system

Abstract

The system according to this embodiment aims to allow intuitive operation of each function of a car using voice commands. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and an operation unit. The reception unit receives voice commands. The analysis unit analyzes the voice commands received by the reception unit. The operation unit operates the functions of the vehicle based on the voice commands analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to intuitively operate each function of a vehicle, and there is a problem that the safety and comfort of the driver cannot be sufficiently supported.

[0005] The system according to the embodiment aims to intuitively operate each function of a vehicle by voice.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and an operation unit. The reception unit receives a voice command. The analysis unit analyzes the voice command received by the reception unit. The operation unit operates a function of a vehicle based on the voice command analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment allows for intuitive operation of each function of the car using voice commands. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The automotive OS according to an embodiment of the present invention is a system equipped with a generative AI that allows intuitive voice control of various car functions. This system covers almost all in-car functions, including engine management, brakes, locking, audio, air conditioning, and car navigation, providing maximum support for driver safety and comfort. First, the user inputs a command by voice. For example, commands such as "Start the engine," "Turn on the air conditioner," or "Find the nearest gas station using the navigation system" are entered. This voice command is input to the generative AI. Next, the generative AI analyzes the input voice command and operates the corresponding car function. The generative AI understands the voice command using natural language processing and provides real-time response and feedback using a software architecture that enables integration with each system of the vehicle. For example, in response to the command "Start the engine," the generative AI starts the engine. Also, in response to the command "Find the nearest gas station using the navigation system," the generative AI operates the car navigation system and displays the nearest gas station. This mechanism allows the driver to reduce manual operation while driving and reduce accidents caused by distraction. Furthermore, operation by voice commands can shorten the time spent using in-car functions and improve user satisfaction. For example, when changing the air conditioning settings while driving, manual operation is unnecessary; it can be easily controlled by voice commands, allowing the driver to concentrate on driving. Furthermore, the generative AI provides customization features based on the user's behavior and preferences. For instance, it can automatically play music preferred by a particular user or prioritize guiding them along specific routes. This allows users to enjoy a comfortable driving environment tailored to their preferences. This car OS equipped with generative AI also offers significant advantages for automakers. Automakers aiming for technological innovation can implement this system to ensure driving safety, improve the usability of in-car functions, and enhance customer satisfaction. In addition, the seamless integration of each function reduces driver stress and creates a sustainable driver assistance system.Thus, an in-car OS equipped with generational AI will provide maximum support for driver safety and comfort, offering a future driving experience. This allows the in-car OS to fully support driver safety and comfort.

[0029] The automotive OS according to this embodiment comprises a reception unit, an analysis unit, and an operation unit. The reception unit receives voice commands. Voice commands include, but are not limited to, examples such as "start the engine," "turn on the air conditioner," and "find the nearest gas station using the navigation system." The reception unit can receive voice commands using, for example, voice recognition technology. The reception unit can also receive voice commands using generative AI. The analysis unit analyzes the voice commands received by the reception unit. The analysis unit can analyze voice commands using, for example, natural language processing technology. The analysis unit can also analyze voice commands using generative AI. The operation unit operates the functions of the vehicle based on the voice commands analyzed by the analysis unit. The operation unit can operate in-vehicle functions such as engine management, brakes, locking, audio, air conditioning, and car navigation. The operation unit can also operate the functions of the vehicle using generative AI. As a result, the automotive OS according to this embodiment can support the safety and comfort of the driver by receiving, analyzing, and operating the functions of the vehicle.

[0030] The reception unit accepts voice commands. Voice commands include, but are not limited to, examples such as "start the engine," "turn on the air conditioner," and "find the nearest gas station using the navigation system." The reception unit can accept voice commands using, for example, speech recognition technology. Specifically, a microphone installed in the vehicle captures the driver's voice and transmits the voice data to a speech recognition engine. The speech recognition engine analyzes the voice data and converts the voice command into text data. This text data is further analyzed using natural language processing technology to understand the intent of the command. For example, the command "start the engine" corresponds to the operation of starting the engine. The reception unit can also accept voice commands using generative AI. Generative AI understands the context and nuances of the voice command, enabling more natural dialogue. For example, if the driver says "It's hot today," the generative AI understands the context and can suggest lowering the air conditioner temperature. This allows the reception unit to flexibly respond to a variety of voice commands from the driver and enable intuitive operation. Furthermore, the reception unit can improve the accuracy of speech recognition by removing in-car noise using noise cancellation technology. This allows the driver to input voice commands accurately even while the vehicle is in motion.

[0031] The analysis unit analyzes voice commands received by the reception unit. The analysis unit can analyze voice commands using, for example, natural language processing technology. Specifically, it analyzes the text data of the voice command and performs grammatical and semantic analysis to understand its intent. For example, the command "Find the nearest gas station using the navigation system" corresponds to the operation "Activate the navigation system and search for the nearest gas station from the current location." The analysis unit can also analyze voice commands using generative AI. Generative AI understands the context and nuances of the voice command and performs more advanced analysis. For example, if the driver says "It's cold," the generative AI understands the context and can suggest increasing the air conditioning temperature. The analysis unit transmits the analysis results of the voice command to the operation unit, generating instructions for operating the car's functions. Furthermore, the analysis unit can learn from the history of past voice commands and perform analysis that reflects the driver's preferences and habits. This allows the analysis unit to accurately understand the driver's intent and provide appropriate instructions. The analysis unit analyzes voice commands in real time, enabling rapid responses. This allows drivers to use voice commands without stress.

[0032] The control unit operates the car's functions based on voice commands analyzed by the analysis unit. For example, the control unit can operate in-car functions such as engine management, brakes, locking, audio, air conditioning, and car navigation. Specifically, it receives instructions from the analysis unit and controls the corresponding car function. For example, in response to the command "Start the engine," it activates the engine start system. In response to the command "Turn on the air conditioning," it activates the air conditioning system and adjusts it to the set temperature. In response to the command "Find the nearest gas station using the navigation," it activates the car navigation system, searches for the nearest gas station from the current location, and begins route guidance. The control unit can also operate car functions using generative AI. The generative AI understands the driver's intentions and suggests the optimal operation. For example, if the driver says "Play music," the generative AI learns the driver's musical preferences and can play the optimal playlist. The control unit can utilize data from various in-car sensors to perform optimal operations according to driving conditions and the environment. For example, it can monitor the outside temperature and inside temperature and automatically adjust the air conditioning settings. This allows the control unit to improve driver comfort and safety. The control unit allows for real-time operation of the car's functions and provides quick responses. This enables the driver to use the car's functions without stress.

