system

Abstract

The system according to this embodiment aims to provide prompt and appropriate responses to the occurrence of an abnormality in an autonomous vehicle and to provide necessary information to the passengers. [Solution] The system according to the embodiment comprises a control unit, an alert unit, and a communication unit. The control unit controls the autonomous vehicle. The alert unit transmits an alert when an abnormality occurs. The communication unit provides information to the passengers.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to respond quickly and appropriately when an abnormality occurs in an autonomous vehicle, and the information provided to passengers is insufficient.

[0005] The system according to the embodiment aims to respond quickly and appropriately when an abnormality occurs in an autonomous vehicle and provide necessary information to passengers.

Means for Solving the Problems

[0006] The system according to the embodiment includes a control unit, an alert unit, and a communication unit. The control unit controls the autonomous vehicle. The alert unit transmits an alert when an abnormality occurs. The communication unit provides information to passengers.

Effects of the Invention

[0007] The system according to this embodiment can respond quickly and appropriately when an abnormality occurs in an autonomous vehicle and provide necessary information to the passengers. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The autonomous driving support system according to an embodiment of the present invention is a system that supports safe and comfortable travel for the elderly by linking an autonomous vehicle with a generative AI. This autonomous driving support system targets elderly people who have surrendered their driver's licenses, people with disabilities, and others who have difficulty driving themselves. By fusing autonomous driving technology with generative AI, it provides information on recommended spots for travel and leisure, optimal route guidance, and responses to abnormal situations. This allows people to continue their lives as before using their own cars even after surrendering their driver's licenses. For example, an AI agent controls the autonomous vehicle. For example, it calculates the optimal route to the destination and gives instructions to the autonomous vehicle. Next, in the event of an abnormality, the generative AI sends an alert to notify the passenger. For example, if an abnormality such as a vehicle malfunction or traffic accident occurs, the generative AI immediately sends an alert to prompt appropriate action. Furthermore, the generative AI supports communication with the passenger. For example, it provides information such as the estimated time of arrival and local sightseeing information. This allows passengers to enjoy their travel with peace of mind. In addition, the generative AI provides information on recommended spots for travel and leisure, and makes suggestions tailored to the passenger's interests and preferences. This system will enable a society where the elderly and people with disabilities can move freely and safely, improving their freedom to go out and their quality of life. For example, they will be able to enjoy travel and leisure activities, providing them with new meaning in life. In this way, the autonomous driving support system can support the elderly and people with disabilities so that they can move safely and comfortably.

[0029] The autonomous driving support system according to this embodiment comprises a control unit, an alert unit, and a communication unit. The control unit controls the autonomous vehicle. For example, the control unit calculates the optimal route to the destination and issues instructions to the autonomous vehicle. For example, the control unit can use AI to collect real-time traffic information and calculate the optimal route. The control unit can also use AI to consider weather information and select a safe route. Furthermore, the control unit can use AI to monitor the health status of the passengers and adjust the driving as needed. The alert unit sends an alert when an abnormality occurs. For example, the alert unit sends an alert when an abnormality occurs, such as a vehicle malfunction or a traffic accident. For example, the alert unit can use AI to identify the type of abnormality and send an appropriate alert. Furthermore, the alert unit can use AI to identify the location of the abnormality and prompt a quick response. Furthermore, the alert unit can use AI to analyze the cause of the abnormality and propose measures to prevent recurrence. The communication unit provides information to the passengers. For example, the communication unit provides information such as the estimated time of arrival and local sightseeing information. For example, the communication unit can use AI to provide information based on the passengers' interests and preferences. Furthermore, the communication unit can use AI to estimate the passenger's emotions and provide appropriate information. In addition, the communication unit can use AI to analyze the passenger's past behavioral history and provide individually customized information. As a result, the autonomous driving support system according to this embodiment can control the autonomous vehicle, send alerts in case of abnormalities, and provide information to the passenger.

[0030] The control unit controls the autonomous vehicle. For example, the control unit calculates the optimal route to the destination and issues instructions to the autonomous vehicle. For example, the control unit can use AI to collect real-time traffic information and calculate the optimal route. Specifically, the AI ​​utilizes traffic sensors, cameras, and GPS data to grasp the current traffic situation in real time. This allows it to immediately reflect information such as congestion and accidents and select the optimal route. The control unit can also use AI to consider weather information and select a safe route. For example, it can analyze weather data and select a route that avoids bad weather such as rain, snow, and fog. Furthermore, the control unit can use AI to monitor the health of the passengers and adjust driving as needed. For example, it can detect the passengers' heart rate, body temperature, and respiratory status with sensors and, if there is an abnormality, it can reduce the driving speed or instruct the vehicle to head to the nearest medical facility. In this way, the control unit can comprehensively consider traffic conditions, weather, and the health of the passengers to achieve safe and efficient driving. Furthermore, the control unit can learn from past driving data and traffic patterns to make future predictions. For example, by predicting fluctuations in traffic volume during specific times of day or on specific days of the week and adjusting the route in advance, congestion can be avoided. This allows the control unit to consistently provide optimal driving, improving passenger comfort and safety.

[0031] The alert unit sends an alert when an abnormality occurs. For example, it sends an alert when an abnormality occurs, such as a vehicle breakdown or a traffic accident. The alert unit can, for example, use AI to identify the type of abnormality and send an appropriate alert. Specifically, it monitors data obtained from various sensors in the vehicle in real time and detects abnormal vibrations, temperature increases, unusual noises, etc. This allows it to identify specific abnormalities such as engine failure or a flat tire and send an appropriate alert. The alert unit can also use AI to pinpoint the location of the abnormality and prompt a quick response. For example, it can use GPS data to pinpoint the exact location where the abnormality occurred and notify the nearest repair shop or emergency service. Furthermore, the alert unit can use AI to analyze the cause of the abnormality and propose measures to prevent recurrence. For example, it can analyze abnormality patterns based on past data and propose a maintenance schedule tailored to the deterioration and usage of specific parts. This enables the alert unit to achieve early detection of abnormalities and a quick response, improving vehicle safety and reliability. In addition, the alert unit records the history of responses to abnormalities, which can be used for future improvements. For example, by analyzing past data on handling anomalies, it is possible to speed up and streamline responses. This allows the alert unit to always provide appropriate alerts based on the latest information, supporting the safe operation of vehicles.

[0032] The communications department provides information to passengers. For example, it provides information such as estimated arrival times and local sightseeing information. The communications department can also use AI to provide information based on passengers' interests and preferences. Specifically, it can analyze passengers' past search history and behavioral patterns to provide information on tourist destinations, restaurants, and events that may be of interest to them. Furthermore, the communications department can use AI to estimate passengers' emotions and provide appropriate information. For example, it can analyze passengers' facial expressions and tone of voice to suggest relaxation music if they want to relax, or provide information to calm them down if they are excited. In addition, the communications department can use AI to analyze passengers' past behavioral history and provide individually customized information. For example, based on places visited in the past and services used, it can provide information to encourage repeat visits or new suggestions. In this way, the communications department can provide personalized information to passengers and offer a comfortable and fulfilling travel experience. Moreover, the communications department can continuously update information in real time through dialogue with passengers. For example, if a passenger makes a new request, it can respond on the spot and provide the latest information. This allows the communications department to consistently provide information tailored to passengers' needs, thereby increasing their satisfaction during their journey.

