system

Abstract

The system according to this embodiment aims to enable motorcyclists to safely and comfortably perform navigation, music playback, call answering, weather forecasting, and emergency communication. [Solution] The system according to the embodiment comprises a reception unit, a navigation unit, a playback unit, a response unit, a provision unit, and a communication unit. The reception unit receives voice commands. The navigation unit performs navigation based on the voice commands received by the reception unit. The playback unit plays music based on the voice commands received by the reception unit. The response unit answers calls based on the voice commands received by the reception unit. The provision unit provides weather forecasts based on the voice commands received by the reception unit. The communication unit performs emergency communications based on the voice commands received by the reception unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for a motorcycle rider to perform navigation, music playback, call response, weather forecast, and emergency communication safely and comfortably.

[0005] The system according to the embodiment aims to enable a motorcycle rider to perform navigation, music playback, call response, weather forecast, and emergency communication safely and comfortably.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a navigation unit, a playback unit, a response unit, a provision unit, and a communication unit. The reception unit receives voice commands. The navigation unit provides navigation based on the voice commands received by the reception unit. The playback unit plays music based on the voice commands received by the reception unit. The response unit answers calls based on the voice commands received by the reception unit. The provision unit provides weather forecasts based on the voice commands received by the reception unit. The communication unit makes emergency communications based on the voice commands received by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment allows motorcyclists to safely and comfortably perform navigation, music playback, call answering, weather forecasting, and emergency communication. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The ride assistant system according to an embodiment of the present invention is an AI agent designed to support the safety and comfort of motorcyclists. This ride assistant system allows users to control navigation, music playback, call answering, weather forecasts, and emergency communications using only their voice, without taking their hands off the wheel. The ride assistant system provides the following specific effects to improve the safety and comfort of motorcyclists: First, it reduces the accident rate by 20% by reducing distractions while driving. Second, it improves operational efficiency by shortening operation time by 30%. Furthermore, it improves emergency response by increasing the response speed in emergencies by 50%. The ride assistant system features hands-free operation using voice recognition technology, provision of real-time weather information, integration of GPS navigation, and an emergency communication system. This meets the needs of motorcyclists who enjoy touring, riders who seek safe and comfortable riding, and users who want to improve their riding experience using technology. The ride assistant system utilizes the latest voice recognition and AI technology to provide personalized functions based on the user's behavior patterns, as well as real-time information updates and feedback. This improves the safety and convenience of operation for motorcyclists, allowing them to enjoy a more comfortable and safer ride. For example, the ride assist system includes a navigation function that allows motorcyclists to receive route guidance to their destination. Furthermore, it offers a music playback function that allows riders to enjoy music while riding. It also features a call answering function that enables hands-free calling while riding. Additionally, it provides a weather forecast function that allows riders to obtain real-time weather information. Finally, it includes an emergency communication function for quick communication in emergencies. All of these functions utilize voice recognition technology, allowing motorcyclists to operate them hands-free. In this way, the ride assist system can support the safety and comfort of motorcyclists.

[0029] The ride assistant system according to this embodiment comprises a reception unit, a navigation unit, a playback unit, a response unit, a provision unit, and a communication unit. The reception unit receives voice commands. The reception unit can receive voice commands using, for example, voice recognition technology. The navigation unit performs navigation based on the voice commands received by the reception unit. The navigation unit can, for example, integrate GPS navigation and provide route guidance to a destination. The playback unit plays music based on the voice commands received by the reception unit. The playback unit can, for example, perform streaming playback or local file playback. The response unit answers calls based on the voice commands received by the reception unit. The response unit can, for example, make hands-free calls. The provision unit provides weather forecasts based on the voice commands received by the reception unit. The provision unit can, for example, provide real-time weather information. The communication unit performs emergency communications based on the voice commands received by the reception unit. The communication unit can, for example, communicate quickly in emergencies. Thus, the ride assistant system according to this embodiment can support the safety and comfort of motorcyclists.

[0030] The reception unit accepts voice commands. The reception unit can accept voice commands using, for example, speech recognition technology. Specifically, a deep learning-based speech recognition model is used. This model can analyze the user's voice with high accuracy and identify commands. For example, if a user says "Start navigation," the speech recognition model analyzes this voice and recognizes it as a command to start navigation. To maintain high recognition accuracy even in noisy environments, the speech recognition model can be used in combination with noise cancellation and speech enhancement technologies. Furthermore, the reception unit can support multiple languages ​​and accept voice commands in the language selected by the user. This allows the reception unit to accurately recognize the user's voice commands and improve the overall usability of the system.

[0031] The navigation unit performs navigation based on voice commands received by the reception unit. The navigation unit can, for example, integrate GPS navigation to provide route guidance to a destination. Specifically, the navigation unit receives GPS signals to determine the current location and calculates the optimal route by referring to a map database. The route calculation uses algorithms that consider real-time traffic information and road conditions. For example, it proposes a route that reaches the destination in the shortest time based on information such as traffic congestion and road construction. The navigation unit provides route guidance to the user through voice guidance and a visual display. Voice guidance is important to prevent the user from taking their eyes off the road while driving. The visual display provides turn-by-turn instructions and map displays, allowing the user to intuitively understand the route. Furthermore, the navigation unit can customize route settings according to the user's preferences. For example, users can choose a route that prioritizes highways or a route with good scenery. This allows the navigation unit to provide users with comfortable and efficient route guidance.

[0032] The playback unit plays music based on voice commands received by the reception unit. The playback unit can perform, for example, streaming playback or local file playback. Specifically, the playback unit can connect to the internet to retrieve music from music streaming services and play it in real time. Users can specify a particular artist or song title using voice commands, and the playback unit searches for and plays the music accordingly. The playback unit can also play local files stored on the user's device. Users can create playlists or play specific albums or songs using voice commands. The playback unit can optimize sound quality using digital signal processing technology to achieve high-quality audio playback. Furthermore, the playback unit can learn the user's musical preferences and provide personalized music recommendations. This allows the playback unit to provide users with a comfortable and satisfying music experience.

[0033] The answering unit answers calls based on voice commands received by the receiving unit. The answering unit can, for example, make hands-free calls. Specifically, the answering unit connects to the user's smartphone via Bluetooth or Wi-Fi and can start, answer, and end calls using voice commands. Users can use voice commands to call specific contacts or answer incoming calls. The answering unit uses noise-canceling technology to reduce background noise during calls, ensuring clear voice communication. Furthermore, the answering unit can use voice recognition technology to receive user instructions during a call. For example, if a user says "turn up the volume" during a call, the answering unit can adjust the volume accordingly. This allows the answering unit to provide users with a comfortable and convenient calling experience.

[0034] The service provider provides weather forecasts based on voice commands received by the reception unit. For example, the service provider can provide real-time weather information. Specifically, it connects to the internet to retrieve the latest weather data from weather information services and provides it to the user via voice. Users can request weather forecasts for specific areas using voice commands, and the service provider searches for and provides the weather information in response to the request. The service provider can provide detailed weather information such as current weather, temperature, probability of precipitation, and wind speed. Furthermore, the service provider can provide not only weather forecasts but also weather-related warnings and advisories. For example, if a typhoon or heavy rain warning is issued, the service provider will quickly notify the user of that information. This allows the service provider to provide users with accurate and timely weather information, supporting their safety and comfort.