[0033] The control unit can operate in-vehicle functions such as engine management, brakes, locking, audio, air conditioning, and car navigation. For example, as part of engine management, the control unit can start and stop the engine and monitor its status. The control unit can also operate the brakes according to the brake control method and type. Furthermore, as part of locking, the control unit can lock and unlock the doors and control the timing of locking. The control unit can also adjust the volume and select songs for the audio system. The control unit can also adjust the temperature and airflow for the air conditioning system. The control unit can also set destinations and provide route guidance for the car navigation system. In this way, the control unit can operate almost all in-vehicle functions, thereby providing maximum support for the driver's safety and comfort. Some or all of the above-mentioned processes in the control unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the control unit can input engine management operations into a generation AI, which can then start and stop the engine.

[0034] The analysis unit can analyze voice commands using natural language processing. For example, the analysis unit can analyze voice commands using morphological analysis. The analysis unit can also analyze voice commands using grammatical analysis. The analysis unit can also analyze voice commands using semantic analysis. In this way, the analysis unit can improve the accuracy of voice command analysis by using natural language processing. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input a voice command to a generative AI, and the generative AI can perform the analysis of the voice command.

[0035] The control unit includes a customization unit that provides customization functions based on user behavior and preferences. For example, the customization unit can analyze the user's behavior history and automatically play music preferred by a particular user. The customization unit can also prioritize guiding the user along specific routes. The customization unit can also provide settings tailored to the user's preferences. In this way, the control unit can improve user satisfaction by providing customization functions based on user behavior and preferences. Some or all of the above-described processes in the customization unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the customization unit can input the user's behavior history into a generative AI, which can then execute the customization functions.

[0036] The control unit includes a feedback unit that provides real-time responses and feedback. The feedback unit can, for example, respond to voice commands in real time. The feedback unit can also provide real-time feedback to voice commands. The feedback unit can also respond to user operations in real time. This allows the control unit to improve user operability by providing real-time responses and feedback. Some or all of the above-described processing in the feedback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the feedback unit can input a voice command to a generative AI, which can then provide real-time responses and feedback.

[0037] The reception unit can analyze the user's past voice command history and select the optimal reception method. For example, the reception unit can prioritize receiving voice commands that the user has frequently used in the past. The reception unit can also predict commands to be used during specific time periods based on the user's past voice command history and adjust the reception method accordingly. The reception unit can also analyze the user's past voice command history and apply the optimal speech recognition algorithm. This allows the reception unit to select the optimal reception method by analyzing the user's past voice command history. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit can input the user's past voice command history into a generative AI, which can then select the optimal reception method.

[0038] The reception unit can filter voice commands based on the user's current driving status and in-vehicle environment. For example, if the user is driving on a highway, the reception unit can only receive important voice commands. If the user is stuck in traffic, the reception unit can receive all voice commands. If the user is parked, the reception unit can receive detailed voice commands. This allows the reception unit to receive only appropriate commands by filtering them based on the user's driving status and in-vehicle environment. Some or all of the above processing in the reception unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception unit can input user driving status and in-vehicle environment data into a generation AI, which can then perform voice command filtering.

[0039] The reception unit can prioritize receiving voice commands by considering the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize receiving voice commands related to that region. If the user is approaching a destination, the reception unit can also prioritize receiving voice commands related to that destination. If the user is at home, the reception unit can also prioritize receiving voice commands related to home. In this way, the reception unit can prioritize receiving commands that are highly relevant by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then prioritize receiving commands that are highly relevant.

[0040] The reception unit can analyze the user's social media activity when receiving a voice command and receive relevant commands. For example, if the reception unit indicates on social media that the user will attend a specific event, it can prioritize receiving voice commands related to that event. If the reception unit indicates on social media that the user will go to a specific place, it can also prioritize receiving voice commands related to that place. If the reception unit indicates on social media that the user will perform a specific activity, it can also prioritize receiving voice commands related to that activity. In this way, the reception unit can prioritize receiving relevant commands by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit can input the user's social media activity data into a generative AI, which can then receive relevant commands.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the voice command during analysis. For example, the analysis unit can perform a detailed analysis for important voice commands and provide an accurate response. For general voice commands, the analysis unit can perform a standard analysis and provide a quick response. For low-priority voice commands, the analysis unit can perform a simplified analysis and provide a quick response. In this way, the analysis unit can perform a detailed analysis for important commands by adjusting the level of detail of the analysis based on the importance of the voice command. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input importance data of the voice command into a generation AI, and the generation AI can adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the voice command during analysis. For example, the analysis unit can apply a dedicated analysis algorithm for engine management to voice commands related to engine management. The analysis unit can also apply a dedicated analysis algorithm for audio to voice commands related to audio. The analysis unit can also apply a dedicated analysis algorithm for car navigation to voice commands related to car navigation. This allows the analysis unit to perform more appropriate analysis by applying different analysis algorithms depending on the category of the voice command. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the category data of the voice command into a generation AI, and the generation AI can apply different analysis algorithms.

[0043] The analysis unit can determine the priority of analysis based on the timing of voice command submissions during analysis. For example, the analysis unit can prioritize the analysis of recently submitted voice commands. The analysis unit can also prioritize the analysis of voice commands submitted within a specific time period. If the user is in a hurry, the analysis unit can also prioritize the analysis of voice commands that require quick action. This enables rapid analysis by allowing the analysis unit to determine the priority of analysis based on the timing of voice command submissions. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input voice command submission timing data into a generation AI, which can then determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the voice commands during analysis. For example, the analysis unit can prioritize the analysis of highly relevant voice commands. The analysis unit can also postpone the analysis of less relevant voice commands. The analysis unit can also prioritize the analysis of voice commands relevant to the user's current situation. In this way, the analysis unit can prioritize the analysis of highly relevant commands by adjusting the order of analysis based on the relevance of the voice commands. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input voice command relevance data into a generating AI, and the generating AI can adjust the order of analysis.

[0045] The control unit can adjust the level of detail of operations based on the importance of the vehicle's functions during operation. For example, the control unit can perform detailed operations for important vehicle functions, providing precise control. For general vehicle functions, the control unit can perform standard operations, providing quick control. For low-priority vehicle functions, the control unit can perform simplified operations, also providing quick control. In this way, the control unit can perform detailed operations for important functions by adjusting the level of detail of operations based on the importance of the vehicle's functions. Some or all of the above processing in the control unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the control unit can input vehicle function importance data into a generative AI, which can then adjust the level of detail of operations.