[0033] The control unit can calculate the optimal route to the destination and issue instructions to the autonomous vehicle. For example, the control unit can use AI to collect real-time traffic information and calculate the optimal route. The control unit can also use AI to consider weather information and select a safe route. The control unit can also use AI to monitor the health status of passengers and adjust driving as needed. This enables efficient travel by calculating the optimal route to the destination and issuing instructions to the autonomous vehicle. The optimal route includes, but is not limited to, distance, time, and traffic conditions. Some or all of the above-described processes in the control unit may be performed using AI or not. For example, the control unit can input real-time traffic information into a generating AI and have the generating AI calculate the optimal route.

[0034] The alert unit can send alerts when an anomaly occurs, such as a vehicle breakdown or a traffic accident. The alert unit can, for example, use AI to identify the type of anomaly and send an appropriate alert. The alert unit can also, for example, use AI to pinpoint the location of the anomaly and prompt a quick response. The alert unit can also, for example, use AI to analyze the cause of the anomaly and propose measures to prevent recurrence. This enables a quick response by sending an alert when an anomaly occurs. Anomalies include, but are not limited to, vehicle breakdowns, traffic accidents, and system errors. Some or all of the above-described processes in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input the type of anomaly into a generating AI and have the generating AI send an appropriate alert.

[0035] The communications department can provide information such as estimated arrival times and local sightseeing information. The communications department can, for example, use AI to provide information based on the passenger's interests and preferences. The communications department can also, for example, use AI to estimate the passenger's emotions and provide appropriate information. The communications department can also, for example, use AI to analyze the passenger's past behavioral history and provide individually customized information. This improves passenger convenience by providing information such as estimated arrival times and local sightseeing information. The information includes, but is not limited to, estimated arrival times, sightseeing information, text, audio, and visual information. Some or all of the above processing in the communications department may be performed using, for example, AI, or not using AI. For example, the communications department can input the passenger's interests and preferences into a generating AI and have the generating AI provide appropriate information.

[0036] The communications department can provide information on recommended spots for travel and leisure. For example, the communications department can use AI to provide information based on the passenger's interests and preferences. The communications department can also use AI to estimate the passenger's emotions and provide appropriate information. The communications department can also use AI to analyze the passenger's past behavioral history and provide individually customized information. This enhances the passenger's enjoyment by providing information on recommended spots for travel and leisure. The information includes, but is not limited to, travel spots, leisure spots, text, audio, and visual information. Some or all of the above processing in the communications department may be performed using AI or not. For example, the communications department can input the passenger's interests and preferences into a generating AI and have the generating AI provide appropriate information.

[0037] The control unit can analyze past driving data and learn and apply the optimal driving pattern. For example, the control unit can learn and apply the driving style preferred by the passenger from past driving data. The control unit can also apply the optimal driving pattern according to specific time periods or weather conditions based on past driving data. For example, the control unit can analyze past driving data and learn and apply a fuel-efficient driving pattern. This enables efficient driving by analyzing past driving data and learning and applying the optimal driving pattern. The optimal driving pattern includes, but is not limited to, fuel efficiency, safety, and comfort. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input past driving data into a generating AI and have the generating AI learn and apply the optimal driving pattern.

[0038] The control unit can recalculate the route in real time according to weather and traffic conditions and provide the optimal route. For example, based on real-time weather information, the control unit can select a route that is less slippery in rainy weather. For example, based on real-time traffic congestion information, the control unit can also select a route that avoids congestion. For example, based on real-time road construction information, the control unit can also select a detour route. This enables efficient travel by recalculating the route in real time according to weather and traffic conditions and providing the optimal route. The optimal route includes, but is not limited to, distance, time, traffic conditions, and weather. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input real-time weather and traffic information into a generating AI and have the generating AI perform the recalculation of the optimal route.

[0039] The control unit can monitor the passenger's health condition and adjust the driving as needed. For example, the control unit can monitor the passenger's heart rate and blood pressure and stop driving if an abnormality is detected. For example, if the passenger is feeling fatigued, the control unit can have the AI ​​suggest a rest and temporarily suspend driving. For example, the control unit can adjust the driving speed and route according to the passenger's health condition. This ensures safe travel by monitoring the passenger's health condition and adjusting driving as needed. Health conditions include, but are not limited to, heart rate, blood pressure, and body temperature. Some or all of the above processing in the control unit may be performed using, for example, AI, or without AI. For example, the control unit can input the passenger's health data into a generating AI and have the generating AI perform driving adjustments.

[0040] The control unit can communicate with other autonomous vehicles and cooperate to perform optimal driving. For example, the control unit can communicate with other autonomous vehicles and coordinate to adjust the route to avoid traffic congestion. The control unit can also communicate with other autonomous vehicles and adjust the speed to maintain a safe distance between vehicles. The control unit can also communicate with other autonomous vehicles and cooperate to secure an evacuation route in an emergency. This enables efficient and safe driving by communicating with other autonomous vehicles and performing optimal driving in cooperation. Communication with other autonomous vehicles includes, but is not limited to, V2V communication, Wi-Fi, and 5G. Some or all of the above processing in the control unit may be performed using, for example, AI, or not using AI. For example, the control unit can input communication data with other autonomous vehicles into a generating AI and have the generating AI perform adjustments for cooperative driving.

[0041] The alert unit can propose the optimal response when an anomaly occurs by referring to past data. For example, the alert unit can propose the optimal response based on past anomaly occurrence data. The alert unit can also propose a quick response by analyzing past anomaly occurrence data. The alert unit can also propose a response to similar anomalies by referring to past anomaly occurrence data. This enables a quick and appropriate response by proposing the optimal response when an anomaly occurs by referring to past data. Anomalies include, but are not limited to, vehicle breakdowns, traffic accidents, and system errors. Optimal response measures include, but are not limited to, repair procedures, evacuation routes, and contact information. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input past anomaly occurrence data into a generating AI and have the generating AI propose an optimal response.

[0042] The alert unit can select different alert methods (voice, visual, vibration, etc.) depending on the type of anomaly. For example, in the event of a vehicle malfunction, the alert unit prioritizes voice alerts to notify the occupants. For example, in the event of a traffic accident, the alert unit can also prioritize visual alerts to notify the occupants. For example, in an emergency, the alert unit can also prioritize vibration alerts to notify the occupants. This allows for more effective alerts by selecting different alert methods depending on the type of anomaly. Anomalies include, but are not limited to, vehicle malfunctions, traffic accidents, and system errors. Alert methods include, but are not limited to, voice alerts, visual alerts, and vibration alerts. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input the type of anomaly into a generating AI and have the generating AI select an appropriate alert method.