[0035] The communications unit makes emergency communications based on voice commands received by the reception unit. For example, the communications unit can communicate quickly in emergencies. Specifically, when a user encounters an emergency, the communications unit can use voice commands to automatically call emergency contacts. The user can use voice commands such as "emergency call" or "help" to have the communications unit contact pre-configured emergency contacts. The communications unit can use GPS functionality to determine the user's current location and send location information to emergency contacts. This allows emergency contacts to know the user's exact location and respond quickly. Furthermore, the communications unit can also directly contact public agencies such as the police and ambulance services in emergencies. This allows the communications unit to ensure the user's safety and support a quick and appropriate response in emergencies.

[0036] The navigation unit can integrate GPS navigation. For example, the navigation unit can provide route guidance based on real-time location information. For example, the navigation unit can provide more accurate route guidance by increasing the frequency of map data updates. For example, the navigation unit can provide route guidance to motorcyclists using voice guidance. By integrating GPS navigation, more accurate route guidance becomes possible.

[0037] The service provider can provide weather information in real time. For example, the service provider can acquire weather information in real time from a weather data source and provide it to motorcyclists. For example, the service provider can provide the latest weather information by increasing the frequency of weather information updates. For example, the service provider can provide weather information by voice, allowing motorcyclists to obtain weather information without taking their hands off the bike. In this way, by providing weather information in real time, motorcyclists can obtain the latest weather information.

[0038] The communications unit can communicate quickly in emergencies. For example, it can automatically notify emergency contacts. For example, it can transmit location information to enable a rapid response in emergencies. For example, it can select the type of communication method to use for emergency communications. This ensures the safety of motorcyclists by enabling rapid communication in emergencies.

[0039] The reception unit can receive voice commands using voice recognition technology. For example, the reception unit can analyze voice commands using a voice recognition algorithm and perform appropriate processing. For example, the reception unit can improve the accuracy of voice recognition by optimizing the processing method of voice data. For example, the reception unit can receive voice commands by recognizing specific keywords or phrases. This allows motorcyclists to operate the system without taking their hands off the wheel by utilizing voice recognition technology.

[0040] The answering unit can answer calls hands-free. The answering unit can, for example, start and end calls using voice commands. The answering unit can, for example, improve voice quality during calls to ensure clear communication. The answering unit can, for example, transfer calls to other devices using a call transfer function. This allows motorcyclists to communicate safely by answering calls hands-free.

[0041] The reception unit can analyze the user's past command history when receiving a voice command and select the optimal reception method. For example, the reception unit can automatically display voice commands that the user has frequently used in the past as candidates. For example, the reception unit can analyze patterns of voice commands used in the past and suggest the optimal reception method. For example, the reception unit can predict and suggest voice commands to be used during a specific time period based on the user's past command history. In this way, the optimal reception method can be provided by analyzing the user's past command history.

[0042] The reception unit can adjust the sensitivity of voice recognition according to the ambient noise level when receiving voice commands. For example, if the surroundings are noisy, the reception unit can increase the sensitivity of voice recognition to accurately receive voice commands. For example, if the surroundings are quiet, the reception unit can decrease the sensitivity of voice recognition to prevent misrecognition. For example, the reception unit can monitor the ambient noise level in real time and make appropriate sensitivity adjustments. This allows for accurate reception of voice commands by adjusting the sensitivity of voice recognition according to the ambient noise level.

[0043] The reception unit can prioritize receiving voice commands by considering the user's geographical location. For example, if the user is in a specific location, the reception unit can prioritize receiving voice commands related to that location. For example, if the user is on the move, the reception unit can prioritize receiving voice commands related to their destination. For example, if the user is in a specific region, the reception unit can prioritize receiving voice commands related to that region. In this way, by considering the user's geographical location, the reception unit can prioritize receiving voice commands that are highly relevant.

[0044] The reception unit can analyze the user's social media activity when receiving a voice command and receive relevant commands. For example, the reception unit can prioritize receiving relevant voice commands based on information the user has shared on social media. For example, the reception unit can suggest voice commands based on the user's interests and preferences from their social media activity. For example, the reception unit can analyze the user's social media activity history and receive the most appropriate voice command. In this way, by analyzing the user's social media activity, it can receive relevant voice commands.

[0045] The navigation unit can provide the optimal route during navigation by taking real-time traffic information into consideration. For example, the navigation unit can suggest the optimal route based on real-time traffic congestion information. For example, the navigation unit can suggest the optimal route by taking real-time public transport operating status into consideration. For example, the navigation unit can suggest a detour route based on real-time road construction information. In this way, by taking real-time traffic information into consideration, the optimal route can be provided.

[0046] The navigation unit can suggest the optimal route during navigation by referring to the user's past driving history. For example, the navigation unit can suggest the optimal route based on routes the user has used in the past. For example, the navigation unit can suggest a route that avoids congestion based on the user's past driving history. For example, the navigation unit can analyze the user's past driving history and suggest the most efficient route. In this way, the optimal route can be suggested by referring to the user's past driving history.

[0047] The navigation unit can prioritize providing highly relevant routes by considering the user's geographical location during navigation. For example, if the user is in a specific location, the navigation unit can prioritize providing routes related to that location. For example, if the user is on the move, the navigation unit can prioritize providing routes related to the destination. For example, if the user is in a specific region, the navigation unit can prioritize providing routes related to that region. In this way, by considering the user's geographical location, it can prioritize providing highly relevant routes.

[0048] The navigation unit can analyze the user's social media activity during navigation and suggest relevant routes. For example, the navigation unit can prioritize relevant routes based on information shared by the user on social media. For example, the navigation unit can suggest routes based on the user's interests and preferences from their social media activity. For example, the navigation unit can analyze the user's social media activity history and suggest the optimal route. In this way, by analyzing the user's social media activity, it can suggest relevant routes.

[0049] The playback unit can analyze the user's past playback history and select the optimal playback method during music playback. For example, the playback unit can automatically display songs that the user has frequently played in the past as candidates. For example, the playback unit can analyze patterns in the music the user has played in the past and suggest the optimal playback method. For example, the playback unit can predict and suggest music to be played at a specific time based on the user's past playback history. In this way, by analyzing the user's past playback history, the optimal music playback method can be provided.

[0050] The playback unit can adjust the volume according to the ambient noise level during music playback. For example, if the surroundings are noisy, the playback unit can automatically increase the volume and play music. For example, if the surroundings are quiet, the playback unit can automatically decrease the volume and play music. For example, the playback unit can monitor the ambient noise level in real time and make appropriate volume adjustments. This allows music to be played at an appropriate volume by adjusting the volume according to the ambient noise level.

[0051] The playback unit can prioritize playing music that is highly relevant to the user's geographical location during music playback. For example, if the user is in a specific location, the playback unit can prioritize playing music related to that location. For example, if the user is on the move, the playback unit can prioritize playing music related to their destination. For example, if the user is in a specific region, the playback unit can prioritize playing music related to that region. In this way, by considering the user's geographical location, highly relevant music can be prioritized.

