system

Abstract

The system according to this embodiment aims to enable motorcyclists to operate it safely and conveniently by voice control without using their hands. [Solution] The system according to the embodiment comprises a speech recognition unit, a response generation unit, and an emergency response unit. The speech recognition unit recognizes speech. The response generation unit generates a response based on the speech recognized by the speech recognition unit. The emergency response unit responds to an emergency based on the response generated by the response generation 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 performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a motorcycle rider to perform operations by voice without using hands, and it is difficult to achieve both safety and convenience.

[0005] The system according to the embodiment aims to enable a motorcycle rider to perform operations by voice without using hands and to be used safely and conveniently.

Means for Solving the Problems

[0006] The system according to the embodiment includes a voice recognition unit, a response generation unit, and an emergency response unit. The voice recognition unit recognizes voice. The response generation unit generates a response based on the voice recognized by the voice recognition unit. The emergency response unit responds to an emergency situation based on the response generated by the response generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can be operated by voice without using hands, making it safe and convenient for motorcyclists to use. [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 motorcycle voice control system according to an embodiment of the present invention is a system that allows motorcyclists to fully control operations such as navigation, calls, and music playback by voice without using their hands. This system places particular emphasis on safety and convenience and also enables emergency response. The motorcycle voice control system is equipped with an advanced voice recognition system that utilizes a large-scale language model and AI that handles multiple modals. This system has a highly accurate real-time response function and is also equipped with an automatic response function for emergencies. To improve the safety and convenience of motorcyclists, it is designed to allow operation of multiple functions without using hands. For example, the motorcycle voice control system is expected to reduce the accident rate by 20% and shorten operation time by 30%, and is expected to satisfy more than 90% of users. The target is mainly motorcycle enthusiasts aged 20 to 50 who are interested in safety and technology. This system provides full function access by voice to solve the problems of ensuring safety while operating a motorcycle and the lack of ease of function operation. The market size is estimated to be approximately 50 billion yen per year, and the market penetration rate in the first year is estimated at 20%, aiming for a market size of 10 billion yen. The growing motorcycle accessories market and increasing interest in technology make now the ideal time to enter the market. The vision is to improve the safety and comfort of motorcyclists, reduce accident rates, and "transform the future of motorcycling, now." This will enable motorcyclists to fully control functions such as navigation, calls, and music playback using only their voice, without having to use their hands.

[0029] The voice-controlled motorcycle system according to this embodiment comprises a voice recognition unit, a response generation unit, and an emergency response unit. The voice recognition unit recognizes speech. The voice recognition unit analyzes speech using, for example, a speech recognition algorithm and recognizes voice commands. The voice recognition unit inputs speech using, for example, a microphone and applies the speech recognition algorithm. The voice recognition unit improves the accuracy of speech recognition by, for example, removing ambient noise using noise cancellation technology. The response generation unit generates a response based on the speech recognized by the voice recognition unit. The response generation unit generates an appropriate response using, for example, natural language processing technology. The response generation unit generates a response to a voice command using, for example, a generation AI. The response generation unit understands the user's intent and generates an appropriate response. The emergency response unit responds to an emergency based on the response generated by the response generation unit. The emergency response unit automatically contacts emergency contacts, for example. The emergency response unit instructs the user on appropriate actions in an emergency, for example. The emergency response unit automatically notifies emergency services, for example. As a result, the voice control system for motorcycles according to this embodiment is capable of voice recognition, response generation, and emergency response.

[0030] The voice recognition unit recognizes speech. For example, the voice recognition unit analyzes speech using a voice recognition algorithm and recognizes voice commands. Specifically, the voice recognition unit uses a microphone to input the rider's voice and converts that voice data into a digital signal. The converted digital signal is analyzed by the voice recognition algorithm and recognized as a voice command. The voice recognition algorithm uses, for example, deep learning technology, achieving high recognition accuracy by learning from a large amount of voice data. Furthermore, the voice recognition unit uses noise cancellation technology to remove ambient noise and improve the accuracy of voice recognition. Noise cancellation technology is extremely important because there is a lot of noise such as wind noise and engine noise while riding a motorcycle. Noise cancellation technology removes noise components from the voice data collected by the microphone in real time, providing clear voice data to the voice recognition algorithm. As a result, the voice recognition unit can recognize voice commands with high accuracy even in noisy environments.

[0031] The response generation unit generates a response based on the speech recognized by the speech recognition unit. The response generation unit generates an appropriate response using, for example, natural language processing technology. Specifically, the response generation unit analyzes the voice command received from the speech recognition unit and understands the user's intent. Natural language processing technology is used to analyze the context and meaning of the voice command and generate an appropriate response. The response generation unit generates a response to the voice command using, for example, generative AI. Generative AI is based on a large-scale language model and can generate a variety of responses. For example, if the user inputs the voice command "Tell me the nearest gas station," the response generation unit uses the generative AI to generate a response such as "The nearest gas station is XX." The response generation unit can also consider the user's past command history and current situation in order to understand the user's intent and generate an appropriate response. This allows the response generation unit to provide the user with a natural and appropriate response.

[0032] The emergency response unit responds to emergencies based on responses generated by the response generation unit. For example, the emergency response unit automatically contacts emergency contacts. Specifically, if a user enters a voice command indicating an emergency, such as "I've been in an accident" or "I need help," the emergency response unit automatically contacts pre-registered emergency contacts. The emergency response unit also instructs the user on appropriate actions in an emergency. For example, if a user enters the voice command "I've been in an accident," the emergency response unit provides voice instructions such as "Please move to a safe place." Furthermore, the emergency response unit automatically notifies emergency services. For example, if a user enters the voice command "Call an ambulance," the emergency response unit sends an emergency notification to emergency services, including the user's current location information. This allows the emergency response unit to respond quickly and appropriately when a user faces an emergency. The emergency response unit plays a crucial role in ensuring user safety and supporting a rapid response.

[0033] The navigation unit is controlled by a voice recognition unit and a response generation unit, and provides navigation information to the user. The navigation unit, for example, uses GPS technology to determine the current location and guides the user to their destination. The navigation unit, for example, uses map data to calculate the optimal route. The navigation unit, for example, obtains real-time traffic information and suggests a route that avoids congestion. In this way, the navigation unit can provide navigation information to the user. Some or all of the above-described processes in the navigation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the navigation unit can input GPS data and map data into a generative AI and have the generative AI perform the calculation of the optimal route.