[0043] The alert unit can propose the optimal evacuation route based on surrounding traffic conditions when an anomaly occurs. For example, the alert unit can propose the optimal evacuation route based on surrounding traffic congestion information. The alert unit can also propose a detour route based on surrounding road construction information. The alert unit can also propose a safe evacuation route based on surrounding traffic accident information. This enables safe evacuation by proposing the optimal evacuation route based on surrounding traffic conditions when an anomaly occurs. Anomalies include, but are not limited to, vehicle breakdowns, traffic accidents, and system errors. Optimal evacuation routes include, but are not limited to, distance, time, and traffic conditions. Some or all of the above processing in the alert unit may be performed using, for example, AI, or without AI. For example, the alert unit can input surrounding traffic condition data into a generating AI and have the generating AI propose the optimal evacuation route.

[0044] The alert unit can be equipped with a function to automatically send notifications to emergency contacts when an anomaly occurs. For example, the alert unit can automatically send notifications to emergency contacts when a vehicle breaks down. The alert unit can also automatically send notifications to emergency contacts when a traffic accident occurs. The alert unit can also automatically send notifications to emergency contacts in an emergency to encourage a quick response. This enables a quick response by automatically sending notifications to emergency contacts when an anomaly occurs. Anomalies include, but are not limited to, vehicle breakdowns, traffic accidents, and system errors. Emergency contacts include, but are not limited to, family members, ambulance services, and the police. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input the type of anomaly into a generating AI and have the generating AI execute the notification to the emergency contacts.

[0045] The communications department can analyze passengers' past behavioral history and provide individually customized information. For example, the communications department can provide relevant tourist information based on places the passenger has visited in the past. For example, the communications department can also provide information tailored to the passenger's interests based on their past behavioral history. For example, the communications department can analyze the passenger's past behavioral history and propose the optimal travel plan. This allows for more personalized information to be provided by analyzing the passenger's past behavioral history and providing individually customized information. Behavioral history includes, but is not limited to, past travel history and service usage history. Customized information includes, but is not limited to, individual recommended spots and personalized services. Some or all of the above processing in the communications department may be performed using, for example, AI, or not using AI. For example, the communications department can input passengers' past behavioral history data into a generating AI and have the generating AI perform the task of providing individually customized information.

[0046] The communications department can provide relevant information in real time based on the passenger's interests. For example, the communications department can provide information in real time about tourist spots that the passenger has shown interest in. For example, the communications department can also provide information on local events in real time based on the passenger's interests. For example, the communications department can also provide information on restaurants and cafes in real time according to the passenger's interests. This allows for the provision of more appropriate information by providing relevant information in real time based on the passenger's interests. Interests include, but are not limited to, past behavioral history and survey results. Relevant information includes, but is not limited to, event information and restaurant information. Some or all of the above processing in the communications department may be performed using, for example, AI, or not using AI. For example, the communications department can input passenger interest data into a generating AI and have the generating AI provide relevant information in real time.

[0047] The communications department can monitor the health status of passengers and provide health-related information as needed. For example, the communications department can monitor the passenger's heart rate and blood pressure and provide health-related information if an abnormality is detected. For example, if a passenger is feeling fatigued, the communications department can have the AI ​​suggest a rest and provide health-related information. For example, the communications department can also provide information on appropriate exercise and diet according to the passenger's health status. This makes health management easier by monitoring the passenger's health status and providing health-related information as needed. Health status includes, but is not limited to, heart rate, blood pressure, and body temperature. Health-related information includes, but is not limited to, health advice and information on medical institutions. Some or all of the above processing in the communications department may be performed using, for example, AI, or not using AI. For example, the communications department can input passenger health data into a generating AI and have the generating AI provide health-related information.

[0048] The communications department can collaborate with other services to provide comprehensive support. For example, the communications department can automatically make reservations at restaurants that passengers are interested in. For example, the communications department can automatically purchase tickets for events that passengers wish to attend. For example, the communications department can automatically make reservations for accommodations that match the passenger's travel plans. In this way, by collaborating with other services, comprehensive support is provided to passengers. Other services include, but are not limited to, restaurant reservations and event ticket purchases. Comprehensive support includes, but is not limited to, travel plan suggestions and emergency response. Some or all of the above processes in the communications department may be performed using, for example, AI, or not using AI. For example, the communications department can input data from collaboration with other services into a generating AI and have the generating AI perform the provision of comprehensive support.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The control unit can analyze past driving data, learn, and apply optimal driving patterns. For example, it can learn and apply the driving style preferred by passengers from past driving data. Based on past driving data, it can also apply optimal driving patterns according to specific time periods or weather conditions. It can also analyze past driving data, learn, and apply fuel-efficient driving patterns. As a result, efficient driving becomes possible by analyzing past driving data, learning, and applying optimal driving patterns. Optimal driving patterns include, but are not limited to, fuel efficiency, safety, and comfort. Some or all of the above-described processes in the control unit may be performed using AI or not. For example, the control unit can input past driving data into a generating AI and have the generating AI perform the learning and application of optimal driving patterns.

[0051] The control unit can recalculate routes in real time according to weather and traffic conditions and provide the optimal route. For example, based on real-time weather information, it can select a route that is less slippery in rainy weather. Based on real-time traffic congestion information, it can also select a route that avoids congestion. Based on real-time road construction information, it can also select a detour route. This enables efficient travel by recalculating routes in real time according to weather and traffic conditions and providing the optimal route. The optimal route includes, but is not limited to, distance, time, traffic conditions, and weather. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input real-time weather and traffic information into a generating AI and have the generating AI perform the recalculation of the optimal route.

[0052] The alert unit can propose the optimal response when an anomaly occurs by referring to past data. For example, it can propose the optimal response based on past anomaly occurrence data. It can also analyze past anomaly occurrence data and propose a quick response. It can also refer to past anomaly occurrence data and propose a response to a similar anomaly. This enables a quick and appropriate response by referring to past data and proposing the optimal response when an anomaly occurs. Anomalies include, but are not limited to, vehicle breakdowns, traffic accidents, and system errors. Optimal response measures include, but are not limited to, repair procedures, evacuation routes, and contact information. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input past anomaly occurrence data into a generating AI and have the generating AI propose the optimal response.

[0053] The control unit can communicate with other autonomous vehicles and cooperate to perform optimal driving. For example, it can communicate with other autonomous vehicles to coordinate route adjustments to avoid traffic congestion. It can also communicate with other autonomous vehicles to adjust speed to maintain a safe distance between vehicles. It can also communicate with other autonomous vehicles to cooperate in securing evacuation routes in emergencies. This enables efficient and safe driving by communicating with other autonomous vehicles and performing optimal driving in cooperation. Communication with other autonomous vehicles includes, but is not limited to, V2V communication, Wi-Fi, and 5G. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input communication data with other autonomous vehicles into a generating AI and have the generating AI perform adjustments for cooperative driving.