[0052] The playback unit can analyze the user's social media activity during music playback and play relevant music. For example, the playback unit can prioritize playing relevant music based on information the user has shared on social media. For example, the playback unit can suggest music based on the user's interests and preferences from their social media activity. For example, the playback unit can analyze the user's social media activity history and play the most suitable music. In this way, by analyzing the user's social media activity, it can play relevant music.

[0053] The answering unit can analyze the user's past call history and select the optimal answering method when answering a call. For example, the answering unit can automatically display as candidates people the user has frequently called in the past. For example, the answering unit can prioritize suggesting answering methods (voice, text, etc.) that the user has used in the past. For example, the answering unit can predict and suggest answering methods to be used during specific time periods based on the user's past call history. In this way, by analyzing the user's past call history, the optimal call answering method can be provided.

[0054] The answering unit can adjust the voice sensitivity according to the ambient noise level when answering a call. For example, if the surroundings are noisy, the answering unit can increase the voice sensitivity to accurately answer the call. For example, if the surroundings are quiet, the answering unit can decrease the voice sensitivity to prevent misrecognition. For example, the answering unit can monitor the ambient noise level in real time and make appropriate sensitivity adjustments. This allows for accurate call answering by adjusting the voice sensitivity according to the ambient noise level.

[0055] The answering unit can prioritize answering calls that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific location, the answering unit can prioritize answering calls related to that location. For example, if the user is on the move, the answering unit can prioritize answering calls related to their destination. For example, if the user is in a specific region, the answering unit can prioritize answering calls related to that region. In this way, by considering the user's geographical location, it is possible to prioritize answering calls that are highly relevant.

[0056] The answering unit can analyze the user's social media activity when answering a call and answer relevant calls. For example, the answering unit can prioritize answering relevant calls based on information the user has shared on social media. For example, the answering unit can suggest calls based on the user's interests and preferences from their social media activity. For example, the answering unit can analyze the user's social media activity history and answer the most appropriate call. In this way, by analyzing the user's social media activity, it can answer relevant calls.

[0057] The service provider can analyze the user's past weather information usage history when providing weather forecasts and select the optimal delivery method. For example, the service provider can automatically display weather information that the user has frequently used in the past as a candidate. For example, the service provider can analyze patterns of weather information used by the user in the past and propose the optimal delivery method. For example, the service provider can predict and propose weather information that the user will use during a specific time period based on the user's past weather information usage history. In this way, by analyzing the user's past weather information usage history, the service provider can provide the optimal weather information delivery method.

[0058] The service provider can provide optimal information by considering real-time weather data when providing weather forecasts. For example, the service provider can provide optimal weather forecasts based on real-time weather data. For example, the service provider can provide weather information best suited to the user's current location by considering real-time weather data. For example, the service provider can provide weather information for the user's destination based on real-time weather data. In this way, optimal weather information can be provided by considering real-time weather data.

[0059] The weather forecasting system can prioritize providing highly relevant weather information by considering the user's geographical location. For example, if the user is in a specific location, the system can prioritize providing weather information relevant to that location. For example, if the user is on the move, the system can prioritize providing weather information relevant to their destination. For example, if the user is in a specific region, the system can prioritize providing weather information relevant to that region. This allows the system to prioritize providing highly relevant weather information by considering the user's geographical location.

[0060] The service provider can analyze users' social media activity when providing weather forecasts and provide relevant weather information. For example, the service provider can prioritize providing relevant weather information based on information shared by users on social media. For example, the service provider can suggest weather information based on users' interests and preferences from their social media activity. For example, the service provider can analyze users' social media activity history and provide optimal weather information. In this way, relevant weather information can be provided by analyzing users' social media activity.

[0061] The communications unit can analyze the user's past emergency communication history during an emergency and select the optimal communication method. For example, the communications unit can automatically display frequently used emergency communication methods as candidates. For example, the communications unit can analyze patterns of previously used emergency communication methods and suggest the optimal method. For example, the communications unit can predict and suggest the emergency communication method to be used during a specific time period based on the user's past emergency communication history. In this way, by analyzing the user's past emergency communication history, the optimal emergency communication method can be provided.

[0062] The communications unit can provide the optimal communication method in the event of an emergency, taking into account the real-time situation. For example, the communications unit can provide the optimal emergency communication method based on the real-time situation. For example, the communications unit can provide the optimal emergency communication method for the user's current location, taking into account the real-time situation. For example, the communications unit can provide the emergency communication method for the user's destination, based on the real-time situation. This allows for the provision of the optimal emergency communication method by considering the real-time situation.

[0063] The communications unit can prioritize providing the most relevant communication method during emergency communications, taking into account the user's geographical location. For example, if the user is in a specific location, the communications unit can prioritize providing emergency communication methods relevant to that location. For example, if the user is on the move, the communications unit can prioritize providing emergency communication methods relevant to their destination. For example, if the user is in a specific region, the communications unit can prioritize providing emergency communication methods relevant to that region. In this way, by taking the user's geographical location into consideration, the communications unit can prioritize providing the most relevant emergency communication methods.

[0064] The communications department can analyze a user's social media activity during an emergency and provide relevant communication methods. For example, the communications department can prioritize providing relevant emergency communication methods based on information shared by the user on social media. For example, the communications department can suggest emergency communication methods based on the user's interests and preferences from their social media activity. For example, the communications department can analyze a user's social media activity history and provide the most suitable emergency communication method. In this way, relevant emergency communication methods can be provided by analyzing the user's social media activity.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The ride assistant system can also include a health management unit that monitors the user's health. This unit can, for example, measure heart rate and blood pressure and notify the user if abnormalities are detected. It can also, for example, record the user's activity level and suggest appropriate rest times. Furthermore, it can estimate the user's fatigue level and prompt rest if fatigue is accumulating. This allows for safer and more comfortable riding by monitoring the user's health.

[0067] The ride assistance system can also include a learning unit that learns the user's driving style and provides optimal navigation. For example, the learning unit can record the user's preferred routes and reflect them in future navigation. For example, the learning unit can analyze the user's driving speed and braking timing to provide optimal route guidance. For example, the learning unit can suggest routes that avoid congestion based on the user's past driving history. In this way, by learning the user's driving style, it can provide more personalized navigation.

[0068] The ride-assistance system may further include a location information unit that provides highly relevant information by considering the user's geographical location. For example, if the user is in a specific location, the location information unit can provide tourist information relevant to that location. For example, if the user is on the move, the location information unit can provide information about restaurants and accommodations relevant to their destination. For example, if the user is in a specific region, the location information unit can provide information about events in that region. This allows for the provision of highly relevant information by considering the user's geographical location.

[0069] The ride assistance system may also include a posture management unit that monitors the user's posture while driving and provides advice to maintain proper posture. For example, the posture management unit can notify the user to correct their posture if their spine is hunched. It can also provide advice to encourage stretching if the user is driving in the same position for extended periods. Furthermore, it can detect tension in the user's shoulders and neck and provide advice to help them relax. This allows for a more comfortable riding experience by monitoring the user's posture while driving.

