System

Abstract

To provide a system which presents an optimal route to a destination and gives an advice to a driver.SOLUTION: A system comprises a reception section, a traffic information collection section, a route guide section, a vehicle information provision section and a safety operation advice section. The reception section receives input for setting a destination. The traffic information collection section collects traffic information based on information received with the reception section. The route guide section proposes a route based on the information collected with the traffic information collection section. The vehicle information provision section provides vehicle information based on the route proposed with the route guide section. The safety operation advice section gives an operational advice based on the information provided with the vehicle information provision section.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately develop systems that suggest optimal routes to a destination and provide driving advice, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest an optimal route to a destination and provide driving advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a traffic information collection unit, a route guidance unit, a vehicle information provision unit, and a safe driving advice unit. The reception unit receives input of a destination setting. The traffic information collection unit collects traffic information based on the information received by the reception unit. The route guidance unit proposes a route based on the information collected by the traffic information collection unit. The vehicle information provision unit provides vehicle information based on the route proposed by the route guidance unit. The safe driving advice unit provides driving advice based on the information provided by the vehicle information provision unit. Effect of the Invention

[0007] The system according to the embodiment can suggest optimal routes to a destination and provide driving advice. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A driving assistant system according to an embodiment of the present invention is a system that uses a generating AI to provide a car navigation system and a conversational driving assistant service. In this system, the driver sets a destination in natural language, and the generating AI collects real-time traffic information, proposes an optimal route, provides vehicle information, and provides safe driving advice. For example, when the driver inputs an instruction such as "I want to go to the next gas station" to the generating AI, the generating AI analyzes this instruction and calculates the optimal route. Next, the generating AI collects real-time traffic information and provides route guidance. For example, the optimal route is proposed based on traffic congestion information and accident information. This allows the driver to reach the destination smoothly. In addition, the generating AI provides vehicle information. For example, the generating AI informs the driver of information such as the remaining fuel level and tire pressure. This allows the driver to always be aware of the vehicle's condition. The generating AI also provides safe driving advice. For example, the generating AI advises the driver to avoid sudden braking and sudden acceleration. This allows the driver to drive safely. This allows the driving assistant system to achieve a convenient driving experience and improved safety.

[0029] The driving assistant system according to the embodiment includes a reception unit, a traffic information collection unit, a route guidance unit, a vehicle information provision unit, and a safe driving advice unit. The reception unit receives an input of a destination setting from the driver. For example, the driver can set the destination in natural language such as "I want to go to the next gas station." The reception unit uses a generation AI to analyze the driver's voice input or touch panel input and set the destination. The traffic information collection unit collects real-time traffic information based on the information received by the reception unit. For example, the traffic information collection unit collects congestion information, accident information, road construction information, etc., and provides information for proposing an optimal route. The traffic information collection unit uses a generation AI to collect data from a traffic information database on the Internet and sensors, and updates the data in real time. The route guidance unit proposes an optimal route based on the information collected by the traffic information collection unit. For example, the route guidance unit proposes an optimal route to the driver based on criteria such as the shortest distance, the shortest time, and a route with low traffic volume. The route guidance unit uses a generation AI to analyze the collected traffic information and calculate an optimal route. The vehicle information providing unit provides vehicle information based on the route proposed by the route guidance unit. For example, the vehicle information providing unit provides information such as the remaining amount of fuel, tire pressure, and engine condition to the driver. The vehicle information providing unit uses the generation AI to analyze data from the vehicle's sensors and monitor the vehicle's condition in real time. The safe driving advice unit provides safe driving advice based on the information provided by the vehicle information providing unit. For example, the safe driving advice unit advises the driver to avoid sudden braking and sudden acceleration. The safe driving advice unit uses the generation AI to analyze the driver's driving behavior and provide advice for safe driving. As a result, the driving assistant system according to the embodiment can consistently perform operations from setting the driver's destination to collecting traffic information, proposing routes, providing vehicle information, and providing safe driving advice.

[0030] The reception unit accepts input of destination setting from the driver. For example, the driver can set the destination in natural language such as "I want to go to the next gas station." The reception unit uses the generation AI to analyze the driver's voice input and touch panel input to set the destination. Specifically, the generation AI uses voice recognition technology to convert the driver's voice into text, and analyzes the text to identify the destination. For example, if the driver says "I want to go to a nearby restaurant," the generation AI extracts the keyword "nearby restaurant" and searches for the restaurant closest to the current location. In addition, in the case of touch panel input, when the driver inputs the destination using the on-screen keyboard, the generation AI analyzes the input content and sets the appropriate destination. Furthermore, the generation AI can learn the past input history and the driver's preferences to set the destination with higher accuracy. For example, the driver can reduce the effort of setting the destination by preferentially suggesting places that he or she has frequently visited in the past. This allows the reception unit to accurately understand the driver's intentions and set the destination quickly and efficiently.

[0031] The traffic information collection unit collects real-time traffic information based on the information received by the reception unit. For example, the traffic information collection unit collects congestion information, accident information, road construction information, etc., and provides information for proposing optimal routes. The traffic information collection unit uses the generation AI to collect data from traffic information databases and sensors on the Internet and updates it in real time. Specifically, the generation AI acquires data from multiple traffic information services on the Internet, integrates the data, and centrally manages it. It also collects data from sensors and cameras installed on roads to grasp traffic conditions in real time. For example, it analyzes data from sensors to determine whether a specific road is congested, and analyzes camera footage to check whether there have been any accidents. Furthermore, the generation AI can predict traffic patterns for specific times of day and days of the week based on past traffic data, and evaluate the risk of future congestion and accidents. This allows the traffic information collection unit to build a foundation for always providing the latest traffic information and proposing optimal routes to drivers.

[0032] The route guidance unit proposes an optimal route based on the information collected by the traffic information collection unit. For example, the route guidance unit proposes an optimal route to the driver based on criteria such as the shortest distance, the shortest time, and a route with less traffic. The route guidance unit uses the generation AI to analyze the collected traffic information and calculate the optimal route. Specifically, the generation AI analyzes the collected traffic information in real time and calculates the optimal route taking into account the current traffic situation. For example, it can propose a detour route to avoid roads with congestion. In addition, the generation AI can learn the driver's past driving history and preferences and propose the optimal route for each individual driver. For example, if the driver prefers highways, the generation AI will propose a route that includes highways preferentially. In addition, the generation AI can propose the optimal route by taking into account factors such as weather, time of day, and day of the week. For example, it proposes a route that avoids slippery roads in rainy weather, and proposes a route with less traffic during rush hour. As a result, the route guidance unit can always provide the driver with the optimal route and support comfortable and efficient driving.

[0033] The vehicle information providing unit provides vehicle information based on the route proposed by the route guidance unit. For example, the vehicle information providing unit provides the driver with information such as the remaining amount of fuel, tire pressure, and engine condition. The vehicle information providing unit uses the generating AI to analyze data from the vehicle's sensors and monitor the vehicle's condition in real time. Specifically, the generating AI collects data from various sensors mounted on the vehicle and analyzes the data to evaluate the vehicle's condition. For example, the generating AI analyzes data from the fuel sensor to check the remaining amount of fuel, and analyzes data from the tire pressure sensor to evaluate the tire condition. In addition, in order to monitor the engine condition, the generating AI analyzes data from the engine sensor and issues a warning to the driver if an abnormality is detected. Furthermore, the generating AI can predict the vehicle's maintenance schedule based on past data and suggest maintenance at an appropriate time. As a result, the vehicle information providing unit can provide the driver with the vehicle's condition in real time and support safe and comfortable driving.

[0034] The safe driving advice unit provides safe driving advice based on the information provided by the vehicle information provision unit. For example, the safe driving advice unit advises the driver to avoid sudden braking and sudden acceleration. The safe driving advice unit uses the generation AI to analyze the driver's driving behavior and provide advice for safe driving. Specifically, the generation AI analyzes data from the vehicle's sensors and evaluates the driver's driving pattern. For example, if sudden braking and sudden acceleration occur frequently, the generation AI identifies the cause and proposes specific improvement measures to the driver. In addition, the generation AI can learn the driver's driving history and provide optimal advice to each individual driver. For example, if a particular driver tends to brake suddenly in a particular situation, advice is provided to avoid that situation. Furthermore, the safe driving advice unit can suggest appropriate driving methods to the driver based on traffic information and weather information updated in real time. For example, the safe driving advice unit advises the driver to avoid slippery roads in rainy weather and suggests avoiding unreasonable lane changes in traffic jams. As a result, the safe driving advice unit can always provide optimal driving advice to the driver and support safe and comfortable driving.