[0046] The control unit can apply different operating algorithms depending on the category of the vehicle's function during operation. For example, the control unit can apply a dedicated operating algorithm for engine management operations. The control unit can also apply a dedicated operating algorithm for audio operations. The control unit can also apply a dedicated operating algorithm for car navigation operations. This allows the control unit to perform more appropriate operations by applying different operating algorithms depending on the category of the vehicle's function. Some or all of the above processing in the control unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the control unit can input vehicle function category data into a generative AI, and the generative AI can apply different operating algorithms.

[0047] The control unit can determine the priority of operations based on the timing of vehicle function submissions during operation. For example, the control unit can prioritize recently submitted operations. It can also prioritize operations submitted within a specific time period. If the user is in a hurry, the control unit can prioritize operations that require quick action. This enables quick operation by allowing the control unit to prioritize operations based on the timing of vehicle function submissions. Some or all of the above processing in the control unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the control unit can input vehicle function submission timing data into a generative AI, which can then determine the priority of operations.

[0048] The control unit can adjust the order of operations based on the relationships between the car's functions during operation. For example, the control unit can prioritize operations that are highly relevant. The control unit can also postpone operations that are less relevant. The control unit can also prioritize operations that are relevant to the user's current situation. In this way, the control unit can prioritize operations that are highly relevant by adjusting the order of operations based on the relationships between the car's functions. Some or all of the above processing in the control unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the control unit can input data on the relationships between the car's functions into a generative AI, and the generative AI can adjust the order of operations.

[0049] The customization unit can analyze the user's past behavior history to select the optimal customization method during the customization process. For example, the customization unit can propose the optimal customization method based on the customization settings the user has previously selected. The customization unit can also predict and propose settings to be used during specific time periods based on the user's past behavior history. The customization unit can also analyze the user's past behavior history and propose the most efficient customization method. In this way, the customization unit can select the optimal customization method by analyzing the user's past behavior history. Some or all of the above-described processes in the customization unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the customization unit can input the user's past behavior history data into a generative AI, which can then select the optimal customization method.

[0050] The customization unit can customize the means of customization based on the user's current driving situation during the customization process. For example, if the user is driving on a highway, the customization unit can provide only the most important customization options. If the user is stuck in traffic, the customization unit can provide all customization options. If the user is parked, the customization unit can provide detailed customization options. This allows the customization unit to provide more appropriate customization by customizing the means of customization based on the user's current driving situation. Some or all of the above-described processes in the customization unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the customization unit can input the user's driving situation data into a generative AI, which can then customize the means of customization.

[0051] The customization unit can select the optimal customization method by considering the user's geographical location information during customization. For example, if the user is in a specific region, the customization unit can provide customization options related to that region. If the user is approaching a destination, the customization unit can also provide customization options related to the destination. If the user is at home, the customization unit can also provide customization options related to home. In this way, the customization unit can select the optimal customization method by considering the user's geographical location information. Some or all of the above processing in the customization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the customization unit can input the user's geographical location information data into a generative AI, and the generative AI can select the optimal customization method.

[0052] The customization unit can analyze the user's social media activity during customization and propose customization options. For example, if the customization unit indicates on social media that the user will participate in a specific event, it can provide customization options related to that event. If the customization unit indicates on social media that the user will go to a specific place, it can also provide customization options related to that place. If the customization unit indicates on social media that the user will perform a specific activity, it can also provide customization options related to that activity. In this way, the customization unit can propose relevant customization options by analyzing the user's social media activity. Some or all of the above processing in the customization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the customization unit can input the user's social media activity data into a generative AI, and the generative AI can propose customization options.

[0053] The feedback unit can select the optimal feedback method by referring to the user's past operation history when providing feedback. For example, the feedback unit can propose the optimal feedback method based on feedback the user has received in the past. The feedback unit can also predict and propose a feedback method to be used during a specific time period based on the user's past operation history. The feedback unit can also analyze the user's past operation history and propose the most efficient feedback method. In this way, the feedback unit can select the optimal feedback method by referring to the user's past operation history. Some or all of the above processing in the feedback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the feedback unit can input the user's past operation history data into a generative AI, and the generative AI can select the optimal feedback method.

[0054] The feedback unit can customize the means of feedback based on the user's current driving situation. For example, if the user is driving on a highway, the feedback unit can provide only important feedback. If the user is stuck in traffic, the feedback unit can provide all feedback. If the user is parked, the feedback unit can provide detailed feedback. This allows the feedback unit to provide more appropriate feedback by customizing the means of feedback based on the user's current driving situation. Some or all of the above processing in the feedback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the feedback unit can input the user's driving situation data into a generative AI, which can then customize the means of feedback.

[0055] The feedback unit can select the optimal feedback method by considering the user's geographical location information when providing feedback. For example, if the user is in a specific region, the feedback unit can provide feedback related to that region. If the user is approaching their destination, the feedback unit can also provide feedback related to their destination. If the user is at home, the feedback unit can also provide feedback related to their home. In this way, the feedback unit can select the optimal feedback method by considering the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the feedback unit can input the user's geographical location data into a generative AI, which can then select the optimal feedback method.

[0056] The feedback unit can analyze the user's social media activity and suggest methods for providing feedback when providing feedback. For example, if the user indicates on social media that they will be participating in a specific event, the feedback unit can provide feedback related to that event. If the user indicates on social media that they will be going to a specific place, the feedback unit can also provide feedback related to that place. If the user indicates on social media that they will be performing a specific activity, the feedback unit can also provide feedback related to that activity. In this way, the feedback unit can suggest relevant methods for providing feedback by analyzing the user's social media activity. Some or all of the above processing in the feedback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the feedback unit can input the user's social media activity data into a generative AI, and the generative AI can suggest methods for providing feedback.

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

[0058] The analysis unit, when analyzing voice commands, can refer to the user's past command history and prioritize the analysis of similar commands. For example, by prioritizing the analysis of commands that the user has frequently used in the past, it can provide a quick response. It can also predict commands that the user will use during specific time periods based on their past command history and adjust the analysis priority accordingly. Furthermore, it can analyze the user's past command history and apply the most efficient analysis method. In this way, the analysis unit can select the optimal analysis method by referring to the user's past command history.

[0059] The control unit can adjust its operation method based on the user's current driving situation when operating the vehicle's functions. For example, if the user is driving on a highway, only essential operations can be performed, and other operations can be postponed. If the user is stuck in traffic, all operations can be performed. Furthermore, if the user is parked, detailed operations can be performed. In this way, the control unit can adjust its operation method based on the user's driving situation, enabling more appropriate operation.

[0060] The reception unit can prioritize receiving voice commands by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize receiving voice commands related to that region. Similarly, if the user is approaching a destination, it can prioritize receiving voice commands related to that destination. Furthermore, if the user is at home, it can prioritize receiving voice commands related to home. In this way, the reception unit can prioritize receiving commands that are highly relevant by considering the user's geographical location.