[0034] The call unit is controlled by a speech recognition unit and a response generation unit, and provides the user with call functionality. The call unit establishes a call using, for example, a communication protocol. The call unit compresses audio data using, for example, an audio codec to improve communication quality. The call unit removes background noise during a call using, for example, noise cancellation technology. This enables the call unit to provide the user with call functionality. Some or all of the above processing in the call unit may be performed using a generation AI, or not. For example, the call unit can input audio data into a generation AI and have the generation AI perform audio data compression and communication quality improvement.

[0035] The music playback unit is controlled by a speech recognition unit and a response generation unit, and provides the user with music playback functionality. The music playback unit, for example, recognizes the format of a music file and applies an appropriate playback algorithm. The music playback unit, for example, selects songs from a music library that suit the user's preferences. The music playback unit, for example, performs acoustic processing to optimize sound quality. In this way, the music playback unit can provide the user with music playback functionality. Some or all of the above-described processes in the music playback unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the music playback unit can input the format of a music file into a generation AI and have the generation AI execute the application of an appropriate playback algorithm.

[0036] The speech recognition unit can analyze the ambient noise level in real time during speech recognition and optimize noise cancellation. For example, if the motorcycle engine noise is loud, the speech recognition unit can enhance noise cancellation to improve the accuracy of speech recognition. For example, if the wind noise is strong, the speech recognition unit can cut out a specific frequency band to maintain the accuracy of speech recognition. For example, if the ambient traffic noise is loud, the speech recognition unit can adaptively adjust noise cancellation to maintain the accuracy of speech recognition. This allows for optimization of noise cancellation according to the ambient noise level. Some or all of the above processing in the speech recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the speech recognition unit can input ambient noise data into a generative AI and have the generative AI perform noise cancellation optimization.

[0037] The speech recognition unit can learn the user's speech patterns and generate an individually optimized speech recognition model during speech recognition. For example, the speech recognition unit can learn the user's speech speed and accent and generate an individually optimized speech recognition model. For example, the speech recognition unit can learn the user's specific phrases and verbal tics to improve the accuracy of speech recognition. For example, the speech recognition unit can continuously learn the characteristics of the user's speech and update the speech recognition model. This allows the speech recognition model to be optimized according to the user's speech patterns. Some or all of the above processes in the speech recognition unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the speech recognition unit can input the user's speech data into a generative AI and have the generative AI perform speech pattern learning and speech recognition model generation.

[0038] The speech recognition unit can recognize regional languages ​​and dialects by taking into account the user's geographical location information during speech recognition. For example, if the user is in the Kansai region, the speech recognition unit will recognize the Kansai dialect and generate an appropriate response. For example, if the user is in the Tohoku region, the speech recognition unit will recognize the Tohoku dialect and generate an appropriate response. For example, if the user is overseas, the speech recognition unit will recognize the language and dialect of that region and generate an appropriate response. This allows the system to recognize regional languages ​​and dialects according to the user's geographical location information. Some or all of the above processing in the speech recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the speech recognition unit can input the user's geographical location information into a generative AI and have the generative AI perform the recognition of regional languages ​​and dialects.

[0039] The speech recognition unit can improve the recognition accuracy of specific commands by referring to the user's past speech history during speech recognition. For example, the speech recognition unit may prioritize recognizing commands that the user has frequently used in the past. For example, the speech recognition unit may learn specific phrases and verbal tics from the user's past speech history to improve recognition accuracy. For example, the speech recognition unit may analyze the user's past speech history to improve the recognition accuracy of specific commands. This improves the recognition accuracy of specific commands based on the user's past speech history. Some or all of the above processing in the speech recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the speech recognition unit may input the user's past speech history into a generative AI and have the generative AI perform the task of improving the recognition accuracy of specific commands.

[0040] The response generation unit can generate the optimal response by considering the user's current situation and context when generating a response. For example, if the user is riding a motorcycle, the response generation unit will generate a concise response to allow the user to concentrate on driving. For example, if the user is stopped, the response generation unit will generate a response that includes detailed information. For example, if the user is in an emergency, the response generation unit will generate a quick and appropriate response. This allows the system to generate the optimal response according to the user's current situation and context. Some or all of the above-described processes in the response generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the response generation unit can input the user's current situation data into the generation AI and have the generation AI perform the generation of the optimal response.

[0041] The response generation unit can provide individually optimized responses by referring to the user's past response history when generating responses. For example, the response generation unit can generate an optimal response based on the response style the user has preferred in the past. For example, the response generation unit can generate a response suitable for a specific situation from the user's past response history. For example, the response generation unit can analyze the user's past response history and provide individually optimized responses. This allows the response generation unit to provide individually optimized responses based on the user's past response history. Some or all of the above processing in the response generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response generation unit can input the user's past response history into a generation AI and have the generation AI perform the generation of individually optimized responses.

[0042] The response generation unit can generate responses that include region-specific information, taking into account the user's geographical location information when generating responses. For example, if the user is in a specific region, the response generation unit can generate a response that includes traffic information for that region. For example, if the user is in a tourist destination, the response generation unit can generate a response that includes tourist information. For example, if the user is in a specific region, the response generation unit can generate a response that includes weather information for that region. This makes it possible to generate responses that include region-specific information according to the user's geographical location information. Some or all of the above processing in the response generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response generation unit can input the user's geographical location information into a generation AI and cause the generation AI to perform the generation of responses that include region-specific information.

[0043] The response generation unit can analyze the user's social media activity and generate a response that includes relevant information when generating a response. For example, the response generation unit can generate a relevant response based on information shared by the user on social media. For example, the response generation unit can analyze the content of the user's social media posts and generate an appropriate response. For example, the response generation unit can refer to the user's social media activity history and generate a response that includes relevant information. This makes it possible to generate a response that includes relevant information based on the user's social media activity. Some or all of the above processing in the response generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response generation unit can input the user's social media data into a generation AI and have the generation AI perform the generation of a response that includes relevant information.

[0044] The emergency response unit can monitor the user's current health status and vital signs in real time during an emergency and take the optimal response. For example, the emergency response unit can monitor the user's heart rate and blood pressure and take emergency action if there are abnormalities. For example, the emergency response unit can monitor the user's respiratory status and take appropriate action if there are abnormalities. For example, the emergency response unit can monitor the user's body temperature and take rapid action if there are abnormalities. This allows for the provision of the optimal emergency response based on the user's health status and vital signs. Some or all of the above processing in the emergency response unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the emergency response unit can input the user's vital sign data into a generation AI and have the generation AI perform the optimal emergency response.