[0035] The reception unit can receive input from the driver in natural language. For example, the reception unit can allow the driver to set a destination in natural language, such as "I want to go to the next gas station." The reception unit uses the generation AI to analyze the driver's voice input or touch panel input and set the destination. For example, the generation AI can convert the driver's voice into text using voice recognition technology and analyze the text to identify the destination. The generation AI can also use natural language processing technology to understand the driver's intentions and set an appropriate destination. This allows the driver to set a destination in natural language, improving usability.

[0036] The traffic information collection unit can collect real-time traffic information. For example, the traffic information collection unit collects congestion information, accident information, road construction information, and the like, and provides information for proposing an optimal route. The traffic information collection unit uses the generation AI to collect data from a traffic information database and sensors on the Internet, and updates it in real time. For example, the traffic information collection unit obtains the latest traffic information from a traffic information database, and the generation AI analyzes the information and provides it to the driver. In addition, the traffic information collection unit collects data from sensors installed on the road, and the generation AI analyzes the data to grasp real-time traffic conditions. This allows the traffic information collection unit to provide route guidance based on the latest traffic conditions.

[0037] The route guidance unit can suggest an optimal route based on the collected information. The route guidance unit suggests an optimal route to the driver based on criteria such as the shortest distance, the shortest time, and a route with low traffic volume. The route guidance unit uses the generation AI to analyze the collected traffic information and calculate the optimal route. For example, the generation AI calculates the optimal route based on congestion information and accident information obtained from a traffic information database. The generation AI can also suggest the optimal route by taking into account the driver's past driving history and current driving conditions. This allows the route guidance unit to suggest the optimal route so that the driver can reach the destination smoothly.

[0038] The vehicle information providing unit can provide vehicle information such as the remaining amount of fuel and tire pressure. The vehicle information providing unit provides the driver with information such as the remaining amount of fuel, tire pressure, and engine status. The vehicle information providing unit uses the generating AI to analyze data from the vehicle's sensors and monitor the vehicle's status in real time. For example, the generating AI can analyze data from a fuel sensor to inform the driver of the remaining amount of fuel. The generating AI can also analyze data from a tire pressure sensor to inform the driver of the tire pressure. This allows the driver to always be aware of the vehicle's status and perform appropriate maintenance.

[0039] The safe driving advice unit can advise the driver to avoid sudden braking and sudden acceleration. The safe driving advice unit advises the driver, for example, to avoid sudden braking and sudden acceleration. The safe driving advice unit uses the generation AI to analyze the driving behavior of the driver and provide advice for safe driving. For example, the generation AI analyzes data from the vehicle's acceleration sensor to detect sudden braking and sudden acceleration and provide advice to the driver. In addition, the generation AI can analyze the driver's driving history to grasp the tendency of sudden braking and sudden acceleration and provide appropriate advice. This enables the driver to drive safely.

[0040] The reception unit can analyze the driver's past destination setting history and suggest the optimal input method. For example, the reception unit automatically displays destinations that the driver has frequently input in the past as candidates. It preferentially suggests input methods (voice, text, etc.) that the driver has used in the past. It predicts and suggests destinations to be used during a specific time period from the driver's past destination setting history. The reception unit uses the generation AI to analyze the driver's past destination setting history and suggests the optimal input method. For example, the generation AI can suggest the optimal input method by analyzing past history data and learning the driver's input patterns. This makes destination setting more efficient by suggesting the optimal input method based on past history.

[0041] The reception unit can perform filtering based on the driver's current driving situation and areas of interest when inputting destination settings. For example, when the driver inputs while driving, the reception unit prioritizes voice input and minimizes visual operations. Based on the driver's areas of interest (e.g., restaurants, gas stations, etc.), relevant destinations are preferentially displayed. Based on the driver's current driving situation (e.g., driving on a highway, in a traffic jam, etc.), an appropriate destination is proposed. The reception unit uses the generation AI to analyze the driver's current driving situation and areas of interest, and filters and proposes optimal destinations. For example, the generation AI can prioritize displaying highly relevant destinations based on the driver's driving situation data and past search history. As a result, more appropriate destinations can be proposed by filtering based on the driving situation and areas of interest.

[0042] The reception unit can preferentially suggest highly relevant destinations in consideration of the driver's geographical location information when inputting destination settings. For example, the reception unit preferentially displays destinations close to the driver's current location. Based on the driver's current location, the reception unit proposes the most accessible destination. Based on the driver's current location and past movement history, the reception unit proposes highly relevant destinations. The reception unit uses the generation AI to analyze the driver's geographical location information and preferentially propose highly relevant destinations. For example, the generation AI can identify and propose the destination closest to the driver's current location based on GPS data and map information. This makes it possible to propose highly relevant destinations by taking into account the geographical location information.

[0043] The reception unit can analyze the driver's social media activity and suggest related destinations when the destination setting is input. For example, the reception unit can suggest related destinations based on the location where the driver has checked in on social media. The reception unit can suggest related destinations based on the driver's interests and concerns on social media. The reception unit can suggest related destinations based on the activities of the driver's friends on social media. The reception unit can analyze the driver's social media activity and suggest related destinations using the generation AI. For example, the generation AI can analyze the content of social media posts and check-in information to suggest destinations based on the driver's interests and concerns. This makes it possible to suggest related destinations by analyzing social media activity.

[0044] When collecting traffic information, the traffic information collection unit can predict the current traffic situation by referring to past traffic data. The traffic information collection unit, for example, predicts the current congestion situation based on past traffic congestion data. The traffic information collection unit predicts the current accident risk based on past accident data. The traffic information collection unit predicts the current traffic volume based on past traffic volume data. The traffic information collection unit analyzes the past traffic data using the generation AI to predict the current traffic situation. For example, the generation AI can learn the past traffic data and construct an algorithm that predicts the current traffic situation. This makes it possible to predict the current traffic situation by referring to the past traffic data, enabling more accurate route guidance.

[0045] The traffic information collection unit can improve the accuracy of collection by taking into account specific events and weather information when collecting traffic information. The traffic information collection unit predicts traffic conditions, for example, based on a specific event (e.g., a sporting event or a concert). The traffic information collection unit predicts traffic conditions by taking into account weather information (e.g., rain or snow). The traffic information collection unit predicts traffic conditions based on a specific season (e.g., summer vacation or New Year's holidays). The traffic information collection unit uses the generation AI to analyze specific events and weather information and predict traffic conditions. For example, the generation AI can construct an algorithm that predicts traffic conditions based on event schedules and weather data. As a result, by taking into account specific events and weather information, the accuracy of collection is improved, enabling more accurate route guidance.

[0046] The traffic information collection unit can prioritize collecting highly relevant information in consideration of the driver's geographical location information when collecting traffic information. The traffic information collection unit collects the most relevant traffic information, for example, based on the driver's current location. The traffic information collection unit collects the most relevant traffic information in consideration of the driver's current location and past movement history. The traffic information collection unit collects the most relevant traffic information based on the driver's current location and destination. The traffic information collection unit uses the generation AI to analyze the driver's geographical location information and prioritize collecting highly relevant traffic information. For example, the generation AI can identify and collect the most relevant traffic information from the driver's current location based on GPS data and map information. This makes it possible to collect highly relevant traffic information by considering the geographical location information.

[0047] The traffic information collection unit can analyze the driver's social media activity and collect related traffic information when collecting traffic information. The traffic information collection unit can collect related traffic information based on the driver's check-in information on social media, for example. The traffic information collection unit can collect related traffic information based on the driver's interests and concerns on social media. The traffic information collection unit can collect related traffic information by referring to the activities of the driver's friends on social media. The traffic information collection unit can analyze the driver's social media activity and collect related traffic information using the generation AI. For example, the generation AI can analyze the content of social media posts and check-in information to collect traffic information based on the driver's interests and concerns. This makes it possible to collect related traffic information by analyzing social media activity.

[0048] When providing route guidance, the route guidance unit can suggest the optimal route by referring to past route guidance history. For example, the route guidance unit suggests the optimal route based on routes the driver has used in the past. It suggests a route that avoids congestion based on the driver's past route guidance history. It analyzes the driver's past route guidance history and suggests the most efficient route. The route guidance unit uses the generation AI to analyze the past route guidance history and suggest the optimal route. For example, the generation AI can suggest the optimal route by analyzing past history data and learning the driver's driving patterns. This makes it possible to suggest the optimal route by referring to past route guidance history.

