore, the notification unit can estimate the driver's emotion using a biosensor. For example, the notification unit measures the heart rate and electrodermal activity to estimate the emotion. This allows the notification unit to estimate the driver's emotion in detail and adjust the length of the notification based on the estimated emotion.

[0093] When generating a notification, the notification unit can determine the priority of the notification based on the collection time of the driving data. For example, the notification unit takes into account the collection timing of the data to evaluate the collection time of the driving data. For example, the notification unit can prioritize notifying recently collected data. The notification unit can also prioritize notifying data collected during a specific time period. Furthermore, the notification unit can prioritize notifying data collected immediately after a specific event (e.g., an accident). This allows the notification unit to determine the priority of the notification in detail based on the collection time of the driving data.

[0094] The notification unit may adjust the order of notifications based on the relevance of driving data when generating notifications. For example, the notification unit may consider correlations between data to evaluate the relevance of driving data. For example, the notification unit may provide notifications by considering the relevance between braking reaction time and lane change frequency. The notification unit may also provide notifications by considering the relevance between speed fluctuations and driving stability. Furthermore, the notification unit may provide notifications by considering the relevance between driving consistency and reaction speed. This allows the notification unit to precisely adjust the order of notifications based on the relevance of driving data.

[0095] The information providing unit can estimate the driver's emotions and adjust the presentation method of the materials based on the estimated driver's emotions. The information providing unit, for example, uses facial expression recognition technology to estimate the driver's emotions. For example, the information providing unit captures the driver's facial expressions using a camera and estimates the emotions using an emotion estimation algorithm. The information providing unit can also estimate the driver's emotions using voice analysis technology. For example, the information providing unit analyzes the tone and speed of the driver's voice to estimate the emotions. Furthermore, the information providing unit can estimate the driver's emotions using a biosensor. For example, the information providing unit measures the heart rate and electrodermal activity to estimate the emotions. This allows the information providing unit to estimate the driver's emotions in detail and adjust the presentation method of the materials based on the estimated emotions.

[0096] When providing the data, the data providing unit can adjust the level of detail of the data based on the importance of the driving data. For example, the data providing unit considers the degree of influence of the data to evaluate the importance of the driving data. For example, if braking reaction time is important, the data providing unit can describe the data in detail in the data. Furthermore, if lane change frequency is important, the data providing unit can also describe the data in detail in the data. Furthermore, if speed fluctuations are important, the data providing unit can also describe the data in detail in the data. This allows the data providing unit to adjust the level of detail of the data based on the importance of the driving data.

[0097] The data providing unit may apply different data formats depending on the category of driving data when providing the data. For example, the data providing unit may apply different data formats depending on the category of driving data. For example, the data providing unit may apply a data format for evaluating reaction speed to braking reaction time. The data providing unit may also apply a data format for evaluating driving stability to lane change frequency. Furthermore, the data providing unit may apply a data format for evaluating driving consistency to speed fluctuation. In this way, the data providing unit may apply different data formats in detail depending on the category of driving data.

[0098] The information providing unit can estimate the driver's emotions and adjust the length of the information based on the estimated driver's emotions. The information providing unit, for example, uses facial expression recognition technology to estimate the driver's emotions. For example, the information providing unit captures the driver's facial expressions using a camera and estimates the emotions using an emotion estimation algorithm. The information providing unit can also estimate the driver's emotions using voice analysis technology. For example, the information providing unit analyzes the tone and speed of the driver's voice to estimate the emotions. Furthermore, the information providing unit can estimate the driver's emotions using a biosensor. For example, the information providing unit measures the heart rate and electrodermal activity to estimate the emotions. This allows the information providing unit to estimate the driver's emotions in detail and adjust the length of the information based on the estimated emotions.

[0099] When providing the materials, the material providing unit can determine the priority of the materials based on the collection time of the driving data. For example, the material providing unit takes into account the collection timing of the data to evaluate the collection time of the driving data. For example, the material providing unit can prioritize recently collected data in the materials. The material providing unit can also prioritize data collected during a specific time period in the materials. Furthermore, the material providing unit can prioritize data collected immediately after a specific event (e.g., an accident) in the materials. This allows the material providing unit to determine the priority of the materials in detail based on the collection time of the driving data.