[0045] The emergency response unit can provide individually optimized responses by referring to the user's past emergency history during an emergency. For example, the emergency response unit can provide the optimal response based on the user's past emergency experiences. For example, the emergency response unit can prioritize providing a specific response method based on the user's past emergency history. For example, the emergency response unit can analyze the user's past emergency history and provide individually optimized responses. This allows the unit to provide individually optimized responses based on the user's past emergency history. Some or all of the above processing in the emergency response unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the emergency response unit can input the user's past emergency history into a generation AI and have the generation AI perform the task of providing individually optimized responses.

[0046] The emergency response unit can automatically notify the user of the nearest emergency service, taking into account the user's geographical location during an emergency. For example, if the user is involved in an accident, the emergency response unit will automatically call the nearest ambulance. For example, if the user complains of feeling unwell, the emergency response unit will automatically guide the user to the nearest hospital. For example, if the user is lost, the emergency response unit will automatically notify the user of the nearest police station. This allows the system to automatically notify the user of the nearest emergency service based on their geographical location. Some or all of the above-described processes in the emergency response unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the emergency response unit can input the user's geographical location information into a generation AI and have the generation AI execute the notification of the nearest emergency service.

[0047] The emergency response unit can analyze a user's social media activity during an emergency and take appropriate action, including relevant information. For example, if a user requests help on social media, the emergency response unit can take action based on that information. For example, the emergency response unit can analyze the content of a user's social media posts and take appropriate action. For example, the emergency response unit can refer to a user's social media activity history and take action, including relevant information. This allows the emergency response unit to take action, including relevant information, based on the user's social media activity. Some or all of the above processing in the emergency response unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the emergency response unit can input the user's social media data into a generative AI and have the generative AI perform an emergency response, including relevant information.

[0048] The navigation unit can analyze real-time traffic information during navigation and provide the optimal route. For example, the navigation unit can suggest the optimal route based on real-time traffic congestion information. For example, the navigation unit can suggest a detour route based on real-time road construction information. For example, the navigation unit can suggest a safe route based on real-time accident information. This allows the navigation unit to provide the optimal route based on real-time traffic information. Some or all of the above processing in the navigation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the navigation unit can input real-time traffic data into a generation AI and have the generation AI perform the task of suggesting the optimal route.

[0049] The navigation unit can provide individually optimized routes by referring to the user's past travel history during navigation. For example, the navigation unit may suggest the optimal route based on routes the user has used in the past. For example, the navigation unit may suggest a route that avoids congestion based on the user's past travel history. For example, the navigation unit may analyze the user's past travel history and suggest the most efficient route. This allows the navigation unit to provide individually optimized routes based on the user's past travel history. Some or all of the above processing in the navigation unit may be performed using a generative AI, or not. For example, the navigation unit may input the user's past travel history into a generative AI and have the generative AI perform the task of suggesting individually optimized routes.

[0050] The navigation unit can provide navigation that includes region-specific information, taking into account the user's geographical location during navigation. For example, if the user is in a specific region, the navigation unit can provide navigation that includes traffic information for that region. For example, if the user is in a tourist destination, the navigation unit can provide navigation that includes tourist information. For example, if the user is in a specific region, the navigation unit can provide navigation that includes weather information for that region. This allows the navigation unit to provide navigation that includes region-specific information based on the user's geographical location. Some or all of the above processing in the navigation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the navigation unit can input the user's geographical location information into a generation AI and have the generation AI perform the task of providing navigation that includes region-specific information.

[0051] The navigation unit can analyze the user's social media activity during navigation and provide navigation that includes relevant information. For example, the navigation unit can provide relevant navigation based on information shared by the user on social media. For example, the navigation unit can analyze the content of the user's social media posts and provide appropriate navigation. For example, the navigation unit can refer to the user's social media activity history and provide navigation that includes relevant information. This allows the navigation unit to provide navigation that includes relevant information based on the user's social media activity. Some or all of the above processing in the navigation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the navigation unit can input the user's social media data into a generative AI and have the generative AI perform the task of providing navigation that includes relevant information.

[0052] The call unit can analyze the ambient noise level in real time during a call and optimize noise cancellation. For example, if the motorcycle engine noise is loud, the call unit will enhance noise cancellation to improve call quality. For example, if the wind noise is strong, the call unit will cut out a specific frequency band to maintain call quality. For example, if the ambient traffic noise is loud, the call unit will adaptively adjust noise cancellation to maintain call quality. This allows noise cancellation to be optimized according to the ambient noise level. Some or all of the above processing in the call unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the call unit can input ambient noise data into a generative AI and have the generative AI perform noise cancellation optimization.

[0053] The call unit can, during a call, refer to the user's past call history and provide individually optimized call settings. For example, the call unit can provide optimal call settings based on the user's past preferred call settings. For example, the call unit can prioritize providing specific call settings from the user's past call history. For example, the call unit can analyze the user's past call history and provide individually optimized call settings. This allows the call unit to provide individually optimized call settings based on the user's past call history. Some or all of the above processing in the call unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the call unit can input the user's past call history into a generative AI and have the generative AI perform the task of providing individually optimized call settings.

[0054] The call unit can provide region-specific call settings during a call, taking into account the user's geographical location information. For example, if the user is in a specific region, the call unit can provide call settings that correspond to the communication conditions in that region. For example, if the user is overseas, the call unit can provide call settings suitable for international calls. For example, if the user is in a specific region, the call unit can provide call settings that correspond to the communication provider in that region. This allows for the provision of region-specific call settings based on the user's geographical location information. Some or all of the above processing in the call unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the call unit can input the user's geographical location information into a generation AI and have the generation AI perform the provision of region-specific call settings.

[0055] The call unit can analyze the user's social media activity during a call and provide call settings that include relevant information. For example, the call unit can provide relevant call settings based on information the user has shared on social media. For example, the call unit can analyze the content of the user's social media posts and provide appropriate call settings. For example, the call unit can refer to the user's social media activity history and provide call settings that include relevant information. This allows the call unit to provide call settings that include relevant information based on the user's social media activity. Some or all of the above processing in the call unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the call unit can input the user's social media data into a generative AI and have the generative AI perform the task of providing call settings that include relevant information.

[0056] The music playback unit can provide optimal music by considering the user's current situation and context during music playback. For example, if the user is riding a motorcycle, the music playback unit can provide music that allows the user to concentrate on driving. For example, if the user is stopped, the music playback unit can provide relaxing music. For example, if the user is in an emergency, the music playback unit can provide calming music. In this way, the music playback unit can provide optimal music according to the user's current situation and context. Some or all of the above processing in the music playback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the music playback unit can input data on the user's current situation into a generative AI and have the generative AI perform the task of providing optimal music.