[0054] The alert unit can select different alert methods (voice, visual, vibration, etc.) depending on the type of anomaly. For example, in the event of a vehicle malfunction, a voice alert will be prioritized to notify the occupants. In the event of a traffic accident, a visual alert may be prioritized to notify the occupants. In an emergency, a vibration alert may be prioritized to notify the occupants. This allows for more effective alerts by selecting different alert methods depending on the type of anomaly. Anomalies include, but are not limited to, vehicle malfunctions, traffic accidents, and system errors. Alert methods include, but are not limited to, voice alerts, visual alerts, and vibration alerts. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input the type of anomaly into a generating AI and have the generating AI select an appropriate alert method.

[0055] The communications department can collaborate with other services to provide comprehensive support. For example, it can automatically make reservations at restaurants that passengers are interested in, automatically purchase tickets for events that passengers wish to attend, and automatically make reservations for accommodations that match the passengers' travel plans. In this way, by collaborating with other services, comprehensive support is provided to passengers. Other services include, but are not limited to, restaurant reservations and event ticket purchases. Comprehensive support includes, but are not limited to, travel plan suggestions and emergency response. Some or all of the above processes in the communications department may be performed using AI or not. For example, the communications department can input data from collaboration with other services into a generating AI and have the generating AI perform the provision of comprehensive support.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The control unit controls the autonomous vehicle. For example, the control unit calculates the optimal route to the destination and issues instructions to the autonomous vehicle. The control unit can use AI to collect real-time traffic information and calculate the optimal route. The control unit can also use AI to consider weather information and select a safe route. Furthermore, the control unit can use AI to monitor the health status of the passengers and adjust the driving as needed. Step 2: The alert unit sends an alert when an anomaly occurs. For example, the alert unit sends an alert when an anomaly occurs, such as a vehicle breakdown or a traffic accident. The alert unit can use AI to identify the type of anomaly and send an appropriate alert. The alert unit can also use AI to pinpoint the location of the anomaly and prompt a quick response. Furthermore, the alert unit can use AI to analyze the cause of the anomaly and propose measures to prevent recurrence. Step 3: The communications department provides information to passengers. For example, the communications department provides information such as the estimated time of arrival and local sightseeing information. The communications department can use AI to provide information based on the passengers' interests and preferences. The communications department can also use AI to estimate the passengers' emotions and provide appropriate information. Furthermore, the communications department can use AI to analyze the passengers' past behavioral history and provide individually customized information.

[0058] (Example of form 2) The autonomous driving support system according to an embodiment of the present invention is a system that supports safe and comfortable travel for the elderly by linking an autonomous vehicle with a generative AI. This autonomous driving support system targets elderly people who have surrendered their driver's licenses, people with disabilities, and others who have difficulty driving themselves. By fusing autonomous driving technology with generative AI, it provides information on recommended spots for travel and leisure, optimal route guidance, and responses to abnormal situations. This allows people to continue their lives as before using their own cars even after surrendering their driver's licenses. For example, an AI agent controls the autonomous vehicle. For example, it calculates the optimal route to the destination and gives instructions to the autonomous vehicle. Next, in the event of an abnormality, the generative AI sends an alert to notify the passenger. For example, if an abnormality such as a vehicle malfunction or traffic accident occurs, the generative AI immediately sends an alert to prompt appropriate action. Furthermore, the generative AI supports communication with the passenger. For example, it provides information such as the estimated time of arrival and local sightseeing information. This allows passengers to enjoy their travel with peace of mind. In addition, the generative AI provides information on recommended spots for travel and leisure, and makes suggestions tailored to the passenger's interests and preferences. This system will enable a society where the elderly and people with disabilities can move freely and safely, improving their freedom to go out and their quality of life. For example, they will be able to enjoy travel and leisure activities, providing them with new meaning in life. In this way, the autonomous driving support system can support the elderly and people with disabilities so that they can move safely and comfortably.

[0059] The autonomous driving support system according to this embodiment comprises a control unit, an alert unit, and a communication unit. The control unit controls the autonomous vehicle. For example, the control unit calculates the optimal route to the destination and issues instructions to the autonomous vehicle. For example, the control unit can use AI to collect real-time traffic information and calculate the optimal route. The control unit can also use AI to consider weather information and select a safe route. Furthermore, the control unit can use AI to monitor the health status of the passengers and adjust the driving as needed. The alert unit sends an alert when an abnormality occurs. For example, the alert unit sends an alert when an abnormality occurs, such as a vehicle malfunction or a traffic accident. For example, the alert unit can use AI to identify the type of abnormality and send an appropriate alert. Furthermore, the alert unit can use AI to identify the location of the abnormality and prompt a quick response. Furthermore, the alert unit can use AI to analyze the cause of the abnormality and propose measures to prevent recurrence. The communication unit provides information to the passengers. For example, the communication unit provides information such as the estimated time of arrival and local sightseeing information. For example, the communication unit can use AI to provide information based on the passengers' interests and preferences. Furthermore, the communication unit can use AI to estimate the passenger's emotions and provide appropriate information. In addition, the communication unit can use AI to analyze the passenger's past behavioral history and provide individually customized information. As a result, the autonomous driving support system according to this embodiment can control the autonomous vehicle, send alerts in case of abnormalities, and provide information to the passenger.

[0060] The control unit controls the autonomous vehicle. For example, the control unit calculates the optimal route to the destination and issues instructions to the autonomous vehicle. For example, the control unit can use AI to collect real-time traffic information and calculate the optimal route. Specifically, the AI ​​utilizes traffic sensors, cameras, and GPS data to grasp the current traffic situation in real time. This allows it to immediately reflect information such as congestion and accidents and select the optimal route. The control unit can also use AI to consider weather information and select a safe route. For example, it can analyze weather data and select a route that avoids bad weather such as rain, snow, and fog. Furthermore, the control unit can use AI to monitor the health of the passengers and adjust driving as needed. For example, it can detect the passengers' heart rate, body temperature, and respiratory status with sensors and, if there is an abnormality, it can reduce the driving speed or instruct the vehicle to head to the nearest medical facility. In this way, the control unit can comprehensively consider traffic conditions, weather, and the health of the passengers to achieve safe and efficient driving. Furthermore, the control unit can learn from past driving data and traffic patterns to make future predictions. For example, by predicting fluctuations in traffic volume during specific times of day or on specific days of the week and adjusting the route in advance, congestion can be avoided. This allows the control unit to consistently provide optimal driving, improving passenger comfort and safety.