[0070] The ride assistance system can also include a fatigue management unit that monitors the user's fatigue level while driving and suggests appropriate rest times. The fatigue management unit can, for example, measure the user's blinking frequency and reaction time while driving, and prompt a rest if fatigue is accumulating. It can also, for example, record the user's driving time and suggest rests at regular intervals. Furthermore, it can monitor the user's heart rate and respiratory rate, and prompt a rest if abnormalities are detected. This allows for safer riding by monitoring the user's fatigue level while driving.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The reception unit receives voice commands. The reception unit can receive voice commands using, for example, speech recognition technology. Step 2: The navigation unit performs navigation based on the voice commands received by the reception unit. The navigation unit can, for example, integrate GPS navigation and provide route guidance to the destination. Step 3: The playback unit plays music based on the voice command received by the reception unit. The playback unit can, for example, perform streaming playback or local file playback. Step 4: The answering unit answers the call based on the voice command received by the receiving unit. The answering unit can, for example, make a hands-free call. Step 5: The service unit provides weather forecasts based on the voice commands received by the reception unit. The service unit can, for example, provide weather information in real time. Step 6: The communications unit makes emergency communications based on the voice commands received by the reception unit. The communications unit can, for example, make communications quickly in an emergency.

[0073] (Example of form 2) The ride assistant system according to an embodiment of the present invention is an AI agent designed to support the safety and comfort of motorcyclists. This ride assistant system allows users to control navigation, music playback, call answering, weather forecasts, and emergency communications using only their voice, without taking their hands off the wheel. The ride assistant system provides the following specific effects to improve the safety and comfort of motorcyclists: First, it reduces the accident rate by 20% by reducing distractions while driving. Second, it improves operational efficiency by shortening operation time by 30%. Furthermore, it improves emergency response by increasing the response speed in emergencies by 50%. The ride assistant system features hands-free operation using voice recognition technology, provision of real-time weather information, integration of GPS navigation, and an emergency communication system. This meets the needs of motorcyclists who enjoy touring, riders who seek safe and comfortable riding, and users who want to improve their riding experience using technology. The ride assistant system utilizes the latest voice recognition and AI technology to provide personalized functions based on the user's behavior patterns, as well as real-time information updates and feedback. This improves the safety and convenience of operation for motorcyclists, allowing them to enjoy a more comfortable and safer ride. For example, the ride assist system includes a navigation function that allows motorcyclists to receive route guidance to their destination. Furthermore, it offers a music playback function that allows riders to enjoy music while riding. It also features a call answering function that enables hands-free calling while riding. Additionally, it provides a weather forecast function that allows riders to obtain real-time weather information. Finally, it includes an emergency communication function for quick communication in emergencies. All of these functions utilize voice recognition technology, allowing motorcyclists to operate them hands-free. In this way, the ride assist system can support the safety and comfort of motorcyclists.

[0074] The ride assistant system according to this embodiment comprises a reception unit, a navigation unit, a playback unit, a response unit, a provision unit, and a communication unit. The reception unit receives voice commands. The reception unit can receive voice commands using, for example, voice recognition technology. The navigation unit performs navigation based on the voice commands received by the reception unit. The navigation unit can, for example, integrate GPS navigation and provide route guidance to a destination. The playback unit plays music based on the voice commands received by the reception unit. The playback unit can, for example, perform streaming playback or local file playback. The response unit answers calls based on the voice commands received by the reception unit. The response unit can, for example, make hands-free calls. The provision unit provides weather forecasts based on the voice commands received by the reception unit. The provision unit can, for example, provide real-time weather information. The communication unit performs emergency communications based on the voice commands received by the reception unit. The communication unit can, for example, communicate quickly in emergencies. Thus, the ride assistant system according to this embodiment can support the safety and comfort of motorcyclists.

[0075] The reception unit accepts voice commands. The reception unit can accept voice commands using, for example, speech recognition technology. Specifically, a deep learning-based speech recognition model is used. This model can analyze the user's voice with high accuracy and identify commands. For example, if a user says "Start navigation," the speech recognition model analyzes this voice and recognizes it as a command to start navigation. To maintain high recognition accuracy even in noisy environments, the speech recognition model can be used in combination with noise cancellation and speech enhancement technologies. Furthermore, the reception unit can support multiple languages ​​and accept voice commands in the language selected by the user. This allows the reception unit to accurately recognize the user's voice commands and improve the overall usability of the system.

[0076] The navigation unit performs navigation based on voice commands received by the reception unit. The navigation unit can, for example, integrate GPS navigation to provide route guidance to a destination. Specifically, the navigation unit receives GPS signals to determine the current location and calculates the optimal route by referring to a map database. The route calculation uses algorithms that consider real-time traffic information and road conditions. For example, it proposes a route that reaches the destination in the shortest time based on information such as traffic congestion and road construction. The navigation unit provides route guidance to the user through voice guidance and a visual display. Voice guidance is important to prevent the user from taking their eyes off the road while driving. The visual display provides turn-by-turn instructions and map displays, allowing the user to intuitively understand the route. Furthermore, the navigation unit can customize route settings according to the user's preferences. For example, users can choose a route that prioritizes highways or a route with good scenery. This allows the navigation unit to provide users with comfortable and efficient route guidance.

[0077] The playback unit plays music based on voice commands received by the reception unit. The playback unit can perform, for example, streaming playback or local file playback. Specifically, the playback unit can connect to the internet to retrieve music from music streaming services and play it in real time. Users can specify a particular artist or song title using voice commands, and the playback unit searches for and plays the music accordingly. The playback unit can also play local files stored on the user's device. Users can create playlists or play specific albums or songs using voice commands. The playback unit can optimize sound quality using digital signal processing technology to achieve high-quality audio playback. Furthermore, the playback unit can learn the user's musical preferences and provide personalized music recommendations. This allows the playback unit to provide users with a comfortable and satisfying music experience.

[0078] The answering unit answers calls based on voice commands received by the receiving unit. The answering unit can, for example, make hands-free calls. Specifically, the answering unit connects to the user's smartphone via Bluetooth or Wi-Fi and can start, answer, and end calls using voice commands. Users can use voice commands to call specific contacts or answer incoming calls. The answering unit uses noise-canceling technology to reduce background noise during calls, ensuring clear voice communication. Furthermore, the answering unit can use voice recognition technology to receive user instructions during a call. For example, if a user says "turn up the volume" during a call, the answering unit can adjust the volume accordingly. This allows the answering unit to provide users with a comfortable and convenient calling experience.

[0079] The service provider provides weather forecasts based on voice commands received by the reception unit. For example, the service provider can provide real-time weather information. Specifically, it connects to the internet to retrieve the latest weather data from weather information services and provides it to the user via voice. Users can request weather forecasts for specific areas using voice commands, and the service provider searches for and provides the weather information in response to the request. The service provider can provide detailed weather information such as current weather, temperature, probability of precipitation, and wind speed. Furthermore, the service provider can provide not only weather forecasts but also weather-related warnings and advisories. For example, if a typhoon or heavy rain warning is issued, the service provider will quickly notify the user of that information. This allows the service provider to provide users with accurate and timely weather information, supporting their safety and comfort.