[0100] The data providing unit may adjust the order of data based on the relevance of the driving data when providing the data. For example, the data providing unit may consider correlations between data to evaluate the relevance of the driving data. For example, the data providing unit may consider the correlation between braking reaction time and lane change frequency and include it in the data. The data providing unit may also consider the correlation between speed fluctuations and driving stability and include it in the data. Furthermore, the data providing unit may consider the correlation between driving consistency and reaction speed and include it in the data. This allows the data providing unit to precisely adjust the order of data based on the relevance of the driving data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, report unit, notification unit, and material provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects driving data using a sensor of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The report unit is realized by the specific processing unit 290 of the data processing device 12 and visually displays the analysis results. The notification unit is realized by the specific processing unit 290 of the data processing device 12 and generates a notification recommending the driver's license surrender. The material provision unit is realized by the control unit 46A of the smart device 14 and provides materials for a family meeting. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, report unit, notification unit, and material provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects driving data using a sensor in the smart glasses 214, and the data is analyzed by the specific processing unit 290 in the data processing device 12. The analysis unit is realized by the specific processing unit 290 in the data processing device 12 and analyzes the collected data. The report unit is realized by the specific processing unit 290 in the data processing device 12 and visually displays the analysis results. The notification unit is realized by the specific processing unit 290 in the data processing device 12 and generates a notification recommending driver's license surrender. The material provision unit is realized by the control unit 46A of the smart glasses 214 and provides materials for a family meeting. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, report unit, notification unit, and material provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects driving data using a sensor of the headset type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The report unit is realized by the specific processing unit 290 of the data processing device 12 and visually displays the analysis results. The notification unit is realized by the specific processing unit 290 of the data processing device 12 and generates a notification recommending the driver's license surrender. The material provision unit is realized by the control unit 46A of the headset type terminal 314 and provides materials for a family meeting. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, report unit, notification unit, and material provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects driving data using sensors of the robot 414, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The report unit is realized by the specific processing unit 290 of the data processing device 12 and visually displays the analysis results. The notification unit is realized by the specific processing unit 290 of the data processing device 12 and generates a notification recommending the driver's license surrender. The material provision unit is realized by the control unit 46A of the robot 414 and provides materials for a family meeting.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The driving diagnosis system may further include a health monitoring unit that monitors the driver's health condition. The health monitoring unit may, for example, measure the driver's heart rate and blood pressure in real time and issue a warning if an abnormality is detected. The health monitoring unit may also adjust the collection of driving data based on the driver's health condition. For example, if the driver's heart rate suddenly increases, the health monitoring unit may temporarily suspend the collection of driving data and prompt the driver to take a break. Furthermore, the health monitoring unit may correct the evaluation result of the driving ability based on the driver's health condition. This allows the driving diagnosis system to evaluate the driving ability taking the driver's health condition into consideration.

[0103] The collection unit can collect gaze data of the driver while driving. For example, the collection unit can track the driver's gaze movements using a camera mounted on the vehicle and record the degree of gaze concentration and gaze movement patterns. The collection unit can also evaluate the driver's attention based on the gaze data. For example, if the driver's gaze is frequently averted, it can be determined that the driver's attention is declining. Furthermore, the collection unit can adjust the timing of collecting driving data based on the gaze data. For example, it can prioritize collecting driving data when the driver's gaze is concentrated. This allows the collection unit to collect detailed gaze data of the driver and use it to evaluate driving ability.

[0104] The analysis unit can analyze voice data of the driver while driving. For example, the analysis unit can use an in-vehicle microphone to collect the driver's speech and tone of voice and estimate their emotions and stress level. The analysis unit can also evaluate the driver's driving ability based on the voice data. For example, if the stress level is high, it can determine that the driver's driving ability is declining. Furthermore, the analysis unit can correct the analysis results of the driving data based on the voice data. For example, if the stress level is high, it can relax the evaluation criteria for the driving data. This allows the analysis unit to analyze the driver's voice data in detail and use it to evaluate their driving ability.

[0105] The report unit may have a sharing function for sharing the driving diagnosis results with family members or medical institutions. For example, the report unit may send the driving diagnosis results to family members or medical institutions via email or a dedicated app. The report unit may also store the driving diagnosis results on the cloud so that relevant parties can access them. Furthermore, the report unit may have a function for receiving feedback from family members or medical institutions based on the driving diagnosis results. This allows the report unit to share the driving diagnosis results with relevant parties and encourage them to take appropriate measures.

[0106] The notification unit can estimate the driver's emotions while driving and adjust the content of the notification based on the estimated emotions. For example, if the driver is feeling stressed, the notification unit can send a message encouraging the driver to relax. Also, if the driver is tired, the notification unit can send a message encouraging the driver to take a break. Furthermore, the notification unit can adjust the timing of the notification based on the driver's emotions. For example, the notification unit can refrain from sending notifications when the driver is concentrating and send notifications when the driver is relaxed. This allows the notification unit to provide appropriate notifications taking the driver's emotions into consideration.