[0061] The alert unit sends an alert when an abnormality occurs. For example, it sends an alert when an abnormality occurs, such as a vehicle breakdown or a traffic accident. The alert unit can, for example, use AI to identify the type of abnormality and send an appropriate alert. Specifically, it monitors data obtained from various sensors in the vehicle in real time and detects abnormal vibrations, temperature increases, unusual noises, etc. This allows it to identify specific abnormalities such as engine failure or a flat tire and send an appropriate alert. The alert unit can also use AI to pinpoint the location of the abnormality and prompt a quick response. For example, it can use GPS data to pinpoint the exact location where the abnormality occurred and notify the nearest repair shop or emergency service. Furthermore, the alert unit can use AI to analyze the cause of the abnormality and propose measures to prevent recurrence. For example, it can analyze abnormality patterns based on past data and propose a maintenance schedule tailored to the deterioration and usage of specific parts. This enables the alert unit to achieve early detection of abnormalities and a quick response, improving vehicle safety and reliability. In addition, the alert unit records the history of responses to abnormalities, which can be used for future improvements. For example, by analyzing past data on handling anomalies, it is possible to speed up and streamline responses. This allows the alert unit to always provide appropriate alerts based on the latest information, supporting the safe operation of vehicles.

[0062] The communications department provides information to passengers. For example, it provides information such as estimated arrival times and local sightseeing information. The communications department can also use AI to provide information based on passengers' interests and preferences. Specifically, it can analyze passengers' past search history and behavioral patterns to provide information on tourist destinations, restaurants, and events that may be of interest to them. Furthermore, the communications department can use AI to estimate passengers' emotions and provide appropriate information. For example, it can analyze passengers' facial expressions and tone of voice to suggest relaxation music if they want to relax, or provide information to calm them down if they are excited. In addition, the communications department can use AI to analyze passengers' past behavioral history and provide individually customized information. For example, based on places visited in the past and services used, it can provide information to encourage repeat visits or new suggestions. In this way, the communications department can provide personalized information to passengers and offer a comfortable and fulfilling travel experience. Moreover, the communications department can continuously update information in real time through dialogue with passengers. For example, if a passenger makes a new request, it can respond on the spot and provide the latest information. This allows the communications department to consistently provide information tailored to passengers' needs, thereby increasing their satisfaction during their journey.

[0063] The control unit can calculate the optimal route to the destination and issue instructions to the autonomous vehicle. For example, the control unit can use AI to collect real-time traffic information and calculate the optimal route. The control unit can also use AI to consider weather information and select a safe route. The control unit can also use AI to monitor the health status of passengers and adjust driving as needed. This enables efficient travel by calculating the optimal route to the destination and issuing instructions to the autonomous vehicle. The optimal route includes, but is not limited to, distance, time, and traffic conditions. Some or all of the above-described processes in the control unit may be performed using AI or not. For example, the control unit can input real-time traffic information into a generating AI and have the generating AI calculate the optimal route.

[0064] The alert unit can send alerts when an anomaly occurs, such as a vehicle breakdown or a traffic accident. The alert unit can, for example, use AI to identify the type of anomaly and send an appropriate alert. The alert unit can also, for example, use AI to pinpoint the location of the anomaly and prompt a quick response. The alert unit can also, for example, use AI to analyze the cause of the anomaly and propose measures to prevent recurrence. This enables a quick response by sending an alert when an anomaly occurs. Anomalies include, but are not limited to, vehicle breakdowns, traffic accidents, and system errors. Some or all of the above-described processes in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input the type of anomaly into a generating AI and have the generating AI send an appropriate alert.

[0065] The communications department can provide information such as estimated arrival times and local sightseeing information. The communications department can, for example, use AI to provide information based on the passenger's interests and preferences. The communications department can also, for example, use AI to estimate the passenger's emotions and provide appropriate information. The communications department can also, for example, use AI to analyze the passenger's past behavioral history and provide individually customized information. This improves passenger convenience by providing information such as estimated arrival times and local sightseeing information. The information includes, but is not limited to, estimated arrival times, sightseeing information, text, audio, and visual information. Some or all of the above processing in the communications department may be performed using, for example, AI, or not using AI. For example, the communications department can input the passenger's interests and preferences into a generating AI and have the generating AI provide appropriate information.

[0066] The communications department can provide information on recommended spots for travel and leisure. For example, the communications department can use AI to provide information based on the passenger's interests and preferences. The communications department can also use AI to estimate the passenger's emotions and provide appropriate information. The communications department can also use AI to analyze the passenger's past behavioral history and provide individually customized information. This enhances the passenger's enjoyment by providing information on recommended spots for travel and leisure. The information includes, but is not limited to, travel spots, leisure spots, text, audio, and visual information. Some or all of the above processing in the communications department may be performed using AI or not. For example, the communications department can input the passenger's interests and preferences into a generating AI and have the generating AI provide appropriate information.

[0067] The control unit can estimate the passenger's emotions and adjust the driving speed and route based on the estimated emotions. For example, if the passenger is tense, the control unit can have the AI ​​reduce the driving speed and select a safer route. If the passenger is relaxed, the control unit can have the AI ​​maintain a moderate driving speed and select a scenic route. If the passenger is in a hurry, the control unit can have the AI ​​select the shortest route and adjust it to reach the destination quickly. This allows for safer and more comfortable travel by adjusting the driving speed and route according to the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit can input passenger emotion data into the generative AI and have the generative AI perform adjustments to the driving speed and route.

[0068] The control unit can analyze past driving data and learn and apply the optimal driving pattern. For example, the control unit can learn and apply the driving style preferred by the passenger from past driving data. The control unit can also apply the optimal driving pattern according to specific time periods or weather conditions based on past driving data. For example, the control unit can analyze past driving data and learn and apply a fuel-efficient driving pattern. This enables efficient driving by analyzing past driving data and learning and applying the optimal driving pattern. The optimal driving pattern includes, but is not limited to, fuel efficiency, safety, and comfort. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input past driving data into a generating AI and have the generating AI learn and apply the optimal driving pattern.

[0069] The control unit can recalculate the route in real time according to weather and traffic conditions and provide the optimal route. For example, based on real-time weather information, the control unit can select a route that is less slippery in rainy weather. For example, based on real-time traffic congestion information, the control unit can also select a route that avoids congestion. For example, based on real-time road construction information, the control unit can also select a detour route. This enables efficient travel by recalculating the route in real time according to weather and traffic conditions and providing the optimal route. The optimal route includes, but is not limited to, distance, time, traffic conditions, and weather. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input real-time weather and traffic information into a generating AI and have the generating AI perform the recalculation of the optimal route.

[0070] The control unit can estimate the passenger's emotions and make adjustments to improve driving comfort based on the estimated emotions. For example, if the passenger is feeling anxious, the control unit can use AI to slow down acceleration and deceleration to improve driving smoothness. For example, if the passenger is relaxed, the control unit can use AI to adjust music and air conditioning settings to provide a comfortable environment. For example, if the passenger is tired, the control unit can use AI to suggest rest points and temporarily suspend driving. This makes for a more comfortable journey by making adjustments to improve driving comfort according to the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit can input passenger emotion data into a generative AI and have the generative AI perform adjustments to improve driving comfort.