[0061] The analysis unit can adjust its analysis method when analyzing voice commands, taking into account the user's current driving situation. For example, if the user is driving on a highway, it can analyze only important commands in detail, while simplifying the analysis of other commands. If the user is stuck in traffic, it can analyze all commands in detail. Furthermore, if the user is parked, it can perform a detailed analysis. In this way, the analysis unit can adjust its analysis method based on the user's driving situation, enabling more appropriate analysis.

[0062] The customization unit can analyze the user's past behavior history to select the optimal customization method during the customization process. For example, it can suggest the optimal customization method based on the customization settings the user has previously selected. It can also predict and suggest settings to be used during specific time periods based on the user's past behavior history. Furthermore, it can analyze the user's past behavior history and suggest the most efficient customization method. In this way, the customization unit can select the optimal customization method by analyzing the user's past behavior history.

[0063] The feedback unit can analyze a user's social media activity and suggest appropriate feedback methods when providing feedback. For example, if a user indicates on social media that they will be attending a specific event, the feedback unit can provide feedback related to that event. Similarly, if a user indicates on social media that they will be going to a specific location, the feedback unit can provide feedback related to that location. Furthermore, if a user indicates on social media that they will be engaging in a specific activity, the feedback unit can provide feedback related to that activity. In this way, the feedback unit can suggest appropriate feedback methods by analyzing a user's social media activity.

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

[0065] Step 1: The reception desk accepts voice commands. Voice commands include "start the engine," "turn on the air conditioner," and "use the navigation system to find the nearest gas station." The reception desk can accept voice commands using voice recognition technology and generative AI. Step 2: The analysis unit analyzes the voice commands received by the reception unit. The analysis unit can analyze the voice commands using natural language processing technology and generative AI. Step 3: The control unit operates the car's functions based on the voice commands analyzed by the analysis unit. The control unit can operate in-car functions such as engine management, brakes, locking, audio, air conditioning, and car navigation. The control unit can also operate the car's functions using generative AI.

[0066] (Example of form 2) The automotive OS according to an embodiment of the present invention is a system equipped with a generative AI that allows intuitive voice control of various car functions. This system covers almost all in-car functions, including engine management, brakes, locking, audio, air conditioning, and car navigation, providing maximum support for driver safety and comfort. First, the user inputs a command by voice. For example, commands such as "Start the engine," "Turn on the air conditioner," or "Find the nearest gas station using the navigation system" are entered. This voice command is input to the generative AI. Next, the generative AI analyzes the input voice command and operates the corresponding car function. The generative AI understands the voice command using natural language processing and provides real-time response and feedback using a software architecture that enables integration with each system of the vehicle. For example, in response to the command "Start the engine," the generative AI starts the engine. Also, in response to the command "Find the nearest gas station using the navigation system," the generative AI operates the car navigation system and displays the nearest gas station. This mechanism allows the driver to reduce manual operation while driving and reduce accidents caused by distraction. Furthermore, operation by voice commands can shorten the time spent using in-car functions and improve user satisfaction. For example, when changing the air conditioning settings while driving, manual operation is unnecessary; it can be easily controlled by voice commands, allowing the driver to concentrate on driving. Furthermore, the generative AI provides customization features based on the user's behavior and preferences. For instance, it can automatically play music preferred by a particular user or prioritize guiding them along specific routes. This allows users to enjoy a comfortable driving environment tailored to their preferences. This car OS equipped with generative AI also offers significant advantages for automakers. Automakers aiming for technological innovation can implement this system to ensure driving safety, improve the usability of in-car functions, and enhance customer satisfaction. In addition, the seamless integration of each function reduces driver stress and creates a sustainable driver assistance system.Thus, an in-car OS equipped with generational AI will provide maximum support for driver safety and comfort, offering a future driving experience. This allows the in-car OS to fully support driver safety and comfort.

[0067] The automotive OS according to this embodiment comprises a reception unit, an analysis unit, and an operation unit. The reception unit receives voice commands. Voice commands include, but are not limited to, examples such as "start the engine," "turn on the air conditioner," and "find the nearest gas station using the navigation system." The reception unit can receive voice commands using, for example, voice recognition technology. The reception unit can also receive voice commands using generative AI. The analysis unit analyzes the voice commands received by the reception unit. The analysis unit can analyze voice commands using, for example, natural language processing technology. The analysis unit can also analyze voice commands using generative AI. The operation unit operates the functions of the vehicle based on the voice commands analyzed by the analysis unit. The operation unit can operate in-vehicle functions such as engine management, brakes, locking, audio, air conditioning, and car navigation. The operation unit can also operate the functions of the vehicle using generative AI. As a result, the automotive OS according to this embodiment can support the safety and comfort of the driver by receiving, analyzing, and operating the functions of the vehicle.

[0068] The reception unit accepts voice commands. Voice commands include, but are not limited to, examples such as "start the engine," "turn on the air conditioner," and "find the nearest gas station using the navigation system." The reception unit can accept voice commands using, for example, speech recognition technology. Specifically, a microphone installed in the vehicle captures the driver's voice and transmits the voice data to a speech recognition engine. The speech recognition engine analyzes the voice data and converts the voice command into text data. This text data is further analyzed using natural language processing technology to understand the intent of the command. For example, the command "start the engine" corresponds to the operation of starting the engine. The reception unit can also accept voice commands using generative AI. Generative AI understands the context and nuances of the voice command, enabling more natural dialogue. For example, if the driver says "It's hot today," the generative AI understands the context and can suggest lowering the air conditioner temperature. This allows the reception unit to flexibly respond to a variety of voice commands from the driver and enable intuitive operation. Furthermore, the reception unit can improve the accuracy of speech recognition by removing in-car noise using noise cancellation technology. This allows the driver to input voice commands accurately even while the vehicle is in motion.

[0069] The analysis unit analyzes voice commands received by the reception unit. The analysis unit can analyze voice commands using, for example, natural language processing technology. Specifically, it analyzes the text data of the voice command and performs grammatical and semantic analysis to understand its intent. For example, the command "Find the nearest gas station using the navigation system" corresponds to the operation "Activate the navigation system and search for the nearest gas station from the current location." The analysis unit can also analyze voice commands using generative AI. Generative AI understands the context and nuances of the voice command and performs more advanced analysis. For example, if the driver says "It's cold," the generative AI understands the context and can suggest increasing the air conditioning temperature. The analysis unit transmits the analysis results of the voice command to the operation unit, generating instructions for operating the car's functions. Furthermore, the analysis unit can learn from the history of past voice commands and perform analysis that reflects the driver's preferences and habits. This allows the analysis unit to accurately understand the driver's intent and provide appropriate instructions. The analysis unit analyzes voice commands in real time, enabling rapid responses. This allows drivers to use voice commands without stress.