[0057] The music playback unit can provide individually optimized music by referring to the user's past music history during music playback. For example, the music playback unit can provide optimal music based on music the user has liked in the past. For example, the music playback unit can provide music suitable for a specific situation from the user's past music history. For example, the music playback unit can analyze the user's past music history and provide individually optimized music. This makes it possible to provide individually optimized music based on the user's past music history. Some or all of the above processing in the music playback unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the music playback unit can input the user's past music history into a generation AI and have the generation AI perform the task of providing individually optimized music.

[0058] The music playback unit can provide region-specific music by considering the user's geographical location information during music playback. For example, if the user is in a specific region, the music playback unit can provide traditional music of that region. For example, if the user is in a tourist destination, the music playback unit can provide tourist music of that region. For example, if the user is in a specific region, the music playback unit can provide popular music of that region. In this way, region-specific music can be provided based on the user's geographical location information. Some or all of the above processing in the music playback unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the music playback unit can input the user's geographical location information into a generation AI and have the generation AI perform the task of providing region-specific music.

[0059] The music playback unit can analyze the user's social media activity and provide relevant music during music playback. For example, the music playback unit can provide relevant music based on music the user has shared on social media. For example, the music playback unit can analyze the content of the user's social media posts and provide appropriate music. For example, the music playback unit can refer to the user's social media activity history and provide relevant music. This allows the music playback unit to provide relevant music based on the user's social media activity. Some or all of the above processing in the music playback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the music playback unit can input the user's social media data into a generative AI and have the generative AI perform the task of providing relevant music.

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

[0061] The speech recognition unit can analyze the user's speaking speed in real time and adjust the sensitivity of speech recognition according to the speaking speed. For example, if the user speaks quickly, the speech recognition unit increases its sensitivity to handle fast speech. Conversely, if the user speaks slowly, it returns the sensitivity to normal to enable natural conversation. Furthermore, if the user's speaking speed fluctuates, the speech recognition unit can adaptively adjust its sensitivity accordingly. This allows for optimization of speech recognition accuracy according to the user's speaking speed.

[0062] The navigation system can analyze a user's past travel history and predict and suggest routes and destinations that the user might prefer. For example, it can predict and guide the user to their next destination based on places the user has frequently visited in the past. It can also learn the characteristics of routes that the user prefers (e.g., scenic routes, routes with low traffic) and suggest routes based on those characteristics. Furthermore, it can analyze a user's past travel history and suggest the optimal route for a specific time of day. This allows for individually optimized navigation based on the user's past travel history.

[0063] The call function can analyze a user's call history and determine call priorities based on the frequency and content of calls with specific individuals. For example, it can prioritize calls with individuals the user frequently speaks with. It can also analyze the content of calls with specific individuals (e.g., work-related, family-related) and adjust call priorities accordingly. Furthermore, it can analyze a user's call history to determine call priorities for specific time periods. This allows for individually optimized call priorities based on the user's call history.

[0064] The music playback unit can analyze the user's music playback history and provide optimal music according to specific times of day and situations. For example, it can provide music suitable for the morning based on the music the user prefers during their morning commute. It can also provide music suitable for exercise based on the music the user prefers while exercising. Furthermore, it can analyze the user's music playback history and provide music tailored to specific seasons or events. This allows for the provision of individually optimized music based on the user's music playback history.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The speech recognition unit recognizes speech. For example, it analyzes speech using a speech recognition algorithm and recognizes voice commands. The speech recognition unit uses a microphone to input speech and uses noise cancellation technology to remove ambient noise and improve the accuracy of speech recognition. Step 2: The response generation unit generates a response based on the speech recognized by the speech recognition unit. For example, it uses natural language processing technology or generative AI to generate an appropriate response, understanding the user's intent and generating a response accordingly. Step 3: The emergency response unit responds to the emergency based on the response generated by the response generation unit. For example, it automatically contacts emergency contacts, instructs the user on appropriate actions to take in the emergency, and automatically notifies emergency services.

[0067] (Example of form 2) The motorcycle voice control system according to an embodiment of the present invention is a system that allows motorcyclists to fully control operations such as navigation, calls, and music playback by voice without using their hands. This system places particular emphasis on safety and convenience and also enables emergency response. The motorcycle voice control system is equipped with an advanced voice recognition system that utilizes a large-scale language model and AI that handles multiple modals. This system has a highly accurate real-time response function and is also equipped with an automatic response function for emergencies. To improve the safety and convenience of motorcyclists, it is designed to allow operation of multiple functions without using hands. For example, the motorcycle voice control system is expected to reduce the accident rate by 20% and shorten operation time by 30%, and is expected to satisfy more than 90% of users. The target is mainly motorcycle enthusiasts aged 20 to 50 who are interested in safety and technology. This system provides full function access by voice to solve the problems of ensuring safety while operating a motorcycle and the lack of ease of function operation. The market size is estimated to be approximately 50 billion yen per year, and the market penetration rate in the first year is estimated at 20%, aiming for a market size of 10 billion yen. The growing motorcycle accessories market and increasing interest in technology make now the ideal time to enter the market. The vision is to improve the safety and comfort of motorcyclists, reduce accident rates, and "transform the future of motorcycling, now." This will enable motorcyclists to fully control functions such as navigation, calls, and music playback using only their voice, without having to use their hands.

[0068] The voice-controlled motorcycle system according to this embodiment comprises a voice recognition unit, a response generation unit, and an emergency response unit. The voice recognition unit recognizes speech. The voice recognition unit analyzes speech using, for example, a speech recognition algorithm and recognizes voice commands. The voice recognition unit inputs speech using, for example, a microphone and applies the speech recognition algorithm. The voice recognition unit improves the accuracy of speech recognition by, for example, removing ambient noise using noise cancellation technology. The response generation unit generates a response based on the speech recognized by the voice recognition unit. The response generation unit generates an appropriate response using, for example, natural language processing technology. The response generation unit generates a response to a voice command using, for example, a generation AI. The response generation unit understands the user's intent and generates an appropriate response. The emergency response unit responds to an emergency based on the response generated by the response generation unit. The emergency response unit automatically contacts emergency contacts, for example. The emergency response unit instructs the user on appropriate actions in an emergency, for example. The emergency response unit automatically notifies emergency services, for example. As a result, the voice control system for motorcycles according to this embodiment is capable of voice recognition, response generation, and emergency response.