[0049] The route guidance unit can improve the accuracy of the route by taking into account specific events and weather information when providing route guidance. The route guidance unit, for example, proposes an optimal route based on a specific event (e.g., a sporting event or a concert). The route guidance unit proposes an optimal route by taking into account weather information (e.g., rain or snow). The route guidance unit proposes an optimal route based on a specific season (e.g., summer vacation or New Year's holidays). The route guidance unit uses the generation AI to analyze specific events and weather information and propose an optimal route. For example, the generation AI can build an algorithm that proposes an optimal route based on event schedules and weather data. As a result, by taking into account specific events and weather information, the accuracy of the route is improved, enabling more accurate route guidance.

[0050] The route guidance unit can propose the optimal route by taking into account the driver's geographical location information when providing route guidance. The route guidance unit proposes the most relevant route based on the driver's current location, for example. The route guidance unit proposes the most efficient route by taking into account the driver's current location and past movement history. The route guidance unit proposes the most relevant route based on the driver's current location and destination. The route guidance unit uses the generation AI to analyze the driver's geographical location information and propose the optimal route. For example, the generation AI can identify and propose the most relevant route from the driver's current location based on GPS data and map information. This makes it possible to propose the optimal route by taking into account the geographical location information.

[0051] The route guidance unit can analyze the driver's social media activity and suggest relevant routes when providing route guidance. The route guidance unit can, for example, suggest relevant routes based on the driver's social media check-in information. The route guidance unit can suggest relevant routes based on the driver's social media interests and concerns. The route guidance unit can suggest relevant routes by referring to the activities of the driver's friends on social media. The route guidance unit can analyze the driver's social media activity and suggest relevant routes using a generation AI. For example, the generation AI can analyze the content of social media posts and check-in information to suggest routes based on the driver's interests and concerns. This makes it possible to suggest relevant routes by analyzing social media activity.

[0052] When providing vehicle information, the vehicle information providing unit can provide optimal information by referring to past vehicle information history. The vehicle information providing unit, for example, predicts and provides the current remaining fuel level based on past fuel consumption data. Provides the current tire condition based on past tire pressure data. Provides the next maintenance date based on past vehicle maintenance history. The vehicle information providing unit uses the generation AI to analyze past vehicle information history and provide optimal information. For example, the generation AI can provide optimal information by analyzing past history data and predicting the vehicle condition. This makes it possible to provide optimal information by referring to past vehicle information history.

[0053] The vehicle information providing unit can improve the accuracy of the information by taking into account specific events and weather information when providing vehicle information. For example, the vehicle information providing unit provides a fuel consumption forecast based on a specific event (e.g., a long-distance drive). Provides tire conditions taking into account weather information (e.g., rain or snow). Provides battery conditions based on a specific season (e.g., winter). The vehicle information providing unit uses the generating AI to analyze specific events and weather information and provide vehicle information. For example, the generating AI can construct an algorithm that predicts the vehicle condition based on an event schedule and weather data. As a result, by taking into account specific events and weather information, the accuracy of the information is improved, and more accurate vehicle information can be provided.

[0054] The vehicle information providing unit can provide optimal information by taking into consideration the geographical location information of the driver when providing vehicle information. The vehicle information providing unit provides the most relevant vehicle information based on, for example, the driver's current location. The vehicle information providing unit provides the most relevant vehicle information by taking into consideration the driver's current location and past movement history. The vehicle information providing unit provides the most relevant vehicle information based on the driver's current location and destination. The vehicle information providing unit uses the generating AI to analyze the driver's geographical location information and provide optimal vehicle information. For example, the generating AI can identify and provide the most relevant vehicle information from the driver's current location based on GPS data and map information. This makes it possible to provide optimal vehicle information by taking into consideration the geographical location information.

[0055] The vehicle information providing unit can analyze the driver's social media activity and provide related vehicle information when providing vehicle information. The vehicle information providing unit can provide related vehicle information based on, for example, the driver's check-in information on social media. The vehicle information providing unit can provide related vehicle information based on the driver's interests and concerns on social media. The vehicle information providing unit can provide related vehicle information with reference to the activities of the driver's friends on social media. The vehicle information providing unit can analyze the driver's social media activity and provide related vehicle information using the generation AI. For example, the generation AI can analyze the content of social media posts and check-in information to provide vehicle information based on the driver's interests and concerns. This makes it possible to provide related vehicle information by analyzing social media activity.

[0056] When giving safe driving advice, the safe driving advice unit can provide optimal advice by referring to past driving history. The safe driving advice unit, for example, provides advice to avoid sudden braking based on past sudden braking data. Provides advice to avoid sudden acceleration based on past sudden acceleration data. Suggests the safest driving method based on past driving history. The safe driving advice unit uses a generation AI to analyze past driving history and provide optimal advice. For example, the generation AI can provide optimal advice by analyzing past history data and learning the driver's driving patterns. This makes it possible to provide optimal advice by referring to past driving history.

[0057] The safe driving advice unit can improve the accuracy of safe driving advice by taking into account specific events and weather information when providing safe driving advice. The safe driving advice unit provides safe driving advice based on, for example, a specific event (e.g., a long-distance drive). The safe driving advice unit provides safe driving advice by taking into account weather information (e.g., rain or snow). The safe driving advice unit provides safe driving advice based on a specific season (e.g., winter). The safe driving advice unit uses the generation AI to analyze specific events and weather information and provide safe driving advice. For example, the generation AI can construct an algorithm for providing safe driving advice based on event schedules and weather data. As a result, by taking into account specific events and weather information, the accuracy of advice is improved, and more accurate safe driving advice can be provided.

[0058] The safe driving advice unit can provide optimal safe driving advice by taking into account the geographical location information of the driver when providing safe driving advice. The safe driving advice unit provides the most relevant safe driving advice based on, for example, the driver's current location. The safe driving advice unit provides the most relevant safe driving advice by taking into account the driver's current location and past driving history. The safe driving advice unit provides the most relevant safe driving advice based on the driver's current location and destination. The safe driving advice unit uses the generation AI to analyze the geographical location information of the driver and provide optimal safe driving advice. For example, the generation AI can identify and provide the most relevant safe driving advice from the driver's current location based on GPS data and map information. As a result, optimal safe driving advice can be provided by taking into account the geographical location information.

[0059] The safe driving advice unit can analyze the driver's social media activity and provide relevant advice when providing safe driving advice. The safe driving advice unit can provide relevant safe driving advice based on the driver's social media check-in information, for example. The safe driving advice unit can provide relevant safe driving advice based on the driver's social media interests and concerns. The safe driving advice unit can provide relevant safe driving advice by referring to the activities of the driver's friends on social media. The safe driving advice unit can analyze the driver's social media activity and provide relevant safe driving advice using the generation AI. For example, the generation AI can analyze the content of social media posts and check-in information to provide safe driving advice based on the driver's interests and concerns. This makes it possible to provide relevant safe driving advice by analyzing social media activity.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The reception unit can monitor the driver's health condition and adjust the destination setting input method based on the health condition. For example, it can monitor the driver's heart rate and stress level, and if the driver's health condition is deteriorating, it can provide a simple interface and minimize the input steps. If the health condition is good, it can provide detailed input options and suggest a customizable input method. This makes it possible to set a destination more comfortably by providing an input method according to the driver's health condition.

[0062] The traffic information collection unit can learn the driving style of the driver and adjust the method of collecting traffic information based on the driving style. For example, if the driver prefers high-speed driving, the smoothest route is collected preferentially. If the driver places importance on safe driving, information on routes with less traffic volume is collected. If the driver prefers enjoying scenery, information on routes with good scenery is collected. This makes it possible to provide more appropriate route guidance by providing a method of collecting traffic information according to the driver's driving style.

[0063] The route guidance unit can analyze the driver's driving history and suggest the optimal route based on the driving history. For example, it suggests the optimal route based on routes the driver has used in the past. It suggests a route that avoids congestion based on the driver's past driving history. It analyzes the driver's past driving history and suggests the most efficient route. In this way, it is possible to suggest the optimal route by referring to the past driving history.