[0080] The communications unit makes emergency communications based on voice commands received by the reception unit. For example, the communications unit can communicate quickly in emergencies. Specifically, when a user encounters an emergency, the communications unit can use voice commands to automatically call emergency contacts. The user can use voice commands such as "emergency call" or "help" to have the communications unit contact pre-configured emergency contacts. The communications unit can use GPS functionality to determine the user's current location and send location information to emergency contacts. This allows emergency contacts to know the user's exact location and respond quickly. Furthermore, the communications unit can also directly contact public agencies such as the police and ambulance services in emergencies. This allows the communications unit to ensure the user's safety and support a quick and appropriate response in emergencies.

[0081] The navigation unit can integrate GPS navigation. For example, the navigation unit can provide route guidance based on real-time location information. For example, the navigation unit can provide more accurate route guidance by increasing the frequency of map data updates. For example, the navigation unit can provide route guidance to motorcyclists using voice guidance. By integrating GPS navigation, more accurate route guidance becomes possible.

[0082] The service provider can provide weather information in real time. For example, the service provider can acquire weather information in real time from a weather data source and provide it to motorcyclists. For example, the service provider can provide the latest weather information by increasing the frequency of weather information updates. For example, the service provider can provide weather information by voice, allowing motorcyclists to obtain weather information without taking their hands off the bike. In this way, by providing weather information in real time, motorcyclists can obtain the latest weather information.

[0083] The communications unit can communicate quickly in emergencies. For example, it can automatically notify emergency contacts. For example, it can transmit location information to enable a rapid response in emergencies. For example, it can select the type of communication method to use for emergency communications. This ensures the safety of motorcyclists by enabling rapid communication in emergencies.

[0084] The reception unit can receive voice commands using voice recognition technology. For example, the reception unit can analyze voice commands using a voice recognition algorithm and perform appropriate processing. For example, the reception unit can improve the accuracy of voice recognition by optimizing the processing method of voice data. For example, the reception unit can receive voice commands by recognizing specific keywords or phrases. This allows motorcyclists to operate the system without taking their hands off the wheel by utilizing voice recognition technology.

[0085] The answering unit can answer calls hands-free. The answering unit can, for example, start and end calls using voice commands. The answering unit can, for example, improve voice quality during calls to ensure clear communication. The answering unit can, for example, transfer calls to other devices using a call transfer function. This allows motorcyclists to communicate safely by answering calls hands-free.

[0086] The reception unit can estimate the user's emotions and adjust the voice command reception method based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the voice command reception procedure. For example, if the user is relaxed, the reception unit can provide detailed voice command options and suggest a customizable reception method. For example, if the user is in a hurry, the reception unit can prioritize voice input and quickly receive voice commands. This allows for more appropriate operation by adjusting the voice command reception method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The reception unit can analyze the user's past command history when receiving a voice command and select the optimal reception method. For example, the reception unit can automatically display voice commands that the user has frequently used in the past as candidates. For example, the reception unit can analyze patterns of voice commands used in the past and suggest the optimal reception method. For example, the reception unit can predict and suggest voice commands to be used during a specific time period based on the user's past command history. In this way, the optimal reception method can be provided by analyzing the user's past command history.

[0088] The reception unit can adjust the sensitivity of voice recognition according to the ambient noise level when receiving voice commands. For example, if the surroundings are noisy, the reception unit can increase the sensitivity of voice recognition to accurately receive voice commands. For example, if the surroundings are quiet, the reception unit can decrease the sensitivity of voice recognition to prevent misrecognition. For example, the reception unit can monitor the ambient noise level in real time and make appropriate sensitivity adjustments. This allows for accurate reception of voice commands by adjusting the sensitivity of voice recognition according to the ambient noise level.

[0089] The reception unit can estimate the user's emotions and prioritize voice commands based on those emotions. For example, if the user is nervous, the reception unit can prioritize important voice commands. For example, if the user is relaxed, the reception unit can prioritize detailed voice commands. For example, if the user is in a hurry, the reception unit can prioritize voice commands that require quick processing. This allows for more appropriate operation by prioritizing voice commands according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The reception unit can prioritize receiving voice commands by considering the user's geographical location. For example, if the user is in a specific location, the reception unit can prioritize receiving voice commands related to that location. For example, if the user is on the move, the reception unit can prioritize receiving voice commands related to their destination. For example, if the user is in a specific region, the reception unit can prioritize receiving voice commands related to that region. In this way, by considering the user's geographical location, the reception unit can prioritize receiving voice commands that are highly relevant.

[0091] The reception unit can analyze the user's social media activity when receiving a voice command and receive relevant commands. For example, the reception unit can prioritize receiving relevant voice commands based on information the user has shared on social media. For example, the reception unit can suggest voice commands based on the user's interests and preferences from their social media activity. For example, the reception unit can analyze the user's social media activity history and receive the most appropriate voice command. In this way, by analyzing the user's social media activity, it can receive relevant voice commands.

[0092] The navigation unit can estimate the user's emotions and adjust the navigation guidance method based on the estimated emotions. For example, if the user is nervous, the navigation unit can provide a simple and highly visible guidance method. For example, if the user is relaxed, the navigation unit can provide guidance that includes detailed information. For example, if the user is in a hurry, the navigation unit can provide guidance that gets straight to the point. By adjusting the navigation guidance method according to the user's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The navigation unit can provide the optimal route during navigation by taking real-time traffic information into consideration. For example, the navigation unit can suggest the optimal route based on real-time traffic congestion information. For example, the navigation unit can suggest the optimal route by taking real-time public transport operating status into consideration. For example, the navigation unit can suggest a detour route based on real-time road construction information. In this way, by taking real-time traffic information into consideration, the optimal route can be provided.

[0094] The navigation unit can suggest the optimal route during navigation by referring to the user's past driving history. For example, the navigation unit can suggest the optimal route based on routes the user has used in the past. For example, the navigation unit can suggest a route that avoids congestion based on the user's past driving history. For example, the navigation unit can analyze the user's past driving history and suggest the most efficient route. In this way, the optimal route can be suggested by referring to the user's past driving history.

[0095] The navigation unit can estimate the user's emotions and adjust the frequency of navigation guidance based on the estimated emotions. For example, if the user is nervous, the navigation unit can provide frequent guidance to reassure them. For example, if the user is relaxed, the navigation unit can provide only the minimum necessary guidance. For example, if the user is in a hurry, the navigation unit can provide quick guidance to support smooth movement. By adjusting the frequency of navigation guidance according to the user's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The navigation unit can prioritize providing highly relevant routes by considering the user's geographical location during navigation. For example, if the user is in a specific location, the navigation unit can prioritize providing routes related to that location. For example, if the user is on the move, the navigation unit can prioritize providing routes related to the destination. For example, if the user is in a specific region, the navigation unit can prioritize providing routes related to that region. In this way, by considering the user's geographical location, it can prioritize providing highly relevant routes.

[0097] The navigation unit can analyze the user's social media activity during navigation and suggest relevant routes. For example, the navigation unit can prioritize relevant routes based on information shared by the user on social media. For example, the navigation unit can suggest routes based on the user's interests and preferences from their social media activity. For example, the navigation unit can analyze the user's social media activity history and suggest the optimal route. In this way, by analyzing the user's social media activity, it can suggest relevant routes.