[0071] The control unit can monitor the passenger's health condition and adjust the driving as needed. For example, the control unit can monitor the passenger's heart rate and blood pressure and stop driving if an abnormality is detected. For example, if the passenger is feeling fatigued, the control unit can have the AI ​​suggest a rest and temporarily suspend driving. For example, the control unit can adjust the driving speed and route according to the passenger's health condition. This ensures safe travel by monitoring the passenger's health condition and adjusting driving as needed. Health conditions include, but are not limited to, heart rate, blood pressure, and body temperature. Some or all of the above processing in the control unit may be performed using, for example, AI, or without AI. For example, the control unit can input the passenger's health data into a generating AI and have the generating AI perform driving adjustments.

[0072] The control unit can communicate with other autonomous vehicles and cooperate to perform optimal driving. For example, the control unit can communicate with other autonomous vehicles and coordinate to adjust the route to avoid traffic congestion. The control unit can also communicate with other autonomous vehicles and adjust the speed to maintain a safe distance between vehicles. The control unit can also communicate with other autonomous vehicles and cooperate to secure an evacuation route in an emergency. This enables efficient and safe driving by communicating with other autonomous vehicles and performing optimal driving in cooperation. Communication with other autonomous vehicles includes, but is not limited to, V2V communication, Wi-Fi, and 5G. Some or all of the above processing in the control unit may be performed using, for example, AI, or not using AI. For example, the control unit can input communication data with other autonomous vehicles into a generating AI and have the generating AI perform adjustments for cooperative driving.

[0073] The alert unit can estimate the passenger's emotions and adjust the content and timing of the alert based on the estimated emotions. For example, if the passenger is tense, the AI ​​can make the alert content concise and delay the timing. If the passenger is relaxed, the AI ​​can provide a more detailed alert and speed up the timing. If the passenger is in a hurry, the AI ​​can send a rapid alert to prompt immediate action. This allows for more appropriate alerts to be provided by adjusting the content and timing of the alert according to the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input passenger emotion data into the generative AI and have the generative AI adjust the content and timing of the alert.

[0074] The alert unit can propose the optimal response when an anomaly occurs by referring to past data. For example, the alert unit can propose the optimal response based on past anomaly occurrence data. The alert unit can also propose a quick response by analyzing past anomaly occurrence data. The alert unit can also propose a response to similar anomalies by referring to past anomaly occurrence data. This enables a quick and appropriate response by proposing the optimal response when an anomaly occurs by referring to past data. Anomalies include, but are not limited to, vehicle breakdowns, traffic accidents, and system errors. Optimal response measures include, but are not limited to, repair procedures, evacuation routes, and contact information. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input past anomaly occurrence data into a generating AI and have the generating AI propose an optimal response.

[0075] The alert unit can select different alert methods (voice, visual, vibration, etc.) depending on the type of anomaly. For example, in the event of a vehicle malfunction, the alert unit prioritizes voice alerts to notify the occupants. For example, in the event of a traffic accident, the alert unit can also prioritize visual alerts to notify the occupants. For example, in an emergency, the alert unit can also prioritize vibration alerts to notify the occupants. This allows for more effective alerts by selecting different alert methods depending on the type of anomaly. Anomalies include, but are not limited to, vehicle malfunctions, traffic accidents, and system errors. Alert methods include, but are not limited to, voice alerts, visual alerts, and vibration alerts. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input the type of anomaly into a generating AI and have the generating AI select an appropriate alert method.

[0076] The alert unit can estimate the passenger's emotions and determine the priority of alerts based on the estimated emotions. For example, if the passenger is tense, the AI ​​will prioritize notifying important alerts. The alert unit can also prioritize notifying detailed alerts if the passenger is relaxed. For example, if the passenger is in a hurry, the AI ​​will prioritize notifying alerts that require immediate attention. This ensures that more important alerts are provided preferentially by prioritizing alerts according to the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input passenger emotion data into a generative AI and have the generative AI determine the priority of alerts.

[0077] The alert unit can propose the optimal evacuation route based on surrounding traffic conditions when an anomaly occurs. For example, the alert unit can propose the optimal evacuation route based on surrounding traffic congestion information. The alert unit can also propose a detour route based on surrounding road construction information. The alert unit can also propose a safe evacuation route based on surrounding traffic accident information. This enables safe evacuation by proposing the optimal evacuation route based on surrounding traffic conditions when an anomaly occurs. Anomalies include, but are not limited to, vehicle breakdowns, traffic accidents, and system errors. Optimal evacuation routes include, but are not limited to, distance, time, and traffic conditions. Some or all of the above processing in the alert unit may be performed using, for example, AI, or without AI. For example, the alert unit can input surrounding traffic condition data into a generating AI and have the generating AI propose the optimal evacuation route.

[0078] The alert unit can be equipped with a function to automatically send notifications to emergency contacts when an anomaly occurs. For example, the alert unit can automatically send notifications to emergency contacts when a vehicle breaks down. The alert unit can also automatically send notifications to emergency contacts when a traffic accident occurs. The alert unit can also automatically send notifications to emergency contacts in an emergency to encourage a quick response. This enables a quick response by automatically sending notifications to emergency contacts when an anomaly occurs. Anomalies include, but are not limited to, vehicle breakdowns, traffic accidents, and system errors. Emergency contacts include, but are not limited to, family members, ambulance services, and the police. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input the type of anomaly into a generating AI and have the generating AI execute the notification to the emergency contacts.

[0079] The communication unit can estimate the passenger's emotions and adjust the method and content of information provision based on the estimated emotions. For example, if the passenger is nervous, the AI ​​can provide concise and easy-to-understand information. If the passenger is relaxed, the AI ​​can provide detailed information. If the passenger is excited, the AI ​​can provide visually appealing information. By adjusting the method and content of information provision according to the passenger's emotions, more appropriate information is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI or not using AI. For example, the communication unit can input passenger emotion data into the generative AI and have the generative AI adjust the method and content of information provision.

[0080] The communications department can analyze passengers' past behavioral history and provide individually customized information. For example, the communications department can provide relevant tourist information based on places the passenger has visited in the past. For example, the communications department can also provide information tailored to the passenger's interests based on their past behavioral history. For example, the communications department can analyze the passenger's past behavioral history and propose the optimal travel plan. This allows for more personalized information to be provided by analyzing the passenger's past behavioral history and providing individually customized information. Behavioral history includes, but is not limited to, past travel history and service usage history. Customized information includes, but is not limited to, individual recommended spots and personalized services. Some or all of the above processing in the communications department may be performed using, for example, AI, or not using AI. For example, the communications department can input passengers' past behavioral history data into a generating AI and have the generating AI perform the task of providing individually customized information.