[0064] The vehicle information providing unit can learn the driving style of the driver and adjust the method of providing vehicle information based on the driving style. For example, if the driver places importance on fuel efficiency, information on fuel consumption is provided with priority. If the driver places importance on safe driving, information on tire pressure and brake status is provided with priority. If the driver places importance on performance, information on engine status and acceleration is provided with priority. In this way, more appropriate vehicle information can be provided by providing an information providing method according to the driver's driving style.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reception unit accepts destination setting input from the driver. For example, the driver can set the destination in natural language, such as "I want to go to the next gas station." The reception unit uses a generative AI to analyze the driver's voice input and touch panel input, and sets the destination. Step 2: The traffic information collection unit collects real-time traffic information based on the information received by the reception unit. For example, the traffic information collection unit collects congestion information, accident information, road construction information, etc., and provides information for proposing optimal routes. The traffic information collection unit uses a generation AI to collect data from traffic information databases on the Internet and sensors, and updates it in real time. Step 3: The route guidance unit suggests the optimal route based on the information collected by the traffic information collection unit. For example, the route guidance unit suggests the optimal route to the driver based on criteria such as the shortest distance, shortest time, and route with the least traffic. The route guidance unit uses a generation AI to analyze the collected traffic information and calculate the optimal route. Step 4: The vehicle information providing unit provides vehicle information based on the route proposed by the route guidance unit. For example, the vehicle information providing unit provides the driver with information such as the remaining fuel, tire pressure, and engine status. The vehicle information providing unit uses a generative AI to analyze data from the vehicle's sensors and monitor the vehicle's status in real time. Step 5: The safe driving advice unit provides safe driving advice based on the information provided by the vehicle information provision unit. For example, the safe driving advice unit advises the driver to avoid sudden braking and sudden acceleration. The safe driving advice unit uses the generation AI to analyze the driver's driving behavior and provide advice for safe driving.

[0067] (Example 2) A driving assistant system according to an embodiment of the present invention is a system that uses a generating AI to provide a car navigation system and a conversational driving assistant service. In this system, the driver sets a destination in natural language, and the generating AI collects real-time traffic information, proposes an optimal route, provides vehicle information, and provides safe driving advice. For example, when the driver inputs an instruction such as "I want to go to the next gas station" to the generating AI, the generating AI analyzes this instruction and calculates the optimal route. Next, the generating AI collects real-time traffic information and provides route guidance. For example, the optimal route is proposed based on traffic congestion information and accident information. This allows the driver to reach the destination smoothly. In addition, the generating AI provides vehicle information. For example, the generating AI informs the driver of information such as the remaining fuel level and tire pressure. This allows the driver to always be aware of the vehicle's condition. The generating AI also provides safe driving advice. For example, the generating AI advises the driver to avoid sudden braking and sudden acceleration. This allows the driver to drive safely. This allows the driving assistant system to achieve a convenient driving experience and improved safety.

[0068] The driving assistant system according to the embodiment includes a reception unit, a traffic information collection unit, a route guidance unit, a vehicle information provision unit, and a safe driving advice unit. The reception unit receives an input of a destination setting from the driver. For example, the driver can set the destination in natural language such as "I want to go to the next gas station." The reception unit uses a generation AI to analyze the driver's voice input or touch panel input and set the destination. The traffic information collection unit collects real-time traffic information based on the information received by the reception unit. For example, the traffic information collection unit collects congestion information, accident information, road construction information, etc., and provides information for proposing an optimal route. The traffic information collection unit uses a generation AI to collect data from a traffic information database on the Internet and sensors, and updates the data in real time. The route guidance unit proposes an optimal route based on the information collected by the traffic information collection unit. For example, the route guidance unit proposes an optimal route to the driver based on criteria such as the shortest distance, the shortest time, and a route with low traffic volume. The route guidance unit uses a generation AI to analyze the collected traffic information and calculate an optimal route. The vehicle information providing unit provides vehicle information based on the route proposed by the route guidance unit. For example, the vehicle information providing unit provides information such as the remaining amount of fuel, tire pressure, and engine condition to the driver. The vehicle information providing unit uses the generation AI to analyze data from the vehicle's sensors and monitor the vehicle's condition in real time. The safe driving advice unit provides safe driving advice based on the information provided by the vehicle information providing unit. For example, the safe driving advice unit advises the driver to avoid sudden braking and sudden acceleration. The safe driving advice unit uses the generation AI to analyze the driver's driving behavior and provide advice for safe driving. As a result, the driving assistant system according to the embodiment can consistently perform operations from setting the driver's destination to collecting traffic information, proposing routes, providing vehicle information, and providing safe driving advice.

[0069] The reception unit accepts input of destination setting from the driver. For example, the driver can set the destination in natural language such as "I want to go to the next gas station." The reception unit uses the generation AI to analyze the driver's voice input and touch panel input to set the destination. Specifically, the generation AI uses voice recognition technology to convert the driver's voice into text, and analyzes the text to identify the destination. For example, if the driver says "I want to go to a nearby restaurant," the generation AI extracts the keyword "nearby restaurant" and searches for the restaurant closest to the current location. In addition, in the case of touch panel input, when the driver inputs the destination using the on-screen keyboard, the generation AI analyzes the input content and sets the appropriate destination. Furthermore, the generation AI can learn the past input history and the driver's preferences to set the destination with higher accuracy. For example, the driver can reduce the effort of setting the destination by preferentially suggesting places that he or she has frequently visited in the past. This allows the reception unit to accurately understand the driver's intentions and set the destination quickly and efficiently.

[0070] The traffic information collection unit collects real-time traffic information based on the information received by the reception unit. For example, the traffic information collection unit collects congestion information, accident information, road construction information, etc., and provides information for proposing optimal routes. The traffic information collection unit uses the generation AI to collect data from traffic information databases and sensors on the Internet and updates it in real time. Specifically, the generation AI acquires data from multiple traffic information services on the Internet, integrates the data, and centrally manages it. It also collects data from sensors and cameras installed on roads to grasp traffic conditions in real time. For example, it analyzes data from sensors to determine whether a specific road is congested, and analyzes camera footage to check whether there have been any accidents. Furthermore, the generation AI can predict traffic patterns for specific times of day and days of the week based on past traffic data, and evaluate the risk of future congestion and accidents. This allows the traffic information collection unit to build a foundation for always providing the latest traffic information and proposing optimal routes to drivers.

[0071] The route guidance unit proposes an optimal route based on the information collected by the traffic information collection unit. For example, the route guidance unit proposes an optimal route to the driver based on criteria such as the shortest distance, the shortest time, and a route with less traffic. The route guidance unit uses the generation AI to analyze the collected traffic information and calculate the optimal route. Specifically, the generation AI analyzes the collected traffic information in real time and calculates the optimal route taking into account the current traffic situation. For example, it can propose a detour route to avoid roads with congestion. In addition, the generation AI can learn the driver's past driving history and preferences and propose the optimal route for each individual driver. For example, if the driver prefers highways, the generation AI will propose a route that includes highways preferentially. In addition, the generation AI can propose the optimal route by taking into account factors such as weather, time of day, and day of the week. For example, it proposes a route that avoids slippery roads in rainy weather, and proposes a route with less traffic during rush hour. As a result, the route guidance unit can always provide the driver with the optimal route and support comfortable and efficient driving.

[0072] The vehicle information providing unit provides vehicle information based on the route proposed by the route guidance unit. For example, the vehicle information providing unit provides the driver with information such as the remaining amount of fuel, tire pressure, and engine condition. The vehicle information providing unit uses the generating AI to analyze data from the vehicle's sensors and monitor the vehicle's condition in real time. Specifically, the generating AI collects data from various sensors mounted on the vehicle and analyzes the data to evaluate the vehicle's condition. For example, the generating AI analyzes data from the fuel sensor to check the remaining amount of fuel, and analyzes data from the tire pressure sensor to evaluate the tire condition. In addition, in order to monitor the engine condition, the generating AI analyzes data from the engine sensor and issues a warning to the driver if an abnormality is detected. Furthermore, the generating AI can predict the vehicle's maintenance schedule based on past data and suggest maintenance at an appropriate time. As a result, the vehicle information providing unit can provide the driver with the vehicle's condition in real time and support safe and comfortable driving.

[0073] The safe driving advice unit provides safe driving advice based on the information provided by the vehicle information provision unit. For example, the safe driving advice unit advises the driver to avoid sudden braking and sudden acceleration. The safe driving advice unit uses the generation AI to analyze the driver's driving behavior and provide advice for safe driving. Specifically, the generation AI analyzes data from the vehicle's sensors and evaluates the driver's driving pattern. For example, if sudden braking and sudden acceleration occur frequently, the generation AI identifies the cause and proposes specific improvement measures to the driver. In addition, the generation AI can learn the driver's driving history and provide optimal advice to each individual driver. For example, if a particular driver tends to brake suddenly in a particular situation, advice is provided to avoid that situation. Furthermore, the safe driving advice unit can suggest appropriate driving methods to the driver based on traffic information and weather information updated in real time. For example, the safe driving advice unit advises the driver to avoid slippery roads in rainy weather and suggests avoiding unreasonable lane changes in traffic jams. As a result, the safe driving advice unit can always provide optimal driving advice to the driver and support safe and comfortable driving.