[0070] The control unit operates the car's functions based on voice commands analyzed by the analysis unit. For example, the control unit can operate in-car functions such as engine management, brakes, locking, audio, air conditioning, and car navigation. Specifically, it receives instructions from the analysis unit and controls the corresponding car function. For example, in response to the command "Start the engine," it activates the engine start system. In response to the command "Turn on the air conditioning," it activates the air conditioning system and adjusts it to the set temperature. In response to the command "Find the nearest gas station using the navigation," it activates the car navigation system, searches for the nearest gas station from the current location, and begins route guidance. The control unit can also operate car functions using generative AI. The generative AI understands the driver's intentions and suggests the optimal operation. For example, if the driver says "Play music," the generative AI learns the driver's musical preferences and can play the optimal playlist. The control unit can utilize data from various in-car sensors to perform optimal operations according to driving conditions and the environment. For example, it can monitor the outside temperature and inside temperature and automatically adjust the air conditioning settings. This allows the control unit to improve driver comfort and safety. The control unit allows for real-time operation of the car's functions and provides quick responses. This enables the driver to use the car's functions without stress.

[0071] The control unit can operate in-vehicle functions such as engine management, brakes, locking, audio, air conditioning, and car navigation. For example, as part of engine management, the control unit can start and stop the engine and monitor its status. The control unit can also operate the brakes according to the brake control method and type. Furthermore, as part of locking, the control unit can lock and unlock the doors and control the timing of locking. The control unit can also adjust the volume and select songs for the audio system. The control unit can also adjust the temperature and airflow for the air conditioning system. The control unit can also set destinations and provide route guidance for the car navigation system. In this way, the control unit can operate almost all in-vehicle functions, thereby providing maximum support for the driver's safety and comfort. Some or all of the above-mentioned processes in the control unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the control unit can input engine management operations into a generation AI, which can then start and stop the engine.

[0072] The analysis unit can analyze voice commands using natural language processing. For example, the analysis unit can analyze voice commands using morphological analysis. The analysis unit can also analyze voice commands using grammatical analysis. The analysis unit can also analyze voice commands using semantic analysis. In this way, the analysis unit can improve the accuracy of voice command analysis by using natural language processing. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input a voice command to a generative AI, and the generative AI can perform the analysis of the voice command.

[0073] The control unit includes a customization unit that provides customization functions based on user behavior and preferences. For example, the customization unit can analyze the user's behavior history and automatically play music preferred by a particular user. The customization unit can also prioritize guiding the user along specific routes. The customization unit can also provide settings tailored to the user's preferences. In this way, the control unit can improve user satisfaction by providing customization functions based on user behavior and preferences. Some or all of the above-described processes in the customization unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the customization unit can input the user's behavior history into a generative AI, which can then execute the customization functions.

[0074] The control unit includes a feedback unit that provides real-time responses and feedback. The feedback unit can, for example, respond to voice commands in real time. The feedback unit can also provide real-time feedback to voice commands. The feedback unit can also respond to user operations in real time. This allows the control unit to improve user operability by providing real-time responses and feedback. Some or all of the above-described processing in the feedback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the feedback unit can input a voice command to a generative AI, which can then provide real-time responses and feedback.

[0075] The reception unit can estimate the user's emotions and adjust the timing of voice command reception based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the timing of voice command reception and wait until the user calms down. If the user is relaxed, the reception unit can speed up the timing of voice command reception to facilitate smooth operation. If the user is in a hurry, the reception unit can make the timing of voice command reception immediate to enable quick operation. In this way, the reception unit can receive voice commands at a more appropriate time by adjusting the timing of voice command reception 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without a generative AI. For example, the reception unit can input user emotion data into a generative AI, which can then adjust the timing of voice command reception.

[0076] The reception unit can analyze the user's past voice command history and select the optimal reception method. For example, the reception unit can prioritize receiving voice commands that the user has frequently used in the past. The reception unit can also predict commands to be used during specific time periods based on the user's past voice command history and adjust the reception method accordingly. The reception unit can also analyze the user's past voice command history and apply the optimal speech recognition algorithm. This allows the reception unit to select the optimal reception method by analyzing the user's past voice command history. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit can input the user's past voice command history into a generative AI, which can then select the optimal reception method.

[0077] The reception unit can filter voice commands based on the user's current driving status and in-vehicle environment. For example, if the user is driving on a highway, the reception unit can only receive important voice commands. If the user is stuck in traffic, the reception unit can receive all voice commands. If the user is parked, the reception unit can receive detailed voice commands. This allows the reception unit to receive only appropriate commands by filtering them based on the user's driving status and in-vehicle environment. Some or all of the above processing in the reception unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception unit can input user driving status and in-vehicle environment data into a generation AI, which can then perform voice command filtering.

[0078] The reception unit can estimate the user's emotions and determine the priority of voice commands to receive based on the estimated emotions. For example, if the user is stressed, the reception unit can prioritize important voice commands. If the user is relaxed, the reception unit can also prioritize all voice commands equally. If the user is in a hurry, the reception unit can also prioritize voice commands that require quick action. In this way, the reception unit can prioritize important commands by determining the priority of 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without a generative AI. For example, the reception unit can input user emotion data into a generative AI, which can then determine the priority of voice commands.

[0079] The reception unit can prioritize receiving voice commands by considering the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize receiving voice commands related to that region. If the user is approaching a destination, the reception unit can also prioritize receiving voice commands related to that destination. If the user is at home, the reception unit can also prioritize receiving voice commands related to home. In this way, the reception unit can prioritize receiving commands that are highly relevant by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then prioritize receiving commands that are highly relevant.