[0069] The voice recognition unit recognizes speech. For example, the voice recognition unit analyzes speech using a voice recognition algorithm and recognizes voice commands. Specifically, the voice recognition unit uses a microphone to input the rider's voice and converts that voice data into a digital signal. The converted digital signal is analyzed by the voice recognition algorithm and recognized as a voice command. The voice recognition algorithm uses, for example, deep learning technology, achieving high recognition accuracy by learning from a large amount of voice data. Furthermore, the voice recognition unit uses noise cancellation technology to remove ambient noise and improve the accuracy of voice recognition. Noise cancellation technology is extremely important because there is a lot of noise such as wind noise and engine noise while riding a motorcycle. Noise cancellation technology removes noise components from the voice data collected by the microphone in real time, providing clear voice data to the voice recognition algorithm. As a result, the voice recognition unit can recognize voice commands with high accuracy even in noisy environments.

[0070] The response generation unit generates a response based on the speech recognized by the speech recognition unit. The response generation unit generates an appropriate response using, for example, natural language processing technology. Specifically, the response generation unit analyzes the voice command received from the speech recognition unit and understands the user's intent. Natural language processing technology is used to analyze the context and meaning of the voice command and generate an appropriate response. The response generation unit generates a response to the voice command using, for example, generative AI. Generative AI is based on a large-scale language model and can generate a variety of responses. For example, if the user inputs the voice command "Tell me the nearest gas station," the response generation unit uses the generative AI to generate a response such as "The nearest gas station is XX." The response generation unit can also consider the user's past command history and current situation in order to understand the user's intent and generate an appropriate response. This allows the response generation unit to provide the user with a natural and appropriate response.

[0071] The emergency response unit responds to emergencies based on responses generated by the response generation unit. For example, the emergency response unit automatically contacts emergency contacts. Specifically, if a user enters a voice command indicating an emergency, such as "I've been in an accident" or "I need help," the emergency response unit automatically contacts pre-registered emergency contacts. The emergency response unit also instructs the user on appropriate actions in an emergency. For example, if a user enters the voice command "I've been in an accident," the emergency response unit provides voice instructions such as "Please move to a safe place." Furthermore, the emergency response unit automatically notifies emergency services. For example, if a user enters the voice command "Call an ambulance," the emergency response unit sends an emergency notification to emergency services, including the user's current location information. This allows the emergency response unit to respond quickly and appropriately when a user faces an emergency. The emergency response unit plays a crucial role in ensuring user safety and supporting a rapid response.

[0072] The navigation unit is controlled by a voice recognition unit and a response generation unit, and provides navigation information to the user. The navigation unit, for example, uses GPS technology to determine the current location and guides the user to their destination. The navigation unit, for example, uses map data to calculate the optimal route. The navigation unit, for example, obtains real-time traffic information and suggests a route that avoids congestion. In this way, the navigation unit can provide navigation information to the user. Some or all of the above-described processes in the navigation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the navigation unit can input GPS data and map data into a generative AI and have the generative AI perform the calculation of the optimal route.

[0073] The call unit is controlled by a speech recognition unit and a response generation unit, and provides the user with call functionality. The call unit establishes a call using, for example, a communication protocol. The call unit compresses audio data using, for example, an audio codec to improve communication quality. The call unit removes background noise during a call using, for example, noise cancellation technology. This enables the call unit to provide the user with call functionality. Some or all of the above processing in the call unit may be performed using a generation AI, or not. For example, the call unit can input audio data into a generation AI and have the generation AI perform audio data compression and communication quality improvement.

[0074] The music playback unit is controlled by a speech recognition unit and a response generation unit, and provides the user with music playback functionality. The music playback unit, for example, recognizes the format of a music file and applies an appropriate playback algorithm. The music playback unit, for example, selects songs from a music library that suit the user's preferences. The music playback unit, for example, performs acoustic processing to optimize sound quality. In this way, the music playback unit can provide the user with music playback functionality. Some or all of the above-described processes in the music playback unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the music playback unit can input the format of a music file into a generation AI and have the generation AI execute the application of an appropriate playback algorithm.

[0075] The speech recognition unit can estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated emotions. For example, if the user is nervous, the speech recognition unit can increase the sensitivity of speech recognition to reduce misrecognition. For example, if the user is relaxed, the speech recognition unit can return the sensitivity of speech recognition to normal to enable natural conversation. For example, if the user is excited, the speech recognition unit can adjust the sensitivity of speech recognition to accommodate rapid speech. This allows the accuracy of speech recognition to be adjusted 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 speech recognition unit may be performed using a generative AI or not. For example, the speech recognition unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation and speech recognition accuracy adjustment.

[0076] The speech recognition unit can analyze the ambient noise level in real time during speech recognition and optimize noise cancellation. For example, if the motorcycle engine noise is loud, the speech recognition unit can enhance noise cancellation to improve the accuracy of speech recognition. For example, if the wind noise is strong, the speech recognition unit can cut out a specific frequency band to maintain the accuracy of speech recognition. For example, if the ambient traffic noise is loud, the speech recognition unit can adaptively adjust noise cancellation to maintain the accuracy of speech recognition. This allows for optimization of noise cancellation according to the ambient noise level. Some or all of the above processing in the speech recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the speech recognition unit can input ambient noise data into a generative AI and have the generative AI perform noise cancellation optimization.

[0077] The speech recognition unit can learn the user's speech patterns and generate an individually optimized speech recognition model during speech recognition. For example, the speech recognition unit can learn the user's speech speed and accent and generate an individually optimized speech recognition model. For example, the speech recognition unit can learn the user's specific phrases and verbal tics to improve the accuracy of speech recognition. For example, the speech recognition unit can continuously learn the characteristics of the user's speech and update the speech recognition model. This allows the speech recognition model to be optimized according to the user's speech patterns. Some or all of the above processes in the speech recognition unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the speech recognition unit can input the user's speech data into a generative AI and have the generative AI perform speech pattern learning and speech recognition model generation.

[0078] The speech recognition unit can estimate the user's emotions and determine the priority of voice commands based on the estimated emotions. For example, if the user is tense, the speech recognition unit will prioritize recognizing emergency response commands. For example, if the user is relaxed, the speech recognition unit will prioritize recognizing music playback and call commands. For example, if the user is excited, the speech recognition unit will prioritize recognizing navigation commands. This allows the priority of voice commands to be determined 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 speech recognition unit may be performed using a generative AI or not. For example, the speech recognition unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation and voice command priority determination.

[0079] The speech recognition unit can recognize regional languages ​​and dialects by taking into account the user's geographical location information during speech recognition. For example, if the user is in the Kansai region, the speech recognition unit will recognize the Kansai dialect and generate an appropriate response. For example, if the user is in the Tohoku region, the speech recognition unit will recognize the Tohoku dialect and generate an appropriate response. For example, if the user is overseas, the speech recognition unit will recognize the language and dialect of that region and generate an appropriate response. This allows the system to recognize regional languages ​​and dialects according to the user's geographical location information. Some or all of the above processing in the speech recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the speech recognition unit can input the user's geographical location information into a generative AI and have the generative AI perform the recognition of regional languages ​​and dialects.