[0074] The reception unit can receive input from the driver in natural language. For example, the reception unit can allow the driver to set a destination in natural language, such as "I want to go to the next gas station." The reception unit uses the generation AI to analyze the driver's voice input or touch panel input and set the destination. For example, the generation AI can convert the driver's voice into text using voice recognition technology and analyze the text to identify the destination. The generation AI can also use natural language processing technology to understand the driver's intentions and set an appropriate destination. This allows the driver to set a destination in natural language, improving usability.

[0075] The traffic information collection unit can collect real-time traffic information. For example, the traffic information collection unit collects congestion information, accident information, road construction information, and the like, and provides information for proposing an optimal route. The traffic information collection unit uses the generation AI to collect data from a traffic information database and sensors on the Internet, and updates it in real time. For example, the traffic information collection unit obtains the latest traffic information from a traffic information database, and the generation AI analyzes the information and provides it to the driver. In addition, the traffic information collection unit collects data from sensors installed on the road, and the generation AI analyzes the data to grasp real-time traffic conditions. This allows the traffic information collection unit to provide route guidance based on the latest traffic conditions.

[0076] The route guidance unit can suggest an optimal route based on the collected information. The route guidance unit suggests an optimal route to the driver based on criteria such as the shortest distance, the shortest time, and a route with low traffic volume. The route guidance unit uses the generation AI to analyze the collected traffic information and calculate the optimal route. For example, the generation AI calculates the optimal route based on congestion information and accident information obtained from a traffic information database. The generation AI can also suggest the optimal route by taking into account the driver's past driving history and current driving conditions. This allows the route guidance unit to suggest the optimal route so that the driver can reach the destination smoothly.

[0077] The vehicle information providing unit can provide vehicle information such as the remaining amount of fuel and tire pressure. The vehicle information providing unit provides the driver with information such as the remaining amount of fuel, tire pressure, and engine status. The vehicle information providing unit uses the generating AI to analyze data from the vehicle's sensors and monitor the vehicle's status in real time. For example, the generating AI can analyze data from a fuel sensor to inform the driver of the remaining amount of fuel. The generating AI can also analyze data from a tire pressure sensor to inform the driver of the tire pressure. This allows the driver to always be aware of the vehicle's status and perform appropriate maintenance.

[0078] The safe driving advice unit can advise the driver to avoid sudden braking and sudden acceleration. The safe driving advice unit advises the driver, for example, to avoid sudden braking and sudden acceleration. The safe driving advice unit uses the generation AI to analyze the driving behavior of the driver and provide advice for safe driving. For example, the generation AI analyzes data from the vehicle's acceleration sensor to detect sudden braking and sudden acceleration and provide advice to the driver. In addition, the generation AI can analyze the driver's driving history to grasp the tendency of sudden braking and sudden acceleration and provide appropriate advice. This enables the driver to drive safely.

[0079] The reception unit can estimate the driver's emotion and adjust the input method of destination setting based on the estimated driver's emotion. For example, when the driver is stressed, the reception unit provides a simple interface and minimizes input procedures. When the driver is relaxed, the reception unit provides detailed input options and suggests a customizable input method. When the driver is in a hurry, the reception unit prioritizes voice input and enables the driver to input the destination quickly. The estimation of emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables a more comfortable destination setting by providing an input method according to the driver's emotion.

[0080] The reception unit can analyze the driver's past destination setting history and suggest the optimal input method. For example, the reception unit automatically displays destinations that the driver has frequently input in the past as candidates. It preferentially suggests input methods (voice, text, etc.) that the driver has used in the past. It predicts and suggests destinations to be used during a specific time period from the driver's past destination setting history. The reception unit uses the generation AI to analyze the driver's past destination setting history and suggests the optimal input method. For example, the generation AI can suggest the optimal input method by analyzing past history data and learning the driver's input patterns. This makes destination setting more efficient by suggesting the optimal input method based on past history.

[0081] The reception unit can perform filtering based on the driver's current driving situation and areas of interest when inputting destination settings. For example, when the driver inputs while driving, the reception unit prioritizes voice input and minimizes visual operations. Based on the driver's areas of interest (e.g., restaurants, gas stations, etc.), relevant destinations are preferentially displayed. Based on the driver's current driving situation (e.g., driving on a highway, in a traffic jam, etc.), an appropriate destination is proposed. The reception unit uses the generation AI to analyze the driver's current driving situation and areas of interest, and filters and proposes optimal destinations. For example, the generation AI can prioritize displaying highly relevant destinations based on the driver's driving situation data and past search history. As a result, more appropriate destinations can be proposed by filtering based on the driving situation and areas of interest.

[0082] The reception unit can estimate the driver's emotion and determine the priority of the input destinations based on the estimated driver's emotion. For example, when the driver is tired, the reception unit preferentially suggests the closest destination. When the driver is relaxed, the reception unit preferentially suggests a destination based on the driver's interest. When the driver is in a hurry, the reception unit preferentially suggests a destination that can be reached in the shortest time. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a more appropriate destination can be proposed by setting a priority according to the driver's emotion.

[0083] The reception unit can preferentially suggest highly relevant destinations in consideration of the driver's geographical location information when inputting destination settings. For example, the reception unit preferentially displays destinations close to the driver's current location. Based on the driver's current location, the reception unit proposes the most accessible destination. Based on the driver's current location and past movement history, the reception unit proposes highly relevant destinations. The reception unit uses the generation AI to analyze the driver's geographical location information and preferentially propose highly relevant destinations. For example, the generation AI can identify and propose the destination closest to the driver's current location based on GPS data and map information. This makes it possible to propose highly relevant destinations by taking into account the geographical location information.

[0084] The reception unit can analyze the driver's social media activity and suggest related destinations when the destination setting is input. For example, the reception unit can suggest related destinations based on the location where the driver has checked in on social media. The reception unit can suggest related destinations based on the driver's interests and concerns on social media. The reception unit can suggest related destinations based on the activities of the driver's friends on social media. The reception unit can analyze the driver's social media activity and suggest related destinations using the generation AI. For example, the generation AI can analyze the content of social media posts and check-in information to suggest destinations based on the driver's interests and concerns. This makes it possible to suggest related destinations by analyzing social media activity.

[0085] The traffic information collection unit can estimate the driver's emotion and adjust the method of collecting traffic information based on the estimated driver's emotion. For example, when the driver is stressed, the traffic information collection unit preferentially collects the smoothest route. When the driver is relaxed, the traffic information collection unit collects information on scenic routes. When the driver is in a hurry, the traffic information collection unit collects information on routes that can be reached in the shortest time. The estimation of emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more appropriate route guidance by providing a method of collecting traffic information according to the driver's emotion.

[0086] When collecting traffic information, the traffic information collection unit can predict the current traffic situation by referring to past traffic data. The traffic information collection unit, for example, predicts the current congestion situation based on past traffic congestion data. The traffic information collection unit predicts the current accident risk based on past accident data. The traffic information collection unit predicts the current traffic volume based on past traffic volume data. The traffic information collection unit analyzes the past traffic data using the generation AI to predict the current traffic situation. For example, the generation AI can learn the past traffic data and construct an algorithm that predicts the current traffic situation. This makes it possible to predict the current traffic situation by referring to the past traffic data, enabling more accurate route guidance.

[0087] The traffic information collection unit can improve the accuracy of collection by taking into account specific events and weather information when collecting traffic information. The traffic information collection unit predicts traffic conditions, for example, based on a specific event (e.g., a sporting event or a concert). The traffic information collection unit predicts traffic conditions by taking into account weather information (e.g., rain or snow). The traffic information collection unit predicts traffic conditions based on a specific season (e.g., summer vacation or New Year's holidays). The traffic information collection unit uses the generation AI to analyze specific events and weather information and predict traffic conditions. For example, the generation AI can construct an algorithm that predicts traffic conditions based on event schedules and weather data. As a result, by taking into account specific events and weather information, the accuracy of collection is improved, enabling more accurate route guidance.

[0088] The traffic information collection unit can estimate the driver's emotion and determine the priority of traffic information to be collected based on the estimated driver's emotion. For example, when the driver is stressed, the traffic information collection unit prioritizes collecting information on the smoothest route. When the driver is relaxed, the traffic information collection unit prioritizes collecting information on a scenic route. When the driver is in a hurry, the traffic information collection unit prioritizes collecting information on a route that can be reached in the shortest time. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows more appropriate traffic information to be collected by setting a priority according to the driver's emotion.