[0080] The reception unit can analyze the user's social media activity when receiving a voice command and receive relevant commands. For example, if the reception unit indicates on social media that the user will attend a specific event, it can prioritize receiving voice commands related to that event. If the reception unit indicates on social media that the user will go to a specific place, it can also prioritize receiving voice commands related to that place. If the reception unit indicates on social media that the user will perform a specific activity, it can also prioritize receiving voice commands related to that activity. In this way, the reception unit can prioritize receiving relevant commands by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit can input the user's social media activity data into a generative AI, which can then receive relevant commands.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis method of voice commands based on the estimated user emotions. For example, if the user is stressed, the analysis unit can apply a simple analysis method and respond quickly. If the user is relaxed, the analysis unit can also apply a detailed analysis method and provide an accurate response. If the user is in a hurry, the analysis unit can apply a rapid analysis method and respond immediately. This allows the analysis unit to perform more appropriate analysis by adjusting the analysis 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the analysis method of voice commands.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the voice command during analysis. For example, the analysis unit can perform a detailed analysis for important voice commands and provide an accurate response. For general voice commands, the analysis unit can perform a standard analysis and provide a quick response. For low-priority voice commands, the analysis unit can perform a simplified analysis and provide a quick response. In this way, the analysis unit can perform a detailed analysis for important commands by adjusting the level of detail of the analysis based on the importance of the voice command. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input importance data of the voice command into a generation AI, and the generation AI can adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the voice command during analysis. For example, the analysis unit can apply a dedicated analysis algorithm for engine management to voice commands related to engine management. The analysis unit can also apply a dedicated analysis algorithm for audio to voice commands related to audio. The analysis unit can also apply a dedicated analysis algorithm for car navigation to voice commands related to car navigation. This allows the analysis unit to perform more appropriate analysis by applying different analysis algorithms depending on the category of the voice command. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the category data of the voice command into a generation AI, and the generation AI can apply different analysis algorithms.

[0084] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize the analysis of important voice commands. If the user is relaxed, the analysis unit can also analyze all voice commands equally. If the user is in a hurry, the analysis unit can also prioritize the analysis of voice commands that require quick action. In this way, the analysis unit can prioritize the analysis of important commands by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI, which can then determine the priority of analysis.

[0085] The analysis unit can determine the priority of analysis based on the timing of voice command submissions during analysis. For example, the analysis unit can prioritize the analysis of recently submitted voice commands. The analysis unit can also prioritize the analysis of voice commands submitted within a specific time period. If the user is in a hurry, the analysis unit can also prioritize the analysis of voice commands that require quick action. This enables rapid analysis by allowing the analysis unit to determine the priority of analysis based on the timing of voice command submissions. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input voice command submission timing data into a generation AI, which can then determine the priority of analysis.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the voice commands during analysis. For example, the analysis unit can prioritize the analysis of highly relevant voice commands. The analysis unit can also postpone the analysis of less relevant voice commands. The analysis unit can also prioritize the analysis of voice commands relevant to the user's current situation. In this way, the analysis unit can prioritize the analysis of highly relevant commands by adjusting the order of analysis based on the relevance of the voice commands. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input voice command relevance data into a generating AI, and the generating AI can adjust the order of analysis.

[0087] The control unit can estimate the user's emotions and adjust the operation of the car's functions based on the estimated emotions. For example, if the user is stressed, the control unit can simplify the operation and allow for quick operation. If the user is relaxed, the control unit can provide detailed instructions for precise operation. If the user is in a hurry, the control unit can provide quick instructions for immediate operation. In this way, the control unit can provide more appropriate operation by adjusting the operation 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using or without generative AI. For example, the control unit can input user emotion data into the generative AI, which can then adjust the operation method.

[0088] The control unit can adjust the level of detail of operations based on the importance of the vehicle's functions during operation. For example, the control unit can perform detailed operations for important vehicle functions, providing precise control. For general vehicle functions, the control unit can perform standard operations, providing quick control. For low-priority vehicle functions, the control unit can perform simplified operations, also providing quick control. In this way, the control unit can perform detailed operations for important functions by adjusting the level of detail of operations based on the importance of the vehicle's functions. Some or all of the above processing in the control unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the control unit can input vehicle function importance data into a generative AI, which can then adjust the level of detail of operations.

[0089] The control unit can apply different operating algorithms depending on the category of the vehicle's function during operation. For example, the control unit can apply a dedicated operating algorithm for engine management operations. The control unit can also apply a dedicated operating algorithm for audio operations. The control unit can also apply a dedicated operating algorithm for car navigation operations. This allows the control unit to perform more appropriate operations by applying different operating algorithms depending on the category of the vehicle's function. Some or all of the above processing in the control unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the control unit can input vehicle function category data into a generative AI, and the generative AI can apply different operating algorithms.

[0090] The control unit can estimate the user's emotions and determine the priority of operations based on the estimated emotions. For example, if the user is stressed, the control unit can prioritize important operations. If the user is relaxed, the control unit can perform all operations equally. If the user is in a hurry, the control unit can prioritize operations that require quick action. In this way, the control unit can prioritize important operations by determining the priority of operations 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using or without a generative AI. For example, the control unit can input user emotion data into a generative AI, which can then determine the priority of operations.

[0091] The control unit can determine the priority of operations based on the timing of vehicle function submissions during operation. For example, the control unit can prioritize recently submitted operations. It can also prioritize operations submitted within a specific time period. If the user is in a hurry, the control unit can prioritize operations that require quick action. This enables quick operation by allowing the control unit to prioritize operations based on the timing of vehicle function submissions. Some or all of the above processing in the control unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the control unit can input vehicle function submission timing data into a generative AI, which can then determine the priority of operations.

[0092] The control unit can adjust the order of operations based on the relationships between the car's functions during operation. For example, the control unit can prioritize operations that are highly relevant. The control unit can also postpone operations that are less relevant. The control unit can also prioritize operations that are relevant to the user's current situation. In this way, the control unit can prioritize operations that are highly relevant by adjusting the order of operations based on the relationships between the car's functions. Some or all of the above processing in the control unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the control unit can input data on the relationships between the car's functions into a generative AI, and the generative AI can adjust the order of operations.

[0093] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated emotions. For example, if the user is stressed, the customization unit can provide simple customization options to simplify the operation. If the user is relaxed, the customization unit can also provide detailed customization options and suggest settings tailored to the user's preferences. If the user is in a hurry, the customization unit can also provide an option to complete the customization quickly. This allows the customization unit to provide more appropriate customization by adjusting the customization 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using or without generative AI. For example, the customization unit can input user emotion data into a generative AI, which can then adjust the customization method.

[0094] The customization unit can analyze the user's past behavior history to select the optimal customization method during the customization process. For example, the customization unit can propose the optimal customization method based on the customization settings the user has previously selected. The customization unit can also predict and propose settings to be used during specific time periods based on the user's past behavior history. The customization unit can also analyze the user's past behavior history and propose the most efficient customization method. In this way, the customization unit can select the optimal customization method by analyzing the user's past behavior history. Some or all of the above-described processes in the customization unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the customization unit can input the user's past behavior history data into a generative AI, which can then select the optimal customization method.