[0081] The communications department can provide relevant information in real time based on the passenger's interests. For example, the communications department can provide information in real time about tourist spots that the passenger has shown interest in. For example, the communications department can also provide information on local events in real time based on the passenger's interests. For example, the communications department can also provide information on restaurants and cafes in real time according to the passenger's interests. This allows for the provision of more appropriate information by providing relevant information in real time based on the passenger's interests. Interests include, but are not limited to, past behavioral history and survey results. Relevant information includes, but is not limited to, event information and restaurant information. Some or all of the above processing in the communications department may be performed using, for example, AI, or not using AI. For example, the communications department can input passenger interest data into a generating AI and have the generating AI provide relevant information in real time.

[0082] The communication unit can estimate the passenger's emotions and adjust the timing of information delivery based on the estimated emotions. For example, if the passenger is tense, the AI ​​in the communication unit can delay the timing of information delivery. For example, if the passenger is relaxed, the AI ​​in the communication unit can also advance the timing of information delivery. For example, if the passenger is in a hurry, the AI ​​in the communication unit can also provide information quickly. In this way, by adjusting the timing of information delivery according to the passenger's emotions, information is provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can input passenger emotion data into the generative AI and have the generative AI perform the adjustment of the timing of information delivery.

[0083] The communications department can monitor the health status of passengers and provide health-related information as needed. For example, the communications department can monitor the passenger's heart rate and blood pressure and provide health-related information if an abnormality is detected. For example, if a passenger is feeling fatigued, the communications department can have the AI ​​suggest a rest and provide health-related information. For example, the communications department can also provide information on appropriate exercise and diet according to the passenger's health status. This makes health management easier by monitoring the passenger's health status and providing health-related information as needed. Health status includes, but is not limited to, heart rate, blood pressure, and body temperature. Health-related information includes, but is not limited to, health advice and information on medical institutions. Some or all of the above processing in the communications department may be performed using, for example, AI, or not using AI. For example, the communications department can input passenger health data into a generating AI and have the generating AI provide health-related information.

[0084] The communications department can collaborate with other services to provide comprehensive support. For example, the communications department can automatically make reservations at restaurants that passengers are interested in. For example, the communications department can automatically purchase tickets for events that passengers wish to attend. For example, the communications department can automatically make reservations for accommodations that match the passenger's travel plans. In this way, by collaborating with other services, comprehensive support is provided to passengers. Other services include, but are not limited to, restaurant reservations and event ticket purchases. Comprehensive support includes, but is not limited to, travel plan suggestions and emergency response. Some or all of the above processes in the communications department may be performed using, for example, AI, or not using AI. For example, the communications department can input data from collaboration with other services into a generating AI and have the generating AI perform the provision of comprehensive support.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The control unit can estimate the passenger's emotions and adjust the driving speed and route based on the estimated emotions. For example, if the passenger is tense, the AI ​​can reduce the driving speed and select a safer route. If the passenger is relaxed, the AI ​​can maintain a moderate driving speed and select a scenic route. If the passenger is in a hurry, the AI ​​can select the shortest route and adjust it to reach the destination quickly. This allows for safer and more comfortable travel by adjusting the driving speed and route according to the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input passenger emotion data into a generative AI and have the generative AI adjust the driving speed and route.

[0087] The control unit can analyze past driving data, learn, and apply optimal driving patterns. For example, it can learn and apply the driving style preferred by passengers from past driving data. Based on past driving data, it can also apply optimal driving patterns according to specific time periods or weather conditions. It can also analyze past driving data, learn, and apply fuel-efficient driving patterns. As a result, efficient driving becomes possible by analyzing past driving data, learning, and applying optimal driving patterns. Optimal driving patterns include, but are not limited to, fuel efficiency, safety, and comfort. Some or all of the above-described processes in the control unit may be performed using AI or not. For example, the control unit can input past driving data into a generating AI and have the generating AI perform the learning and application of optimal driving patterns.

[0088] The alert unit can estimate the passenger's emotions and adjust the content and timing of the alert based on the estimated emotions. For example, if the passenger is nervous, the AI ​​can make the alert content concise and delay the timing. If the passenger is relaxed, the AI ​​can provide a more detailed alert and speed up the timing. If the passenger is in a hurry, the AI ​​can send a rapid alert to prompt immediate action. This allows for more appropriate alerts to be provided by adjusting the content and timing of the alert according to the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input passenger emotion data into a generative AI and have the generative AI adjust the content and timing of the alert.

[0089] The control unit can recalculate routes in real time according to weather and traffic conditions and provide the optimal route. For example, based on real-time weather information, it can select a route that is less slippery in rainy weather. Based on real-time traffic congestion information, it can also select a route that avoids congestion. Based on real-time road construction information, it can also select a detour route. This enables efficient travel by recalculating routes in real time according to weather and traffic conditions and providing the optimal route. The optimal route includes, but is not limited to, distance, time, traffic conditions, and weather. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input real-time weather and traffic information into a generating AI and have the generating AI perform the recalculation of the optimal route.

[0090] The communication unit can estimate the passenger's emotions and adjust the method and content of information delivery based on the estimated emotions. For example, if the passenger is nervous, the AI ​​can provide concise and easy-to-understand information. If the passenger is relaxed, the AI ​​can also provide detailed information. If the passenger is excited, the AI ​​can also provide visually appealing information. In this way, more appropriate information is provided by adjusting the method and content of information delivery according to the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can input passenger emotion data into a generative AI and have the generative AI adjust the method and content of information delivery.

[0091] The alert unit can propose the optimal response when an anomaly occurs by referring to past data. For example, it can propose the optimal response based on past anomaly occurrence data. It can also analyze past anomaly occurrence data and propose a quick response. It can also refer to past anomaly occurrence data and propose a response to a similar anomaly. This enables a quick and appropriate response by referring to past data and proposing the optimal response when an anomaly occurs. Anomalies include, but are not limited to, vehicle breakdowns, traffic accidents, and system errors. Optimal response measures include, but are not limited to, repair procedures, evacuation routes, and contact information. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input past anomaly occurrence data into a generating AI and have the generating AI propose the optimal response.

[0092] The control unit can communicate with other autonomous vehicles and cooperate to perform optimal driving. For example, it can communicate with other autonomous vehicles to coordinate route adjustments to avoid traffic congestion. It can also communicate with other autonomous vehicles to adjust speed to maintain a safe distance between vehicles. It can also communicate with other autonomous vehicles to cooperate in securing evacuation routes in emergencies. This enables efficient and safe driving by communicating with other autonomous vehicles and performing optimal driving in cooperation. Communication with other autonomous vehicles includes, but is not limited to, V2V communication, Wi-Fi, and 5G. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input communication data with other autonomous vehicles into a generating AI and have the generating AI perform adjustments for cooperative driving.

[0093] The alert unit can select different alert methods (voice, visual, vibration, etc.) depending on the type of anomaly. For example, in the event of a vehicle malfunction, a voice alert will be prioritized to notify the occupants. In the event of a traffic accident, a visual alert may be prioritized to notify the occupants. In an emergency, a vibration alert may be prioritized to notify the occupants. This allows for more effective alerts by selecting different alert methods depending on the type of anomaly. Anomalies include, but are not limited to, vehicle malfunctions, traffic accidents, and system errors. Alert methods include, but are not limited to, voice alerts, visual alerts, and vibration alerts. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input the type of anomaly into a generating AI and have the generating AI select an appropriate alert method.