[0089] The traffic information collection unit can prioritize collecting highly relevant information in consideration of the driver's geographical location information when collecting traffic information. The traffic information collection unit collects the most relevant traffic information, for example, based on the driver's current location. The traffic information collection unit collects the most relevant traffic information in consideration of the driver's current location and past movement history. The traffic information collection unit collects the most relevant traffic information based on the driver's current location and destination. The traffic information collection unit uses the generation AI to analyze the driver's geographical location information and prioritize collecting highly relevant traffic information. For example, the generation AI can identify and collect the most relevant traffic information from the driver's current location based on GPS data and map information. This makes it possible to collect highly relevant traffic information by considering the geographical location information.

[0090] The traffic information collection unit can analyze the driver's social media activity and collect related traffic information when collecting traffic information. The traffic information collection unit can collect related traffic information based on the driver's check-in information on social media, for example. The traffic information collection unit can collect related traffic information based on the driver's interests and concerns on social media. The traffic information collection unit can collect related traffic information by referring to the activities of the driver's friends on social media. The traffic information collection unit can analyze the driver's social media activity and collect related traffic information using the generation AI. For example, the generation AI can analyze the content of social media posts and check-in information to collect traffic information based on the driver's interests and concerns. This makes it possible to collect related traffic information by analyzing social media activity.

[0091] The route guidance unit can estimate the driver's emotion and adjust the route guidance method based on the estimated driver's emotion. For example, when the driver is stressed, the route guidance unit provides simple and highly visible route guidance. When the driver is relaxed, the route guidance unit provides route guidance including detailed information. When the driver is in a hurry, the route guidance unit provides route guidance that focuses on the main points. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more comfortable route guidance by providing a route guidance method according to the driver's emotion.

[0092] When providing route guidance, the route guidance unit can suggest the optimal route by referring to past route guidance history. For example, the route guidance unit suggests the optimal route based on routes the driver has used in the past. It suggests a route that avoids congestion based on the driver's past route guidance history. It analyzes the driver's past route guidance history and suggests the most efficient route. The route guidance unit uses the generation AI to analyze the past route guidance history and suggest the optimal route. For example, the generation AI can suggest the optimal route by analyzing past history data and learning the driver's driving patterns. This makes it possible to suggest the optimal route by referring to past route guidance history.

[0093] The route guidance unit can improve the accuracy of the route by taking into account specific events and weather information when providing route guidance. The route guidance unit, for example, proposes an optimal route based on a specific event (e.g., a sporting event or a concert). The route guidance unit proposes an optimal route by taking into account weather information (e.g., rain or snow). The route guidance unit proposes an optimal route based on a specific season (e.g., summer vacation or New Year's holidays). The route guidance unit uses the generation AI to analyze specific events and weather information and propose an optimal route. For example, the generation AI can build an algorithm that proposes an optimal route based on event schedules and weather data. As a result, by taking into account specific events and weather information, the accuracy of the route is improved, enabling more accurate route guidance.

[0094] The route guidance unit can estimate the driver's emotion and determine the priority of the proposed route based on the estimated driver's emotion. For example, when the driver is stressed, the route guidance unit preferentially suggests the smoothest route. When the driver is relaxed, the route guidance unit preferentially suggests a scenic route. When the driver is in a hurry, the route guidance unit preferentially suggests a route that can be reached in the shortest time. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows a more appropriate route to be proposed by setting a priority according to the driver's emotion.

[0095] The route guidance unit can propose the optimal route by taking into account the driver's geographical location information when providing route guidance. The route guidance unit proposes the most relevant route based on the driver's current location, for example. The route guidance unit proposes the most efficient route by taking into account the driver's current location and past movement history. The route guidance unit proposes the most relevant route based on the driver's current location and destination. The route guidance unit uses the generation AI to analyze the driver's geographical location information and propose the optimal route. For example, the generation AI can identify and propose the most relevant route from the driver's current location based on GPS data and map information. This makes it possible to propose the optimal route by taking into account the geographical location information.

[0096] The route guidance unit can analyze the driver's social media activity and suggest relevant routes when providing route guidance. The route guidance unit can, for example, suggest relevant routes based on the driver's social media check-in information. The route guidance unit can suggest relevant routes based on the driver's social media interests and concerns. The route guidance unit can suggest relevant routes by referring to the activities of the driver's friends on social media. The route guidance unit can analyze the driver's social media activity and suggest relevant routes using a generation AI. For example, the generation AI can analyze the content of social media posts and check-in information to suggest routes based on the driver's interests and concerns. This makes it possible to suggest relevant routes by analyzing social media activity.

[0097] The vehicle information providing unit can estimate the driver's emotion and adjust the method of providing vehicle information based on the estimated driver's emotion. For example, when the driver is stressed, the vehicle information providing unit provides only important information concisely. When the driver is relaxed, the vehicle information providing unit provides detailed vehicle information. When the driver is in a hurry, the vehicle information providing unit provides the most important information quickly. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to provide more appropriate vehicle information by providing an information provision method according to the driver's emotion.

[0098] When providing vehicle information, the vehicle information providing unit can provide optimal information by referring to past vehicle information history. The vehicle information providing unit, for example, predicts and provides the current remaining fuel level based on past fuel consumption data. Provides the current tire condition based on past tire pressure data. Provides the next maintenance date based on past vehicle maintenance history. The vehicle information providing unit uses the generation AI to analyze past vehicle information history and provide optimal information. For example, the generation AI can provide optimal information by analyzing past history data and predicting the vehicle condition. This makes it possible to provide optimal information by referring to past vehicle information history.

[0099] The vehicle information providing unit can improve the accuracy of the information by taking into account specific events and weather information when providing vehicle information. For example, the vehicle information providing unit provides a fuel consumption forecast based on a specific event (e.g., a long-distance drive). Provides tire conditions taking into account weather information (e.g., rain or snow). Provides battery conditions based on a specific season (e.g., winter). The vehicle information providing unit uses the generating AI to analyze specific events and weather information and provide vehicle information. For example, the generating AI can construct an algorithm that predicts the vehicle condition based on an event schedule and weather data. As a result, by taking into account specific events and weather information, the accuracy of the information is improved, and more accurate vehicle information can be provided.

[0100] The vehicle information providing unit can estimate the driver's emotion and determine the priority of the vehicle information to be provided based on the estimated driver's emotion. For example, when the driver is stressed, the vehicle information providing unit provides the most important vehicle information with priority. When the driver is relaxed, the vehicle information providing unit provides detailed vehicle information. When the driver is in a hurry, the vehicle information providing unit provides information that needs to be provided quickly with priority. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows more appropriate vehicle information to be provided by setting a priority according to the driver's emotion.

[0101] The vehicle information providing unit can provide optimal information by taking into consideration the geographical location information of the driver when providing vehicle information. The vehicle information providing unit provides the most relevant vehicle information based on, for example, the driver's current location. The vehicle information providing unit provides the most relevant vehicle information by taking into consideration the driver's current location and past movement history. The vehicle information providing unit provides the most relevant vehicle information based on the driver's current location and destination. The vehicle information providing unit uses the generating AI to analyze the driver's geographical location information and provide optimal vehicle information. For example, the generating AI can identify and provide the most relevant vehicle information from the driver's current location based on GPS data and map information. This makes it possible to provide optimal vehicle information by taking into consideration the geographical location information.

[0102] The vehicle information providing unit can analyze the driver's social media activity and provide related vehicle information when providing vehicle information. The vehicle information providing unit can provide related vehicle information based on, for example, the driver's check-in information on social media. The vehicle information providing unit can provide related vehicle information based on the driver's interests and concerns on social media. The vehicle information providing unit can provide related vehicle information with reference to the activities of the driver's friends on social media. The vehicle information providing unit can analyze the driver's social media activity and provide related vehicle information using the generation AI. For example, the generation AI can analyze the content of social media posts and check-in information to provide vehicle information based on the driver's interests and concerns. This makes it possible to provide related vehicle information by analyzing social media activity.

[0103] The safe driving advice unit can estimate the driver's emotions and adjust the method of safe driving advice based on the estimated driver's emotions. For example, the safe driving advice unit provides advice in a calm voice when the driver is stressed; provides detailed advice when the driver is relaxed; and provides quick and concise advice when the driver is in a hurry. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to provide more appropriate safe driving advice by providing an advice method according to the driver's emotions.