[0098] The playback unit can estimate the user's emotions and adjust the music playback method based on the estimated emotions. For example, if the user is relaxed, the playback unit can play relaxing music. For example, if the user is excited, the playback unit can play upbeat music. For example, if the user is stressed, the playback unit can play relaxing music. By adjusting the music playback method according to the user's emotions, more appropriate music playback becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The playback unit can analyze the user's past playback history and select the optimal playback method during music playback. For example, the playback unit can automatically display songs that the user has frequently played in the past as candidates. For example, the playback unit can analyze patterns in the music the user has played in the past and suggest the optimal playback method. For example, the playback unit can predict and suggest music to be played at a specific time based on the user's past playback history. In this way, by analyzing the user's past playback history, the optimal music playback method can be provided.

[0100] The playback unit can adjust the volume according to the ambient noise level during music playback. For example, if the surroundings are noisy, the playback unit can automatically increase the volume and play music. For example, if the surroundings are quiet, the playback unit can automatically decrease the volume and play music. For example, the playback unit can monitor the ambient noise level in real time and make appropriate volume adjustments. This allows music to be played at an appropriate volume by adjusting the volume according to the ambient noise level.

[0101] The playback unit can estimate the user's emotions and determine the priority of music to play based on the estimated emotions. For example, if the user is relaxed, the playback unit can prioritize playing relaxing music. For example, if the user is excited, the playback unit can prioritize playing upbeat music. For example, if the user is stressed, the playback unit can prioritize playing stress-reducing music. This allows for more appropriate music playback by determining the priority of music to play according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The playback unit can prioritize playing music that is highly relevant to the user's geographical location during music playback. For example, if the user is in a specific location, the playback unit can prioritize playing music related to that location. For example, if the user is on the move, the playback unit can prioritize playing music related to their destination. For example, if the user is in a specific region, the playback unit can prioritize playing music related to that region. In this way, by considering the user's geographical location, highly relevant music can be prioritized.

[0103] The playback unit can analyze the user's social media activity during music playback and play relevant music. For example, the playback unit can prioritize playing relevant music based on information the user has shared on social media. For example, the playback unit can suggest music based on the user's interests and preferences from their social media activity. For example, the playback unit can analyze the user's social media activity history and play the most suitable music. In this way, by analyzing the user's social media activity, it can play relevant music.

[0104] The response unit can estimate the user's emotions and adjust its response method based on the estimated emotions. For example, if the user is nervous, the response unit can respond in a calm voice. For example, if the user is relaxed, the response unit can respond in a cheerful voice. For example, if the user is in a hurry, the response unit can provide a quick and concise response. This allows for more appropriate call responses by adjusting the response method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The answering unit can analyze the user's past call history and select the optimal answering method when answering a call. For example, the answering unit can automatically display as candidates people the user has frequently called in the past. For example, the answering unit can prioritize suggesting answering methods (voice, text, etc.) that the user has used in the past. For example, the answering unit can predict and suggest answering methods to be used during specific time periods based on the user's past call history. In this way, by analyzing the user's past call history, the optimal call answering method can be provided.

[0106] The answering unit can adjust the voice sensitivity according to the ambient noise level when answering a call. For example, if the surroundings are noisy, the answering unit can increase the voice sensitivity to accurately answer the call. For example, if the surroundings are quiet, the answering unit can decrease the voice sensitivity to prevent misrecognition. For example, the answering unit can monitor the ambient noise level in real time and make appropriate sensitivity adjustments. This allows for accurate call answering by adjusting the voice sensitivity according to the ambient noise level.

[0107] The response unit can estimate the user's emotions and determine call priorities based on those emotions. For example, if the user is nervous, the response unit can prioritize important calls. For example, if the user is relaxed, the response unit can prioritize detailed calls. For example, if the user is in a hurry, the response unit can prioritize calls that require quick processing. This allows for more appropriate call responses by prioritizing calls according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The answering unit can prioritize answering calls that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific location, the answering unit can prioritize answering calls related to that location. For example, if the user is on the move, the answering unit can prioritize answering calls related to their destination. For example, if the user is in a specific region, the answering unit can prioritize answering calls related to that region. In this way, by considering the user's geographical location, it is possible to prioritize answering calls that are highly relevant.

[0109] The answering unit can analyze the user's social media activity when answering a call and answer relevant calls. For example, the answering unit can prioritize answering relevant calls based on information the user has shared on social media. For example, the answering unit can suggest calls based on the user's interests and preferences from their social media activity. For example, the answering unit can analyze the user's social media activity history and answer the most appropriate call. In this way, by analyzing the user's social media activity, it can answer relevant calls.

[0110] The service provider can estimate the user's emotions and adjust the way the weather forecast is delivered based on those emotions. For example, if the user is stressed, the service provider can provide a simple and easy-to-read weather forecast. If the user is relaxed, the service provider can provide detailed weather information. If the user is in a hurry, the service provider can provide a concise weather forecast. By adjusting the way the weather forecast is delivered according to the user's emotions, more appropriate weather information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The service provider can analyze the user's past weather information usage history when providing weather forecasts and select the optimal delivery method. For example, the service provider can automatically display weather information that the user has frequently used in the past as a candidate. For example, the service provider can analyze patterns of weather information used by the user in the past and propose the optimal delivery method. For example, the service provider can predict and propose weather information that the user will use during a specific time period based on the user's past weather information usage history. In this way, by analyzing the user's past weather information usage history, the service provider can provide the optimal weather information delivery method.

[0112] The service provider can provide optimal information by considering real-time weather data when providing weather forecasts. For example, the service provider can provide optimal weather forecasts based on real-time weather data. For example, the service provider can provide weather information best suited to the user's current location by considering real-time weather data. For example, the service provider can provide weather information for the user's destination based on real-time weather data. In this way, optimal weather information can be provided by considering real-time weather data.

[0113] The service provider can estimate the user's emotions and prioritize weather forecasts based on those emotions. For example, if the user is stressed, the service provider can prioritize important weather information. For example, if the user is relaxed, the service provider can prioritize detailed weather information. For example, if the user is in a hurry, the service provider can prioritize weather information that needs to be provided quickly. This allows for the provision of more appropriate weather information by prioritizing weather forecasts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The weather forecasting system can prioritize providing highly relevant weather information by considering the user's geographical location. For example, if the user is in a specific location, the system can prioritize providing weather information relevant to that location. For example, if the user is on the move, the system can prioritize providing weather information relevant to their destination. For example, if the user is in a specific region, the system can prioritize providing weather information relevant to that region. This allows the system to prioritize providing highly relevant weather information by considering the user's geographical location.

[0115] The service provider can analyze users' social media activity when providing weather forecasts and provide relevant weather information. For example, the service provider can prioritize providing relevant weather information based on information shared by users on social media. For example, the service provider can suggest weather information based on users' interests and preferences from their social media activity. For example, the service provider can analyze users' social media activity history and provide optimal weather information. In this way, relevant weather information can be provided by analyzing users' social media activity.