[0094] The communication unit can estimate the passenger's emotions and adjust the timing of information delivery based on the estimated emotions. For example, if the passenger is nervous, the AI ​​can delay the timing of information delivery. If the passenger is relaxed, the AI ​​can also speed up the timing of information delivery. If the passenger is in a hurry, the AI ​​can also provide information quickly. In this way, by adjusting the timing of information delivery according to the passenger's emotions, information is delivered at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can input passenger emotion data into a generative AI and have the generative AI adjust the timing of information delivery.

[0095] The communications department can collaborate with other services to provide comprehensive support. For example, it can automatically make reservations at restaurants that passengers are interested in, automatically purchase tickets for events that passengers wish to attend, and automatically make reservations for accommodations that match the passengers' travel plans. In this way, by collaborating with other services, comprehensive support is provided to passengers. Other services include, but are not limited to, restaurant reservations and event ticket purchases. Comprehensive support includes, but are not limited to, travel plan suggestions and emergency response. Some or all of the above processes in the communications department may be performed using AI or not. For example, the communications department can input data from collaboration with other services into a generating AI and have the generating AI perform the provision of comprehensive support.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The control unit controls the autonomous vehicle. For example, the control unit calculates the optimal route to the destination and issues instructions to the autonomous vehicle. The control unit can use AI to collect real-time traffic information and calculate the optimal route. The control unit can also use AI to consider weather information and select a safe route. Furthermore, the control unit can use AI to monitor the health status of the passengers and adjust the driving as needed. Step 2: The alert unit sends an alert when an anomaly occurs. For example, the alert unit sends an alert when an anomaly occurs, such as a vehicle breakdown or a traffic accident. The alert unit can use AI to identify the type of anomaly and send an appropriate alert. The alert unit can also use AI to pinpoint the location of the anomaly and prompt a quick response. Furthermore, the alert unit can use AI to analyze the cause of the anomaly and propose measures to prevent recurrence. Step 3: The communications department provides information to passengers. For example, the communications department provides information such as the estimated time of arrival and local sightseeing information. The communications department can use AI to provide information based on the passengers' interests and preferences. The communications department can also use AI to estimate the passengers' emotions and provide appropriate information. Furthermore, the communications department can use AI to analyze the passengers' past behavioral history and provide individually customized information.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] Each of the multiple elements described above, including the control unit, alert unit, and communication unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the control unit is implemented by the control unit 46A of the smart device 14 and controls the autonomous vehicle. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends an alert when an abnormality occurs. The communication unit is implemented by the control unit 46A of the smart device 14 and provides information to the passenger. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the control unit, alert unit, and communication unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the control unit is implemented by the control unit 46A of the smart glasses 214 and controls the autonomous vehicle. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends an alert when an abnormality occurs. The communication unit is implemented by the control unit 46A of the smart glasses 214 and provides information to the passenger. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the control unit, alert unit, and communication unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the control unit is implemented by the control unit 46A of the headset terminal 314 and controls the autonomous vehicle. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends an alert when an abnormality occurs. The communication unit is implemented by the control unit 46A of the headset terminal 314 and provides information to the passenger. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the control unit, alert unit, and communication unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the control unit is implemented by the control unit 46A of the robot 414 and controls the autonomous vehicle. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends an alert when an abnormality occurs. The communication unit is implemented by the control unit 46A of the robot 414 and provides information to the passenger. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) A control unit that controls the autonomous vehicle, An alert unit that sends an alert when an abnormality occurs, It includes a communications department that provides information to passengers. A system characterized by the following features. (Note 2) The control unit, It calculates the optimal route to the destination and gives instructions to the autonomous vehicle. The system described in Appendix 1, characterized by the features described herein. (Note 3) The alert unit is, The system sends alerts in the event of a vehicle malfunction, traffic accident, or other abnormality. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned communications department, It provides information such as estimated arrival time and local sightseeing information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned communications department, We provide information on recommended spots for travel and leisure. The system described in Appendix 1, characterized by the features described herein. (Note 6) The control unit, The system estimates the passenger's emotions and adjusts the driving speed and route based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The control unit, Analyze past driving data, learn the optimal driving pattern, and apply it. The system described in Appendix 1, characterized by the features described herein. (Note 8) The control unit, The system recalculates routes in real time based on weather and traffic conditions to provide the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 9) The control unit, It estimates the passenger's emotions and makes adjustments to improve driving comfort based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The control unit, The vehicle monitors the health of passengers and adjusts driving as needed. The system described in Appendix 1, characterized by the features described herein. (Note 11) The control unit, It communicates with other autonomous vehicles and works together to perform optimal driving. The system described in Appendix 1, characterized by the features described herein. (Note 12) The alert unit is, The system estimates the passenger's emotions and adjusts the content and timing of alerts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The alert unit is, When an anomaly occurs, we refer to past data to propose the optimal countermeasure. The system described in Appendix 1, characterized by the features described herein. (Note 14) The alert unit is, Choose a different alerting method depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 15) The alert unit is, The system estimates the passengers' emotions and prioritizes alerts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The alert unit is, In the event of an emergency, the system will propose the optimal evacuation route based on the surrounding traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The alert unit is, Add a feature to automatically send notifications to emergency contacts in the event of an anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned communications department, The system estimates the passengers' emotions and adjusts the method and content of information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned communications department, It analyzes the past behavioral history of passengers and provides individually customized information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned communications department, Based on passengers' interests and preferences, relevant information is provided in real time. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned communications department, The system estimates the passenger's emotions and adjusts the timing of information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned communications department, Monitor the health status of passengers and provide health information as needed. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned communications department, We provide comprehensive support by collaborating with other services. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A control unit that controls the autonomous vehicle, An alert unit that sends an alert when an abnormality occurs, It includes a communications department that provides information to passengers. A system characterized by the following features.

2. The control unit, It calculates the optimal route to the destination and gives instructions to the autonomous vehicle. The system according to feature 1.

3. The alert unit is, The system sends alerts in the event of a vehicle malfunction, traffic accident, or other abnormality. The system according to feature 1.

4. The aforementioned communications department, It provides information such as estimated arrival time and local sightseeing information. The system according to feature 1.

5. The aforementioned communications department, We provide information on recommended spots for travel and leisure. The system according to feature 1.

6. The control unit, The system estimates the passenger's emotions and adjusts the driving speed and route based on those estimated emotions. The system according to feature 1.

7. The control unit, Analyze past driving data, learn the optimal driving pattern, and apply it. The system according to feature 1.

8. The control unit, The system recalculates routes in real time based on weather and traffic conditions to provide the optimal route. The system according to feature 1.

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