[0104] When giving safe driving advice, the safe driving advice unit can provide optimal advice by referring to past driving history. The safe driving advice unit, for example, provides advice to avoid sudden braking based on past sudden braking data. Provides advice to avoid sudden acceleration based on past sudden acceleration data. Suggests the safest driving method based on past driving history. The safe driving advice unit uses a generation AI to analyze past driving history and provide optimal advice. For example, the generation AI can provide optimal advice by analyzing past history data and learning the driver's driving patterns. This makes it possible to provide optimal advice by referring to past driving history.

[0105] The safe driving advice unit can improve the accuracy of safe driving advice by taking into account specific events and weather information when providing safe driving advice. The safe driving advice unit provides safe driving advice based on, for example, a specific event (e.g., a long-distance drive). The safe driving advice unit provides safe driving advice by taking into account weather information (e.g., rain or snow). The safe driving advice unit provides safe driving advice based on a specific season (e.g., winter). The safe driving advice unit uses the generation AI to analyze specific events and weather information and provide safe driving advice. For example, the generation AI can construct an algorithm for providing safe driving advice based on event schedules and weather data. As a result, by taking into account specific events and weather information, the accuracy of advice is improved, and more accurate safe driving advice can be provided.

[0106] The safe driving advice unit can estimate the driver's emotions and determine the priority of advice to be provided based on the estimated driver's emotions. For example, when the driver is stressed, the safe driving advice unit provides the most important advice with priority. When the driver is relaxed, the safe driving advice unit provides detailed advice. When the driver is in a hurry, the safe driving advice unit provides advice that needs to be provided quickly with priority. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows more appropriate safe driving advice to be provided by setting a priority according to the driver's emotions.

[0107] The safe driving advice unit can provide optimal safe driving advice by taking into account the geographical location information of the driver when providing safe driving advice. The safe driving advice unit provides the most relevant safe driving advice based on, for example, the driver's current location. The safe driving advice unit provides the most relevant safe driving advice by taking into account the driver's current location and past driving history. The safe driving advice unit provides the most relevant safe driving advice based on the driver's current location and destination. The safe driving advice unit uses the generation AI to analyze the geographical location information of the driver and provide optimal safe driving advice. For example, the generation AI can identify and provide the most relevant safe driving advice from the driver's current location based on GPS data and map information. As a result, optimal safe driving advice can be provided by taking into account the geographical location information.

[0108] The safe driving advice unit can analyze the driver's social media activity and provide relevant advice when providing safe driving advice. The safe driving advice unit can provide relevant safe driving advice based on the driver's social media check-in information, for example. The safe driving advice unit can provide relevant safe driving advice based on the driver's social media interests and concerns. The safe driving advice unit can provide relevant safe driving advice by referring to the activities of the driver's friends on social media. The safe driving advice unit can analyze the driver's social media activity and provide relevant safe driving advice using the generation AI. For example, the generation AI can analyze the content of social media posts and check-in information to provide safe driving advice based on the driver's interests and concerns. This makes it possible to provide relevant safe driving advice by analyzing social media activity.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The driving assistant system may further include an entertainment providing unit. The entertainment providing unit may suggest optimal music or podcasts based on the driver's music preferences and past playback history. For example, music that allows the driver to relax during long-distance driving may be provided. The entertainment providing unit may also estimate the driver's emotions and provide appropriate entertainment content based on the estimated emotions. For example, relaxing music may be suggested when the driver is stressed, and energetic music may be suggested when the driver is relaxed. This allows the driver to improve comfort while driving by providing entertainment according to the driver's emotions.

[0111] The reception unit can monitor the driver's health condition and adjust the destination setting input method based on the health condition. For example, it can monitor the driver's heart rate and stress level, and if the driver's health condition is deteriorating, it can provide a simple interface and minimize the input steps. If the health condition is good, it can provide detailed input options and suggest a customizable input method. This makes it possible to set a destination more comfortably by providing an input method according to the driver's health condition.

[0112] The traffic information collection unit can learn the driving style of the driver and adjust the method of collecting traffic information based on the driving style. For example, if the driver prefers high-speed driving, the smoothest route is collected preferentially. If the driver places importance on safe driving, information on routes with less traffic volume is collected. If the driver prefers enjoying scenery, information on routes with good scenery is collected. This makes it possible to provide more appropriate route guidance by providing a method of collecting traffic information according to the driver's driving style.

[0113] The route guidance unit can analyze the driver's driving history and suggest the optimal route based on the driving history. For example, it suggests the optimal route based on routes the driver has used in the past. It suggests a route that avoids congestion based on the driver's past driving history. It analyzes the driver's past driving history and suggests the most efficient route. In this way, it is possible to suggest the optimal route by referring to the past driving history.

[0114] The vehicle information providing unit can learn the driving style of the driver and adjust the method of providing vehicle information based on the driving style. For example, if the driver places importance on fuel efficiency, information on fuel consumption is provided with priority. If the driver places importance on safe driving, information on tire pressure and brake status is provided with priority. If the driver places importance on performance, information on engine status and acceleration is provided with priority. In this way, more appropriate vehicle information can be provided by providing an information providing method according to the driver's driving style.

[0115] The safe driving advice unit can estimate the driver's emotions and adjust the content of advice based on the estimated driver's emotions. For example, if the driver is feeling stressed, advice on breathing techniques and stretching to relax is provided. If the driver is relaxed, advice on improving driving skills is provided. If the driver is in a hurry, advice on efficient driving methods is provided. In this way, more appropriate safe driving advice can be provided by providing advice according to the driver's emotions.

[0116] The reception unit can estimate the driver's emotions and adjust the destination setting input method based on the estimated driver's emotions. For example, if the driver is stressed, a simple interface is provided and the input steps are minimized. If the driver is relaxed, detailed input options are provided and a customizable input method is suggested. If the driver is in a hurry, voice input is prioritized to enable the destination to be input quickly. This makes it possible to set a destination more comfortably by providing an input method according to the driver's emotions.

[0117] The traffic information collection unit can estimate the driver's emotions and adjust the method of collecting traffic information based on the estimated driver's emotions. For example, if the driver is stressed, the smoothest route is preferentially collected. If the driver is relaxed, information on scenic routes is collected. If the driver is in a hurry, information on routes that can be reached in the shortest time is collected. This allows for more appropriate route guidance by providing a method of collecting traffic information according to the driver's emotions.

[0118] The route guidance unit can estimate the driver's emotions and adjust the route guidance method based on the estimated driver's emotions. For example, if the driver is stressed, simple and highly visible route guidance is provided. If the driver is relaxed, route guidance including detailed information is provided. If the driver is in a hurry, route guidance that focuses on the main points is provided. In this way, more comfortable route guidance can be provided by providing a route guidance method according to the driver's emotions.

[0119] The vehicle information providing unit can estimate the driver's emotions and adjust the method of providing vehicle information based on the estimated driver's emotions. For example, if the driver is stressed, only important information is provided concisely. If the driver is relaxed, detailed vehicle information is provided. If the driver is in a hurry, the most important information is provided quickly. In this way, more appropriate vehicle information can be provided by providing an information providing method according to the driver's emotions.

[0120] The process flow of the second embodiment will be briefly described below.

[0121] Step 1: The reception unit accepts destination setting input from the driver. For example, the driver can set the destination in natural language, such as "I want to go to the next gas station." The reception unit uses a generative AI to analyze the driver's voice input and touch panel input, and sets the destination. Step 2: The traffic information collection unit collects real-time traffic information based on the information received by the reception unit. For example, the traffic information collection unit collects congestion information, accident information, road construction information, etc., and provides information for proposing optimal routes. The traffic information collection unit uses a generation AI to collect data from traffic information databases on the Internet and sensors, and updates it in real time. Step 3: The route guidance unit suggests the optimal route based on the information collected by the traffic information collection unit. For example, the route guidance unit suggests the optimal route to the driver based on criteria such as the shortest distance, shortest time, and route with the least traffic. The route guidance unit uses a generation AI to analyze the collected traffic information and calculate the optimal route. Step 4: The vehicle information providing unit provides vehicle information based on the route proposed by the route guidance unit. For example, the vehicle information providing unit provides the driver with information such as the remaining fuel, tire pressure, and engine status. The vehicle information providing unit uses a generative AI to analyze data from the vehicle's sensors and monitor the vehicle's status in real time. Step 5: The safe driving advice unit provides safe driving advice based on the information provided by the vehicle information provision unit. For example, the safe driving advice unit advises the driver to avoid sudden braking and sudden acceleration. The safe driving advice unit uses the generation AI to analyze the driver's driving behavior and provide advice for safe driving.