[0107] The driving diagnosis system may further include a learning unit that learns the driver's driving style. For example, the learning unit may analyze the driver's driving style based on past driving data and identify individual driving patterns. The learning unit may also optimize the method of collecting driving data based on the driver's driving style. For example, the learning unit may adjust the sensitivity of the sensor to suit a specific driving style. Furthermore, the learning unit may customize the evaluation criteria for driving ability based on the driver's driving style. This allows the driving diagnosis system to evaluate driving ability taking into account the driver's individual driving style.

[0108] The collection unit can estimate the driver's emotions and adjust the method of collecting driving data based on the estimated emotions. For example, the collection unit can collect normal data when the driver is relaxed, but can increase the frequency of data collection when the driver is stressed. The collection unit can also prioritize collection of specific data based on the driver's emotions. For example, when the driver is stressed, the collection unit can prioritize collection of brake use frequency and speed fluctuations. Furthermore, the collection unit can adjust the timing of data collection based on the driver's emotions. This allows the collection unit to collect driving data taking the driver's emotions into consideration.

[0109] The collection unit can collect biometric data of the driver while driving. For example, the collection unit can measure the driver's heart rate and electrodermal activity in real time to evaluate the stress level while driving. The collection unit can also estimate the driver's fatigue level based on the biometric data. For example, if the heart rate drops and the electrodermal activity decreases, it can be determined that fatigue is accumulating. Furthermore, the collection unit can adjust the method of collecting driving data based on the biometric data. For example, if fatigue is accumulating, it can increase the frequency of data collection. In this way, the collection unit can collect detailed biometric data of the driver and use it to evaluate driving ability.

[0110] The analysis unit can analyze the brain wave data of the driver while driving. For example, the analysis unit can measure the driver's brain waves in real time using an brain wave sensor and evaluate the driver's concentration level and fatigue level. The analysis unit can also evaluate the driver's driving ability based on the brain wave data. For example, if the driver's concentration level is declining, it can determine that the driver's driving ability is declining. Furthermore, the analysis unit can correct the analysis results of the driving data based on the brain wave data. For example, if the driver's fatigue level is high, it can relax the evaluation criteria for the driving data. This allows the analysis unit to analyze the driver's brain wave data in detail and use it to evaluate their driving ability.

[0111] The report unit can provide an interactive dashboard for visually displaying the driving diagnosis results. For example, the report unit can update the driving diagnosis results in real time and display them as graphs or charts. The report unit can also allow the user to select specific data and check the details. Furthermore, the report unit can have a function for displaying recommended actions based on the driving diagnosis results. For example, if driving ability is impaired, a message urging the user to take a break can be displayed. In this way, the report unit can provide the driving diagnosis results in an easy-to-understand visual format and support the user in taking appropriate actions.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The collection unit collects driving data. The driving data includes speed, frequency of braking, number of lane changes, etc. The collection unit collects driving data using sensors installed in the vehicle, and can also collect data in real time. For example, a GPS sensor can be used to collect vehicle location information and record speed and travel route. In addition, a brake pedal pressure sensor can be used to record frequency of braking. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on the algorithm used and the purpose of the analysis. For example, a machine learning algorithm can be used to analyze driving data and evaluate the driving ability of older drivers. Data mining technology can also be used to extract driving patterns and evaluate driving safety. Furthermore, statistical analysis can be used to identify trends in driving data and evaluate changes in driving ability. Step 3: The reporting unit reports the analysis results obtained by the analysis unit. The report is made based on the report format and details of the report content. For example, the driving diagnosis results may be displayed visually as graphs or charts. The driving diagnosis results may also be reported in detail in text format. Furthermore, the driving diagnosis results may be provided via email or a web application.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] 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.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects driving data; an analysis unit that analyzes the data collected by the collection unit; a report unit that reports the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect driving data on braking reaction times, lane change frequency, and speed fluctuations 2. The system of claim 1.

3. The analysis unit Evaluate the driving ability of drivers over a certain age based on collected data 2. The system of claim 1.

4. The report unit Report daily driving diagnostics 2. The system of claim 1.

5. A notification unit is provided that generates a notification recommending the driver to surrender his / her license based on the results of the evaluation of the driver's driving ability.

2. The system of claim 1.

6. Have a resource section that provides materials for family meetings.

2. The system of claim 1.

7. The collecting unit Estimate the driver's emotions and adjust the timing of driving data collection based on the estimated driver emotions.

2. The system of claim 1.

8. The collecting unit Analyze the driver's past driving history and select the optimal data collection method 2. The system of claim 1.

9. The collecting unit As driving data is collected, it will be filtered based on the driver's current health and medication status.

2. The system of claim 1.

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