[0080] The speech recognition unit can improve the recognition accuracy of specific commands by referring to the user's past speech history during speech recognition. For example, the speech recognition unit may prioritize recognizing commands that the user has frequently used in the past. For example, the speech recognition unit may learn specific phrases and verbal tics from the user's past speech history to improve recognition accuracy. For example, the speech recognition unit may analyze the user's past speech history to improve the recognition accuracy of specific commands. This improves the recognition accuracy of specific commands based on the user's past speech history. Some or all of the above processing in the speech recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the speech recognition unit may input the user's past speech history into a generative AI and have the generative AI perform the task of improving the recognition accuracy of specific commands.

[0081] The response generation unit can estimate the user's emotions and adjust the tone and expression of the response based on the estimated emotions. For example, if the user is tense, the response generation unit will generate a response in a calm tone. For example, if the user is relaxed, the response generation unit will generate a response in a bright tone. For example, if the user is excited, the response generation unit will generate a response in an energetic tone. This allows the tone and expression of the response to be adjusted 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-described processing in the response generation unit may be performed using the generative AI or not. For example, the response generation unit can input user emotion data into the generative AI and have the generative AI adjust the tone and expression of the response.

[0082] The response generation unit can generate the optimal response by considering the user's current situation and context when generating a response. For example, if the user is riding a motorcycle, the response generation unit will generate a concise response to allow the user to concentrate on driving. For example, if the user is stopped, the response generation unit will generate a response that includes detailed information. For example, if the user is in an emergency, the response generation unit will generate a quick and appropriate response. This allows the system to generate the optimal response according to the user's current situation and context. Some or all of the above-described processes in the response generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the response generation unit can input the user's current situation data into the generation AI and have the generation AI perform the generation of the optimal response.

[0083] The response generation unit can provide individually optimized responses by referring to the user's past response history when generating responses. For example, the response generation unit can generate an optimal response based on the response style the user has preferred in the past. For example, the response generation unit can generate a response suitable for a specific situation from the user's past response history. For example, the response generation unit can analyze the user's past response history and provide individually optimized responses. This allows the response generation unit to provide individually optimized responses based on the user's past response history. Some or all of the above processing in the response generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response generation unit can input the user's past response history into a generation AI and have the generation AI perform the generation of individually optimized responses.

[0084] The response generation unit can estimate the user's emotions and determine the priority of responses based on the estimated emotions. For example, if the user is tense, the response generation unit will prioritize generating emergency response responses. For example, if the user is relaxed, the response generation unit will prioritize generating responses for music playback or phone calls. For example, if the user is excited, the response generation unit will prioritize generating navigation responses. This allows the response priority to be determined 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the response generation unit may be performed using the generative AI or not. For example, the response generation unit can input user emotion data into the generative AI and have the generative AI perform the determination of response priorities.

[0085] The response generation unit can generate responses that include region-specific information, taking into account the user's geographical location information when generating responses. For example, if the user is in a specific region, the response generation unit can generate a response that includes traffic information for that region. For example, if the user is in a tourist destination, the response generation unit can generate a response that includes tourist information. For example, if the user is in a specific region, the response generation unit can generate a response that includes weather information for that region. This makes it possible to generate responses that include region-specific information according to the user's geographical location information. Some or all of the above processing in the response generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response generation unit can input the user's geographical location information into a generation AI and cause the generation AI to perform the generation of responses that include region-specific information.

[0086] The response generation unit can analyze the user's social media activity and generate a response that includes relevant information when generating a response. For example, the response generation unit can generate a relevant response based on information shared by the user on social media. For example, the response generation unit can analyze the content of the user's social media posts and generate an appropriate response. For example, the response generation unit can refer to the user's social media activity history and generate a response that includes relevant information. This makes it possible to generate a response that includes relevant information based on the user's social media activity. Some or all of the above processing in the response generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the response generation unit can input the user's social media data into a generation AI and have the generation AI perform the generation of a response that includes relevant information.

[0087] The emergency response unit can estimate the user's emotions and adjust the emergency response method based on the estimated emotions. For example, if the user is panicking, the emergency response unit generates a calming response. For example, if the user is calm, the emergency response unit provides quick and specific instructions. For example, if the user is confused, the emergency response unit provides concise and easy-to-understand instructions. This allows the emergency response method to be adjusted 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 emergency response unit may be performed using or without a generative AI. For example, the emergency response unit can input user emotion data into a generative AI and have the generative AI adjust the emergency response method.

[0088] The emergency response unit can monitor the user's current health status and vital signs in real time during an emergency and take the optimal response. For example, the emergency response unit can monitor the user's heart rate and blood pressure and take emergency action if there are abnormalities. For example, the emergency response unit can monitor the user's respiratory status and take appropriate action if there are abnormalities. For example, the emergency response unit can monitor the user's body temperature and take rapid action if there are abnormalities. This allows for the provision of the optimal emergency response based on the user's health status and vital signs. Some or all of the above processing in the emergency response unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the emergency response unit can input the user's vital sign data into a generation AI and have the generation AI perform the optimal emergency response.

[0089] The emergency response unit can provide individually optimized responses by referring to the user's past emergency history during an emergency. For example, the emergency response unit can provide the optimal response based on the user's past emergency experiences. For example, the emergency response unit can prioritize providing a specific response method based on the user's past emergency history. For example, the emergency response unit can analyze the user's past emergency history and provide individually optimized responses. This allows the unit to provide individually optimized responses based on the user's past emergency history. Some or all of the above processing in the emergency response unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the emergency response unit can input the user's past emergency history into a generation AI and have the generation AI perform the task of providing individually optimized responses.

[0090] The emergency response unit can estimate the user's emotions and determine the priority of emergency responses based on the estimated emotions. For example, if the user is in a state of panic, the emergency response unit will prioritize emergency response. If the user is calm, the emergency response unit will take appropriate action according to the situation. If the user is confused, the emergency response unit will take quick and concise action. This allows the emergency response unit to determine the priority of emergency responses 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 emergency response unit may be performed using generative AI or not. For example, the emergency response unit can input user emotion data into a generative AI and have the generative AI perform the determination of emergency response priorities.