[0122] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0124] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements including the above-mentioned reception unit, traffic information collection unit, route guidance unit, vehicle information provision unit, and safe driving advice unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and analyzes the driver's voice input and touch panel input. The traffic information collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects data from a traffic information database on the Internet and sensors. The route guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates the optimal route by analyzing the collected traffic information. The vehicle information provision unit is realized, for example, by the control unit 46A of the smart device 14, and analyzes data from the vehicle's sensors to monitor the vehicle's condition. The safe driving advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the driver's driving behavior to provide advice for safe driving. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0133] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0136] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0138] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements including the above-mentioned reception unit, traffic information collection unit, route guidance unit, vehicle information provision unit, and safe driving advice unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and analyzes the driver's voice input and touch panel input. The traffic information collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects data from a traffic information database on the Internet and sensors. The route guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates the optimal route by analyzing the collected traffic information. The vehicle information provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and analyzes data from the vehicle's sensors to monitor the vehicle's condition. The safe driving advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the driver's driving behavior to provide advice for safe driving. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0144] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0149] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0152] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0157] Each of the elements including the reception unit, the traffic information collection unit, the route guidance unit, the vehicle information provision unit, and the safe driving advice unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and analyzes the driver's voice input and touch panel input. The traffic information collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects data from a traffic information database on the Internet and from sensors. The route guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates the optimal route by analyzing the collected traffic information. The vehicle information provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and analyzes data from the vehicle's sensors to monitor the vehicle's condition. The safe driving advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the driver's driving behavior to provide advice for safe driving. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0162] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0165] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0166] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0169] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] Each of the elements including the reception unit, traffic information collection unit, route guidance unit, vehicle information provision unit, and safe driving advice unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and analyzes the driver's voice input and touch panel input. The traffic information collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects data from a traffic information database on the Internet and sensors. The route guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates the optimal route by analyzing the collected traffic information. The vehicle information provision unit is realized, for example, by the control unit 46A of the robot 414, and analyzes data from the vehicle's sensors to monitor the vehicle's condition. The safe driving advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the driver's driving behavior to provide advice for safe driving. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0175] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0176] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0177] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0178] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0179] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0180] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0183] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0184] In addition, 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 upon request from the data processing device 12.

[0185] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0186] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0187] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0188] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0189] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0190] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0191] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0192] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0193] (Appendix 1) A reception unit that receives an input of a destination setting; a traffic information collection unit that collects traffic information based on the information received by the reception unit; a route guidance unit that proposes a route based on the information collected by the traffic information collection unit; a vehicle information providing unit that provides vehicle information based on the route proposed by the route guiding unit; a safe driving advice unit that provides driving advice based on the information provided by the vehicle information providing unit. A system characterized by: (Appendix 2) The reception unit is Accepts natural language input from drivers 2. The system of claim 1, (Appendix 3) The traffic information collection unit, Collect real-time traffic information 2. The system of claim 1, (Appendix 4) The route guidance unit is Suggest optimal routes based on collected information 2. The system of claim 1, (Appendix 5) The vehicle information providing unit, Providing vehicle information such as fuel level and tire pressure 2. The system of claim 1, (Appendix 6) The safe driving advice unit Advise drivers to avoid sudden braking and acceleration 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the driver's emotions and adjust the way the destination setting is entered based on the estimated driver's emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the driver's past destination setting history and suggest the optimal input method 2. The system of claim 1, (Appendix 9) The reception unit is Filter destination settings based on the driver's current driving situation and interests 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the driver's emotions and prioritize input destinations based on the estimated driver's emotions 2. The system of claim 1, (Appendix 11) The reception unit is When entering destination settings, the system takes the driver's geographic location into account to prioritize and suggest relevant destinations 2. The system of claim 1, (Appendix 12) The reception unit is Analyzes the driver's social media activity to suggest relevant destinations when entering destination preferences 2. The system of claim 1, (Appendix 13) The traffic information collection unit, Estimate the driver's emotions and adjust the way traffic information is collected based on the estimated driver's emotions. 2. The system of claim 1, (Appendix 14) The traffic information collection unit, When collecting traffic information, past traffic data is referenced to predict current traffic conditions. 2. The system of claim 1, (Appendix 15) The traffic information collection unit, Improve the accuracy of traffic information collection by taking into account specific events and weather information 2. The system of claim 1, (Appendix 16) The traffic information collection unit, Estimate the driver's emotions and prioritize the traffic information to be collected based on the estimated driver's emotions. 2. The system of claim 1, (Appendix 17) The traffic information collection unit, When collecting traffic information, the system takes into account the driver's geographic location information to prioritize collecting information that is more relevant. 2. The system of claim 1, (Appendix 18) The traffic information collection unit, When collecting traffic information, analyze drivers' social media activities to collect relevant traffic information. 2. The system of claim 1, (Appendix 19) The route guidance unit is Estimate the driver's emotions and adjust route guidance based on the estimated driver's emotions 2. The system of claim 1, (Appendix 20) The route guidance unit is When providing route guidance, the system will suggest the best route by referencing the past route guidance history. 2. The system of claim 1, (Appendix 21) The route guidance unit is Improve route accuracy by taking into account certain events and weather information during route guidance 2. The system of claim 1, (Appendix 22) The route guidance unit is Estimate driver sentiment and prioritize route suggestions based on estimated driver sentiment 2. The system of claim 1, (Appendix 23) The route guidance unit is When providing directions, the system takes the driver's geographic location into account to suggest the best route 2. The system of claim 1, (Appendix 24) The route guidance unit is Analyzes the driver's social media activity and suggests relevant routes when giving directions 2. The system of claim 1, (Appendix 25) The vehicle information providing unit, Estimate the driver's emotions and adjust the way vehicle information is provided based on the estimated driver's emotions 2. The system of claim 1, (Appendix 26) The vehicle information providing unit, When providing vehicle information, provide optimal information by referencing past vehicle information history 2. The system of claim 1, (Appendix 27) The vehicle information providing unit, When providing vehicle information, the accuracy of the information will be improved by taking into account specific events and weather information. 2. The system of claim 1, (Appendix 28) The vehicle information providing unit, Estimate the driver's emotions and prioritize the vehicle information to be provided based on the estimated driver's emotions. 2. The system of claim 1, (Appendix 29) The vehicle information providing unit, When providing vehicle information, the driver's geographic location is taken into consideration to provide the most appropriate information 2. The system of claim 1, (Appendix 30) The vehicle information providing unit, When providing vehicle information, analyze the driver's social media activity and provide relevant vehicle information 2. The system of claim 1, (Appendix 31) The safe driving advice unit Estimate the driver's emotions and adjust the method of safe driving advice based on the estimated driver's emotions 2. The system of claim 1, (Appendix 32) The safe driving advice unit When providing safe driving advice, the system provides optimal advice by referring to past driving history. 2. The system of claim 1, (Appendix 33) The safe driving advice unit When providing safe driving advice, the accuracy of the advice will be improved by taking into account specific events and weather information. 2. The system of claim 1, (Appendix 34) The safe driving advice unit Estimate the driver's emotions and prioritize advice based on the estimated driver's emotions 2. The system of claim 1, (Appendix 35) The safe driving advice unit When providing safe driving advice, the system takes into account the driver's geographic location to provide optimal advice. 2. The system of claim 1, (Appendix 36) The safe driving advice unit Analyze drivers' social media activity and provide relevant advice when providing safe driving advice 2. The system of claim 1, [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives an input of a destination setting; a traffic information collection unit that collects traffic information based on the information received by the reception unit; a route guidance unit that proposes a route based on the information collected by the traffic information collection unit; a vehicle information providing unit that provides vehicle information based on the route proposed by the route guiding unit; a safe driving advice unit that provides driving advice based on the information provided by the vehicle information providing unit. A system characterized by:

2. The reception unit is Accepts natural language input from drivers 2. The system of claim 1 .

3. The traffic information collection unit, Collect real-time traffic information 2. The system of claim 1 .

4. The route guidance unit is Suggest optimal routes based on collected information 2. The system of claim 1 .

5. The vehicle information providing unit, Providing vehicle information such as fuel level and tire pressure 2. The system of claim 1 .

6. The safe driving advice unit Advise drivers to avoid sudden braking and acceleration 2. The system of claim 1 .

7. The reception unit is Estimate the driver's emotions and adjust the way the destination setting is entered based on the estimated driver's emotions 2. The system of claim 1 .

8. The reception unit is Analyze the driver's past destination setting history and suggest the optimal input method 2. The system of claim 1 .

Images