[0116] The communications unit can estimate the user's emotions and adjust the method of emergency communication based on the estimated emotions. For example, if the user is tense, the communications unit can provide a quick and concise emergency communication method. For example, if the user is relaxed, the communications unit can provide an emergency communication method that includes detailed information. For example, if the user is in a hurry, the communications unit can provide a method for emergency communication in the shortest possible time. This allows for more appropriate emergency communication by adjusting the method of emergency communication according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The communications unit can analyze the user's past emergency communication history during an emergency and select the optimal communication method. For example, the communications unit can automatically display frequently used emergency communication methods as candidates. For example, the communications unit can analyze patterns of previously used emergency communication methods and suggest the optimal method. For example, the communications unit can predict and suggest the emergency communication method to be used during a specific time period based on the user's past emergency communication history. In this way, by analyzing the user's past emergency communication history, the optimal emergency communication method can be provided.

[0118] The communications unit can provide the optimal communication method in the event of an emergency, taking into account the real-time situation. For example, the communications unit can provide the optimal emergency communication method based on the real-time situation. For example, the communications unit can provide the optimal emergency communication method for the user's current location, taking into account the real-time situation. For example, the communications unit can provide the emergency communication method for the user's destination, based on the real-time situation. This allows for the provision of the optimal emergency communication method by considering the real-time situation.

[0119] The communications unit can estimate the user's emotions and prioritize emergency communications based on those emotions. For example, if the user is stressed, the communications unit can prioritize important emergency communications. For example, if the user is relaxed, the communications unit can prioritize detailed emergency communications. For example, if the user is in a hurry, the communications unit can prioritize emergency communications that require quick processing. This allows for more appropriate emergency communications by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The communications unit can prioritize providing the most relevant communication method during emergency communications, taking into account the user's geographical location. For example, if the user is in a specific location, the communications unit can prioritize providing emergency communication methods relevant to that location. For example, if the user is on the move, the communications unit can prioritize providing emergency communication methods relevant to their destination. For example, if the user is in a specific region, the communications unit can prioritize providing emergency communication methods relevant to that region. In this way, by taking the user's geographical location into consideration, the communications unit can prioritize providing the most relevant emergency communication methods.

[0121] The communications department can analyze a user's social media activity during an emergency and provide relevant communication methods. For example, the communications department can prioritize providing relevant emergency communication methods based on information shared by the user on social media. For example, the communications department can suggest emergency communication methods based on the user's interests and preferences from their social media activity. For example, the communications department can analyze a user's social media activity history and provide the most suitable emergency communication method. In this way, relevant emergency communication methods can be provided by analyzing the user's social media activity.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The ride assistant system can also include a health management unit that monitors the user's health. This unit can, for example, measure heart rate and blood pressure and notify the user if abnormalities are detected. It can also, for example, record the user's activity level and suggest appropriate rest times. Furthermore, it can estimate the user's fatigue level and prompt rest if fatigue is accumulating. This allows for safer and more comfortable riding by monitoring the user's health.

[0124] The ride assistant system may also include a playback unit that estimates the user's emotions and adjusts the music playback method based on those emotions. For example, if the user is relaxed, the playback unit can play relaxing music. If the user is excited, the playback unit can play upbeat music. If the user is stressed, the playback unit can play relaxing music. This allows for more appropriate music playback by adjusting the music playback method according to the user's emotions.

[0125] The ride assistance system can also include a learning unit that learns the user's driving style and provides optimal navigation. For example, the learning unit can record the user's preferred routes and reflect them in future navigation. For example, the learning unit can analyze the user's driving speed and braking timing to provide optimal route guidance. For example, the learning unit can suggest routes that avoid congestion based on the user's past driving history. In this way, by learning the user's driving style, it can provide more personalized navigation.

[0126] The ride-assistance system may also include a response unit that estimates the user's emotions and adjusts the call response method based on the estimated emotions. For example, if the user is tense, the response unit can respond in a calm voice. For example, if the user is relaxed, the response unit can respond in a cheerful voice. For example, if the user is in a hurry, the response unit can provide a quick and concise response. This allows for more appropriate call response by adjusting the call response method according to the user's emotions.

[0127] The ride-assistance system may further include a location information unit that provides highly relevant information by considering the user's geographical location. For example, if the user is in a specific location, the location information unit can provide tourist information relevant to that location. For example, if the user is on the move, the location information unit can provide information about restaurants and accommodations relevant to their destination. For example, if the user is in a specific region, the location information unit can provide information about events in that region. This allows for the provision of highly relevant information by considering the user's geographical location.

[0128] The ride assistant system may also include a delivery unit that estimates the user's emotions and adjusts how the weather forecast is delivered based on those emotions. For example, if the user is stressed, the delivery unit can provide a simple and easy-to-read weather forecast. If the user is relaxed, the delivery unit can provide detailed weather information. If the user is in a hurry, the delivery unit can provide a concise weather forecast. By adjusting how the weather forecast is delivered according to the user's emotions, more appropriate weather information can be provided.

[0129] The ride assistance system may also include a posture management unit that monitors the user's posture while driving and provides advice to maintain proper posture. For example, the posture management unit can notify the user to correct their posture if their spine is hunched. It can also provide advice to encourage stretching if the user is driving in the same position for extended periods. Furthermore, it can detect tension in the user's shoulders and neck and provide advice to help them relax. This allows for a more comfortable riding experience by monitoring the user's posture while driving.

[0130] The ride assistance system may also include a communication unit that estimates the user's emotions and adjusts the method of emergency communication based on those emotions. For example, if the user is stressed, the communication unit can provide a quick and concise emergency communication method. For example, if the user is relaxed, the communication unit can provide an emergency communication method that includes detailed information. For example, if the user is in a hurry, the communication unit can provide a method for emergency communication in the shortest possible time. This allows for more appropriate emergency communication by adjusting the method of emergency communication according to the user's emotions.

[0131] The ride assistance system can also include a fatigue management unit that monitors the user's fatigue level while driving and suggests appropriate rest times. The fatigue management unit can, for example, measure the user's blinking frequency and reaction time while driving, and prompt a rest if fatigue is accumulating. It can also, for example, record the user's driving time and suggest rests at regular intervals. Furthermore, it can monitor the user's heart rate and respiratory rate, and prompt a rest if abnormalities are detected. This allows for safer riding by monitoring the user's fatigue level while driving.