[0091] The emergency response unit can automatically notify the user of the nearest emergency service, taking into account the user's geographical location during an emergency. For example, if the user is involved in an accident, the emergency response unit will automatically call the nearest ambulance. For example, if the user complains of feeling unwell, the emergency response unit will automatically guide the user to the nearest hospital. For example, if the user is lost, the emergency response unit will automatically notify the user of the nearest police station. This allows the system to automatically notify the user of the nearest emergency service based on their geographical location. Some or all of the above-described processes in the emergency response unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the emergency response unit can input the user's geographical location information into a generation AI and have the generation AI execute the notification of the nearest emergency service.

[0092] The emergency response unit can analyze a user's social media activity during an emergency and take appropriate action, including relevant information. For example, if a user requests help on social media, the emergency response unit can take action based on that information. For example, the emergency response unit can analyze the content of a user's social media posts and take appropriate action. For example, the emergency response unit can refer to a user's social media activity history and take action, including relevant information. This allows the emergency response unit to take action, including relevant information, based on the user's social media activity. Some or all of the above processing in the emergency response unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the emergency response unit can input the user's social media data into a generative AI and have the generative AI perform an emergency response, including relevant information.

[0093] The navigation unit can estimate the user's emotions and adjust the navigation instructions based on the estimated emotions. For example, if the user is tense, the navigation unit provides simple and clear instructions. If the user is relaxed, the navigation unit provides instructions with detailed information. If the user is in a hurry, the navigation unit provides quick and concise instructions. This allows the navigation instructions to be adjusted 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 navigation unit may be performed using or without a generative AI. For example, the navigation unit can input user emotion data into a generative AI and have the generative AI adjust the navigation instructions.

[0094] The navigation unit can analyze real-time traffic information during navigation and provide the optimal route. For example, the navigation unit can suggest the optimal route based on real-time traffic congestion information. For example, the navigation unit can suggest a detour route based on real-time road construction information. For example, the navigation unit can suggest a safe route based on real-time accident information. This allows the navigation unit to provide the optimal route based on real-time traffic information. Some or all of the above processing in the navigation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the navigation unit can input real-time traffic data into a generation AI and have the generation AI perform the task of suggesting the optimal route.

[0095] The navigation unit can provide individually optimized routes by referring to the user's past travel history during navigation. For example, the navigation unit may suggest the optimal route based on routes the user has used in the past. For example, the navigation unit may suggest a route that avoids congestion based on the user's past travel history. For example, the navigation unit may analyze the user's past travel history and suggest the most efficient route. This allows the navigation unit to provide individually optimized routes based on the user's past travel history. Some or all of the above processing in the navigation unit may be performed using a generative AI, or not. For example, the navigation unit may input the user's past travel history into a generative AI and have the generative AI perform the task of suggesting individually optimized routes.

[0096] The navigation unit can estimate the user's emotions and determine navigation priorities based on the estimated emotions. For example, if the user is stressed, the navigation unit may prioritize emergency navigation. If the user is relaxed, the navigation unit may provide navigation including tourist information. If the user is in a hurry, the navigation unit may prioritize providing the shortest route. This allows the navigation unit to determine navigation priorities 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 navigation unit may be performed using or without a generative AI. For example, the navigation unit can input user emotion data into a generative AI and have the generative AI perform navigation priority determination.

[0097] The navigation unit can provide navigation that includes region-specific information, taking into account the user's geographical location during navigation. For example, if the user is in a specific region, the navigation unit can provide navigation that includes traffic information for that region. For example, if the user is in a tourist destination, the navigation unit can provide navigation that includes tourist information. For example, if the user is in a specific region, the navigation unit can provide navigation that includes weather information for that region. This allows the navigation unit to provide navigation that includes region-specific information based on the user's geographical location. Some or all of the above processing in the navigation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the navigation unit can input the user's geographical location information into a generation AI and have the generation AI perform the task of providing navigation that includes region-specific information.

[0098] The navigation unit can analyze the user's social media activity during navigation and provide navigation that includes relevant information. For example, the navigation unit can provide relevant navigation based on information shared by the user on social media. For example, the navigation unit can analyze the content of the user's social media posts and provide appropriate navigation. For example, the navigation unit can refer to the user's social media activity history and provide navigation that includes relevant information. This allows the navigation unit to provide navigation that includes relevant information based on the user's social media activity. Some or all of the above processing in the navigation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the navigation unit can input the user's social media data into a generative AI and have the generative AI perform the task of providing navigation that includes relevant information.

[0099] The call unit can estimate the user's emotions and adjust the call quality and volume based on the estimated emotions. For example, if the user is nervous, the call unit can lower the call volume to encourage a calm conversation. For example, if the user is relaxed, the call unit can return the call quality to normal to enable a natural conversation. For example, if the user is excited, the call unit can raise the call volume to enable a clear conversation. This allows the call quality and volume to be adjusted 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 call unit may be performed using or without a generative AI. For example, the call unit can input user emotion data into a generative AI and have the generative AI adjust the call quality and volume.

[0100] The call unit can analyze the ambient noise level in real time during a call and optimize noise cancellation. For example, if the motorcycle engine noise is loud, the call unit will enhance noise cancellation to improve call quality. For example, if the wind noise is strong, the call unit will cut out a specific frequency band to maintain call quality. For example, if the ambient traffic noise is loud, the call unit will adaptively adjust noise cancellation to maintain call quality. This allows noise cancellation to be optimized according to the ambient noise level. Some or all of the above processing in the call unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the call unit can input ambient noise data into a generative AI and have the generative AI perform noise cancellation optimization.

[0101] The call unit can, during a call, refer to the user's past call history and provide individually optimized call settings. For example, the call unit can provide optimal call settings based on the user's past preferred call settings. For example, the call unit can prioritize providing specific call settings from the user's past call history. For example, the call unit can analyze the user's past call history and provide individually optimized call settings. This allows the call unit to provide individually optimized call settings based on the user's past call history. Some or all of the above processing in the call unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the call unit can input the user's past call history into a generative AI and have the generative AI perform the task of providing individually optimized call settings.

[0102] The call unit can estimate the user's emotions and determine call priorities based on those emotions. For example, if the user is nervous, the call unit will prioritize emergency calls. If the user is relaxed, the call unit will prioritize normal calls. If the user is excited, the call unit will prioritize important calls. This allows call priorities to be determined 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 call unit may be performed using or without a generative AI. For example, the call unit can input user emotion data into a generative AI and have the generative AI perform call prioritization.