[0095] The customization unit can customize the means of customization based on the user's current driving situation during the customization process. For example, if the user is driving on a highway, the customization unit can provide only the most important customization options. If the user is stuck in traffic, the customization unit can provide all customization options. If the user is parked, the customization unit can provide detailed customization options. This allows the customization unit to provide more appropriate customization by customizing the means of customization based on the user's current driving situation. Some or all of the above-described processes in the customization unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the customization unit can input the user's driving situation data into a generative AI, which can then customize the means of customization.

[0096] The customization unit can estimate the user's emotions and determine the priority of customizations based on the estimated emotions. For example, if the user is stressed, the customization unit can prioritize important customizations. If the user is relaxed, the customization unit can also distribute all customizations evenly. If the user is in a hurry, the customization unit can prioritize settings that require quick customization. In this way, the customization unit can prioritize important customizations by determining the priority of customizations 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using or without generative AI. For example, the customization unit can input user emotion data into a generative AI, which can then determine the priority of customizations.

[0097] The customization unit can select the optimal customization method by considering the user's geographical location information during customization. For example, if the user is in a specific region, the customization unit can provide customization options related to that region. If the user is approaching a destination, the customization unit can also provide customization options related to the destination. If the user is at home, the customization unit can also provide customization options related to home. In this way, the customization unit can select the optimal customization method by considering the user's geographical location information. Some or all of the above processing in the customization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the customization unit can input the user's geographical location information data into a generative AI, and the generative AI can select the optimal customization method.

[0098] The customization unit can analyze the user's social media activity during customization and propose customization options. For example, if the customization unit indicates on social media that the user will participate in a specific event, it can provide customization options related to that event. If the customization unit indicates on social media that the user will go to a specific place, it can also provide customization options related to that place. If the customization unit indicates on social media that the user will perform a specific activity, it can also provide customization options related to that activity. In this way, the customization unit can propose relevant customization options by analyzing the user's social media activity. Some or all of the above processing in the customization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the customization unit can input the user's social media activity data into a generative AI, and the generative AI can propose customization options.

[0099] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is stressed, the feedback unit can provide concise and easy-to-understand feedback. If the user is relaxed, the feedback unit can provide detailed feedback. If the user is in a hurry, the feedback unit can provide quick feedback. This allows the feedback unit to provide more appropriate feedback by adjusting the feedback 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using or without a generative AI. For example, the feedback unit can input user emotion data into a generative AI, which can then adjust the feedback method.

[0100] The feedback unit can select the optimal feedback method by referring to the user's past operation history when providing feedback. For example, the feedback unit can propose the optimal feedback method based on feedback the user has received in the past. The feedback unit can also predict and propose a feedback method to be used during a specific time period based on the user's past operation history. The feedback unit can also analyze the user's past operation history and propose the most efficient feedback method. In this way, the feedback unit can select the optimal feedback method by referring to the user's past operation history. Some or all of the above processing in the feedback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the feedback unit can input the user's past operation history data into a generative AI, and the generative AI can select the optimal feedback method.

[0101] The feedback unit can customize the means of feedback based on the user's current driving situation. For example, if the user is driving on a highway, the feedback unit can provide only important feedback. If the user is stuck in traffic, the feedback unit can provide all feedback. If the user is parked, the feedback unit can provide detailed feedback. This allows the feedback unit to provide more appropriate feedback by customizing the means of feedback based on the user's current driving situation. Some or all of the above processing in the feedback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the feedback unit can input the user's driving situation data into a generative AI, which can then customize the means of feedback.

[0102] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, the feedback unit can prioritize providing important feedback. If the user is relaxed, the feedback unit can also provide all feedback equally. If the user is in a hurry, the feedback unit can prioritize providing information that requires quick feedback. In this way, the feedback unit can prioritize important feedback by prioritizing it 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using or without a generative AI. For example, the feedback unit can input user emotion data into a generative AI, which can then determine the priority of feedback.

[0103] The feedback unit can select the optimal feedback method by considering the user's geographical location information when providing feedback. For example, if the user is in a specific region, the feedback unit can provide feedback related to that region. If the user is approaching their destination, the feedback unit can also provide feedback related to their destination. If the user is at home, the feedback unit can also provide feedback related to their home. In this way, the feedback unit can select the optimal feedback method by considering the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the feedback unit can input the user's geographical location data into a generative AI, which can then select the optimal feedback method.

[0104] The feedback unit can analyze the user's social media activity and suggest methods for providing feedback when providing feedback. For example, if the user indicates on social media that they will be participating in a specific event, the feedback unit can provide feedback related to that event. If the user indicates on social media that they will be going to a specific place, the feedback unit can also provide feedback related to that place. If the user indicates on social media that they will be performing a specific activity, the feedback unit can also provide feedback related to that activity. In this way, the feedback unit can suggest relevant methods for providing feedback by analyzing the user's social media activity. Some or all of the above processing in the feedback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the feedback unit can input the user's social media activity data into a generative AI, and the generative AI can suggest methods for providing feedback.

[0105] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0106] When receiving a user's voice command, the reception desk can analyze the user's tone and speed of voice to estimate the user's urgency. For example, if the user's voice sounds urgent, the reception desk can determine that it is a high-priority issue and respond immediately. Conversely, if the user's voice is calm, the reception desk can determine that it is not a high-priority issue and respond in the usual manner. Furthermore, if the user's voice is unstable, the reception desk can estimate that the user is experiencing stress and respond cautiously. In this way, the reception desk can estimate the urgency of the issue based on the user's tone and speed of voice and respond appropriately.

[0107] The analysis unit, when analyzing voice commands, can refer to the user's past command history and prioritize the analysis of similar commands. For example, by prioritizing the analysis of commands that the user has frequently used in the past, it can provide a quick response. It can also predict commands that the user will use during specific time periods based on their past command history and adjust the analysis priority accordingly. Furthermore, it can analyze the user's past command history and apply the most efficient analysis method. In this way, the analysis unit can select the optimal analysis method by referring to the user's past command history.

[0108] The control unit can adjust its operation method based on the user's current driving situation when operating the vehicle's functions. For example, if the user is driving on a highway, only essential operations can be performed, and other operations can be postponed. If the user is stuck in traffic, all operations can be performed. Furthermore, if the user is parked, detailed operations can be performed. In this way, the control unit can adjust its operation method based on the user's driving situation, enabling more appropriate operation.

[0109] The customization unit can estimate the user's emotions and adjust the customization content based on those emotions. For example, if the user is stressed, it can offer simple customization options to simplify the process. If the user is relaxed, it can offer detailed customization options and suggest settings tailored to the user's preferences. Furthermore, if the user is in a hurry, it can offer an option to complete the customization quickly. In this way, the customization unit can provide more appropriate customization by adjusting the content according to the user's emotions.