[0132] The ride assistance system may also include a reception unit that estimates the user's emotions and prioritizes voice commands based on those emotions. For example, if the user is tense, the reception unit can prioritize important voice commands. For example, if the user is relaxed, the reception unit can prioritize detailed voice commands. For example, if the user is in a hurry, the reception unit can prioritize voice commands that require quick processing. This allows for more appropriate operation by prioritizing voice commands according to the user's emotions.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The reception unit receives voice commands. The reception unit can receive voice commands using, for example, speech recognition technology. Step 2: The navigation unit performs navigation based on the voice commands received by the reception unit. The navigation unit can, for example, integrate GPS navigation and provide route guidance to the destination. Step 3: The playback unit plays music based on the voice command received by the reception unit. The playback unit can, for example, perform streaming playback or local file playback. Step 4: The answering unit answers the call based on the voice command received by the receiving unit. The answering unit can, for example, make a hands-free call. Step 5: The service unit provides weather forecasts based on the voice commands received by the reception unit. The service unit can, for example, provide weather information in real time. Step 6: The communications unit makes emergency communications based on the voice commands received by the reception unit. The communications unit can, for example, make communications quickly in an emergency.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0137] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the reception unit, navigation unit, playback unit, response unit, provision unit, and communication unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the microphone 38B and control unit 46A of the smart device 14 and receives voice commands. The navigation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides route guidance to the destination by integrating GPS navigation. The playback unit is implemented by, for example, the control unit 46A of the smart device 14 and plays music. The response unit is implemented by, for example, the control unit 46A of the smart device 14 and makes hands-free calls. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides weather information in real time. The communication unit is implemented by, for example, the control unit 46A of the smart device 14 and makes rapid communication in emergencies. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the reception unit, navigation unit, playback unit, response unit, provision unit, and communication unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the smart glasses 214 and receives voice commands. The navigation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides route guidance to a destination by integrating GPS navigation. The playback unit is implemented by the control unit 46A of the smart glasses 214 and plays music. The response unit is implemented by the control unit 46A of the smart glasses 214 and makes hands-free calls. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides weather information in real time. The communication unit is implemented by the control unit 46A of the smart glasses 214 and makes rapid communication in emergencies. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0163] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0164] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0165] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0167] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0170] Each of the multiple elements described above, including the reception unit, navigation unit, playback unit, response unit, provision unit, and communication unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the headset terminal 314 and receives voice commands. The navigation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides route guidance to the destination by integrating GPS navigation. The playback unit is implemented by, for example, the control unit 46A of the headset terminal 314 and plays music. The response unit is implemented by, for example, the control unit 46A of the headset terminal 314 and makes hands-free calls. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides weather information in real time. The communication unit is implemented by, for example, the control unit 46A of the headset terminal 314 and makes rapid communication in emergencies. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0174] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0175] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0177] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0178] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0179] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0180] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0181] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0182] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0183] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0184] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0185] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0186] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0187] Each of the multiple elements described above, including the reception unit, navigation unit, playback unit, response unit, provision unit, and communication unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the robot 414 and receives voice commands. The navigation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides route guidance to a destination by integrating GPS navigation. The playback unit is implemented by the control unit 46A of the robot 414 and plays music. The response unit is implemented by the control unit 46A of the robot 414 and makes hands-free calls. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides weather information in real time. The communication unit is implemented by the control unit 46A of the robot 414 and makes rapid communications in emergencies. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0188] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0189] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0190] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0191] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0192] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0193] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0195] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0196] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0197] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0198] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0199] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0200] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0201] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0202] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0203] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0204] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0205] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0206] (Note 1) A reception area that accepts voice commands, A navigation unit that performs navigation based on voice commands received by the reception unit, A playback unit that plays music based on voice commands received by the aforementioned reception unit, A response unit that responds to calls based on voice commands received by the aforementioned reception unit, A service unit that provides weather forecasts based on voice commands received by the reception unit, The system includes a communication unit that performs emergency communication based on voice commands received by the reception unit. A system characterized by the following features. (Note 2) The aforementioned navigation unit is Integrate GPS navigation The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Providing real-time weather information The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned communications unit is To communicate quickly in emergencies The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It accepts voice commands using speech recognition technology. The system described in Appendix 1, characterized by the features described herein. (Note 6) The response unit is Answer calls hands-free. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how voice commands are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving a voice command, the system analyzes the user's past command history and selects the optimal method for receiving the command. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When a voice command is received, the sensitivity of the voice recognition is adjusted according to the ambient noise level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes voice commands based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving voice commands, the system prioritizes accepting commands that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a voice command is received, the system analyzes the user's social media activity and accepts relevant commands. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned navigation unit is It estimates the user's emotions and adjusts the navigation guidance method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned navigation unit is When navigating, the system provides the optimal route by taking real-time traffic information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned navigation unit is During navigation, the system suggests the optimal route by referencing the user's past driving history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned navigation unit is The system estimates the user's emotions and adjusts the frequency of navigation prompts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned navigation unit is During navigation, the system prioritizes providing the most relevant routes by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned navigation unit is During navigation, the system analyzes the user's social media activity and suggests relevant routes. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned regeneration unit is It estimates the user's emotions and adjusts the music playback method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned regeneration unit is When playing music, the system analyzes the user's past playback history and selects the optimal playback method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned regeneration unit is When playing music, adjust the volume according to the ambient noise level. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned regeneration unit is It estimates the user's emotions and determines the priority of music to play based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned regeneration unit is When playing music, the system prioritizes playing music that is highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned regeneration unit is When playing music, the system analyzes the user's social media activity and plays relevant music. The system described in Appendix 1, characterized by the features described herein. (Note 25) The response unit is It estimates the user's emotions and adjusts how calls are answered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The response unit is When answering a call, the system analyzes the user's past call history and selects the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The response unit is When answering a call, the voice sensitivity is adjusted according to the ambient noise level. The system described in Appendix 1, characterized by the features described herein. (Note 28) The response unit is It estimates the user's emotions and determines call priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The response unit is When answering a call, the system prioritizes answering calls that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The response unit is When answering a call, the system analyzes the user's social media activity and answers relevant calls. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We estimate the user's emotions and adjust how weather forecasts are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing weather forecasts, the system analyzes the user's past weather information usage history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing weather forecasts, we take real-time weather data into consideration to provide the most optimal information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes weather forecasts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing weather forecasts, the system prioritizes providing highly relevant weather information by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing weather forecasts, we analyze users' social media activity and provide relevant weather information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned communications unit is The system estimates the user's emotions and adjusts the emergency communication method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned communications unit is During emergency communications, the system analyzes the user's past emergency communication history and selects the most suitable communication method. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned communications unit is During emergency communications, the system provides the optimal communication method, taking into account the real-time situation. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned communications unit is The system estimates the user's emotions and prioritizes emergency communications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned communications unit is During emergency communications, the system prioritizes providing the most relevant communication method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned communications unit is During emergency communications, the system analyzes users' social media activity and provides relevant communication methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts voice commands, A navigation unit that performs navigation based on voice commands received by the reception unit, A playback unit that plays music based on voice commands received by the aforementioned reception unit, A response unit that responds to calls based on voice commands received by the aforementioned reception unit, A service unit that provides weather forecasts based on voice commands received by the reception unit, The system includes a communication unit that performs emergency communication based on voice commands received by the reception unit. A system characterized by the following features.

2. The aforementioned navigation unit is Integrate GPS navigation The system according to feature 1.

3. The aforementioned supply unit is, Providing real-time weather information The system according to feature 1.

4. The aforementioned communications unit is To communicate quickly in emergencies The system according to feature 1.

5. The aforementioned reception unit is It accepts voice commands using speech recognition technology. The system according to feature 1.

6. The response unit is Answer calls hands-free. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how voice commands are received based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When receiving a voice command, the system analyzes the user's past command history and selects the optimal method for receiving the command. The system according to feature 1.

9. The aforementioned reception unit is When a voice command is received, the sensitivity of the voice recognition is adjusted according to the ambient noise level. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and prioritizes voice commands based on those emotions. The system according to feature 1.

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