[0110] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is stressed, it can provide concise and easy-to-understand feedback. If the user is relaxed, it can provide detailed feedback. Furthermore, if the user is in a hurry, it can provide quick feedback. In this way, the feedback unit can provide more appropriate feedback by adjusting the content of the feedback according to the user's emotions.

[0111] The reception unit can prioritize receiving voice commands by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize receiving voice commands related to that region. Similarly, if the user is approaching a destination, it can prioritize receiving voice commands related to that destination. Furthermore, if the user is at home, it can prioritize receiving voice commands related to home. In this way, the reception unit can prioritize receiving commands that are highly relevant by considering the user's geographical location.

[0112] The analysis unit can adjust its analysis method when analyzing voice commands, taking into account the user's current driving situation. For example, if the user is driving on a highway, it can analyze only important commands in detail, while simplifying the analysis of other commands. If the user is stuck in traffic, it can analyze all commands in detail. Furthermore, if the user is parked, it can perform a detailed analysis. In this way, the analysis unit can adjust its analysis method based on the user's driving situation, enabling more appropriate analysis.

[0113] The control unit can estimate the user's emotions and adjust the operation of the car's functions based on those emotions. For example, if the user is stressed, the operation method can be simplified for quicker operation. If the user is relaxed, detailed instructions can be provided for precise operation. Furthermore, if the user is in a hurry, quick instructions can be provided for immediate operation. In this way, the control unit can adjust the operation method according to the user's emotions, enabling more appropriate operation.

[0114] The customization unit can analyze the user's past behavior history to select the optimal customization method during the customization process. For example, it can suggest the optimal customization method based on the customization settings the user has previously selected. It can also predict and suggest settings to be used during specific time periods based on the user's past behavior history. Furthermore, it can analyze the user's past behavior history and suggest the most efficient customization method. In this way, the customization unit can select the optimal customization method by analyzing the user's past behavior history.

[0115] The feedback unit can analyze a user's social media activity and suggest appropriate feedback methods when providing feedback. For example, if a user indicates on social media that they will be attending a specific event, the feedback unit can provide feedback related to that event. Similarly, if a user indicates on social media that they will be going to a specific location, the feedback unit can provide feedback related to that location. Furthermore, if a user indicates on social media that they will be engaging in a specific activity, the feedback unit can provide feedback related to that activity. In this way, the feedback unit can suggest appropriate feedback methods by analyzing a user's social media activity.

[0116] The following briefly describes the processing flow for example form 2.

[0117] Step 1: The reception desk accepts voice commands. Voice commands include "start the engine," "turn on the air conditioner," and "use the navigation system to find the nearest gas station." The reception desk can accept voice commands using voice recognition technology and generative AI. Step 2: The analysis unit analyzes the voice commands received by the reception unit. The analysis unit can analyze the voice commands using natural language processing technology and generative AI. Step 3: The control unit operates the car's functions based on the voice commands analyzed by the analysis unit. The control unit can operate in-car functions such as engine management, brakes, locking, audio, air conditioning, and car navigation. The control unit can also operate the car's functions using generative AI.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the reception unit, analysis unit, and operation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives voice commands using the microphone 38B of the smart device 14 and generates voice data by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the voice commands using natural language processing technology. The operation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and operates various functions of the car based on the analyzed voice commands. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0122] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the reception unit, analysis unit, and operation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives voice commands using the microphone 238 of the smart glasses 214 and generates voice data using the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the voice commands using natural language processing technology. The operation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and operates various functions of the car based on the analyzed voice commands. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0138] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the reception unit, analysis unit, and operation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives voice commands using the microphone 238 of the headset terminal 314 and generates voice data by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes voice commands using natural language processing technology. The operation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and operates various functions of the car based on the analyzed voice commands. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[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 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.

[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 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.

[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 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.

[0170] Each of the multiple elements described above, including the reception unit, analysis unit, and operation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives voice commands using the microphone 238 of the robot 414 and generates voice data by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the voice commands using natural language processing technology. The operation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and operates various functions of the car based on the analyzed voice commands. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] (Note 1) A reception area that accepts voice commands, An analysis unit that analyzes voice commands received by the reception unit, The vehicle comprises an operating unit that operates the vehicle's functions based on voice commands analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned operating unit is Control in-car functions such as engine management, brakes, locking, audio, air conditioning, and car navigation. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze voice commands using natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned operating unit is It features a customization section that provides customization functions based on user behavior and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned operating unit is It features a feedback unit that provides real-time response and feedback. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of voice command acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system analyzes the user's past voice command history and selects the optimal response method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When a voice command is received, filtering is performed based on the user's current driving status and in-vehicle environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice commands to accept based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) 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 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the voice command analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the voice command. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the voice command. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the voice commands were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned operating unit is It estimates the user's emotions and adjusts how the car's functions are operated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned operating unit is During operation, the level of detail of the operation is adjusted based on the importance of the car's functions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned operating unit is When operating the vehicle, different operating algorithms are applied depending on the category of the vehicle's function. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned operating unit is It estimates the user's emotions and determines the priority of actions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned operating unit is During operation, the priority of operations is determined based on the timing of the vehicle's function requests. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned operating unit is During operation, the sequence of operations is adjusted based on the relationships between the car's functions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned customization unit is During customization, the system analyzes the user's past behavior history to select the optimal customization method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned customization unit is During customization, the customization method is customized based on the user's current driving conditions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned customization unit is During customization, the optimal customization method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned customization unit is During customization, we analyze the user's social media activity and suggest customization options. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is When providing feedback, the system selects the most suitable feedback method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, the feedback method is customized based on the user's current driving situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback unit is When providing feedback, the optimal feedback method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and suggest ways to provide feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0190] 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, An analysis unit that analyzes voice commands received by the reception unit, The vehicle comprises an operating unit that operates the vehicle's functions based on voice commands analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned operating unit is Control in-car functions such as engine management, brakes, locking, audio, air conditioning, and car navigation. The system according to feature 1.

3. The aforementioned analysis unit, Analyze voice commands using natural language processing. The system according to feature 1.

4. The aforementioned operating unit is It features a customization section that provides customization functions based on user behavior and preferences. The system according to feature 1.

5. The aforementioned operating unit is It features a feedback unit that provides real-time response and feedback. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of voice command acceptance based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is The system analyzes the user's past voice command history and selects the optimal response method. The system according to feature 1.

8. The aforementioned reception unit is When a voice command is received, filtering is performed based on the user's current driving status and in-vehicle environment. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice commands to accept based on the estimated emotions. The system according to feature 1.

10. 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 according to feature 1.

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