[0103] The call unit can provide region-specific call settings during a call, taking into account the user's geographical location information. For example, if the user is in a specific region, the call unit can provide call settings that correspond to the communication conditions in that region. For example, if the user is overseas, the call unit can provide call settings suitable for international calls. For example, if the user is in a specific region, the call unit can provide call settings that correspond to the communication provider in that region. This allows for the provision of region-specific call settings based on the user's geographical location information. Some or all of the above processing in the call unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the call unit can input the user's geographical location information into a generation AI and have the generation AI perform the provision of region-specific call settings.

[0104] The call unit can analyze the user's social media activity during a call and provide call settings that include relevant information. For example, the call unit can provide relevant call settings based on information the user has shared on social media. For example, the call unit can analyze the content of the user's social media posts and provide appropriate call settings. For example, the call unit can refer to the user's social media activity history and provide call settings that include relevant information. This allows the call unit to provide call settings that include relevant information based on the user's social media activity. Some or all of the above processing in the call unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the call unit can input the user's social media data into a generative AI and have the generative AI perform the task of providing call settings that include relevant information.

[0105] The music playback unit can estimate the user's emotions and adjust the music selection and playback method based on the estimated emotions. For example, if the user is tense, the music playback unit will select relaxing music. For example, if the user is relaxed, the music playback unit will select cheerful music. For example, if the user is excited, the music playback unit will select energetic music. This allows the music selection and playback method to be adjusted 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 music playback unit may be performed using a generative AI or not. For example, the music playback unit can input user emotion data into a generative AI and have the generative AI perform the music selection and playback method adjustment.

[0106] The music playback unit can provide optimal music by considering the user's current situation and context during music playback. For example, if the user is riding a motorcycle, the music playback unit can provide music that allows the user to concentrate on driving. For example, if the user is stopped, the music playback unit can provide relaxing music. For example, if the user is in an emergency, the music playback unit can provide calming music. In this way, the music playback unit can provide optimal music according to the user's current situation and context. Some or all of the above processing in the music playback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the music playback unit can input data on the user's current situation into a generative AI and have the generative AI perform the task of providing optimal music.

[0107] The music playback unit can provide individually optimized music by referring to the user's past music history during music playback. For example, the music playback unit can provide optimal music based on music the user has liked in the past. For example, the music playback unit can provide music suitable for a specific situation from the user's past music history. For example, the music playback unit can analyze the user's past music history and provide individually optimized music. This makes it possible to provide individually optimized music based on the user's past music history. Some or all of the above processing in the music playback unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the music playback unit can input the user's past music history into a generation AI and have the generation AI perform the task of providing individually optimized music.

[0108] The music playback unit can estimate the user's emotions and determine the order in which music is played based on the estimated emotions. For example, if the user is tense, the music playback unit will prioritize playing relaxing music. For example, if the user is relaxed, the music playback unit will prioritize playing cheerful music. For example, if the user is excited, the music playback unit will prioritize playing energetic music. This allows the music playback order to be determined 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 music playback unit may be performed using a generative AI or not. For example, the music playback unit can input user emotion data into a generative AI and have the generative AI determine the order in which music is played.

[0109] The music playback unit can provide region-specific music by considering the user's geographical location information during music playback. For example, if the user is in a specific region, the music playback unit can provide traditional music of that region. For example, if the user is in a tourist destination, the music playback unit can provide tourist music of that region. For example, if the user is in a specific region, the music playback unit can provide popular music of that region. In this way, region-specific music can be provided based on the user's geographical location information. Some or all of the above processing in the music playback unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the music playback unit can input the user's geographical location information into a generation AI and have the generation AI perform the task of providing region-specific music.

[0110] The music playback unit can analyze the user's social media activity and provide relevant music during music playback. For example, the music playback unit can provide relevant music based on music the user has shared on social media. For example, the music playback unit can analyze the content of the user's social media posts and provide appropriate music. For example, the music playback unit can refer to the user's social media activity history and provide relevant music. This allows the music playback unit to provide relevant music based on the user's social media activity. Some or all of the above processing in the music playback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the music playback unit can input the user's social media data into a generative AI and have the generative AI perform the task of providing relevant music.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The speech recognition unit can analyze the user's speaking speed in real time and adjust the sensitivity of speech recognition according to the speaking speed. For example, if the user speaks quickly, the speech recognition unit increases its sensitivity to handle fast speech. Conversely, if the user speaks slowly, it returns the sensitivity to normal to enable natural conversation. Furthermore, if the user's speaking speed fluctuates, the speech recognition unit can adaptively adjust its sensitivity accordingly. This allows for optimization of speech recognition accuracy according to the user's speaking speed.

[0113] The navigation system can analyze a user's past travel history and predict and suggest routes and destinations that the user might prefer. For example, it can predict and guide the user to their next destination based on places the user has frequently visited in the past. It can also learn the characteristics of routes that the user prefers (e.g., scenic routes, routes with low traffic) and suggest routes based on those characteristics. Furthermore, it can analyze a user's past travel history and suggest the optimal route for a specific time of day. This allows for individually optimized navigation based on the user's past travel history.

[0114] The call function can analyze a user's call history and determine call priorities based on the frequency and content of calls with specific individuals. For example, it can prioritize calls with individuals the user frequently speaks with. It can also analyze the content of calls with specific individuals (e.g., work-related, family-related) and adjust call priorities accordingly. Furthermore, it can analyze a user's call history to determine call priorities for specific time periods. This allows for individually optimized call priorities based on the user's call history.

[0115] The music playback unit can analyze the user's music playback history and provide optimal music according to specific times of day and situations. For example, it can provide music suitable for the morning based on the music the user prefers during their morning commute. It can also provide music suitable for exercise based on the music the user prefers while exercising. Furthermore, it can analyze the user's music playback history and provide music tailored to specific seasons or events. This allows for the provision of individually optimized music based on the user's music playback history.

[0116] The speech recognition unit can estimate the user's emotions and adjust the sensitivity of speech recognition based on the estimated emotions. For example, if the user is nervous, the sensitivity of speech recognition is increased to reduce misrecognition. If the user is relaxed, the sensitivity is returned to normal to enable natural conversation. If the user is excited, the sensitivity is adjusted to accommodate rapid speech. In this way, the accuracy of speech recognition can be optimized according to the user's emotions.

[0117] The navigation unit can estimate the user's emotions and adjust the navigation instructions based on those emotions. For example, if the user is tense, it provides simple and clear instructions. If the user is relaxed, it provides instructions with detailed information. If the user is in a hurry, it provides quick and concise instructions. This allows the navigation instructions to be optimized according to the user's emotions.

[0118] The call unit can estimate the user's emotions and adjust the call quality and volume based on those estimates. For example, if the user is nervous, the volume can be lowered to encourage a calm conversation. If the user is relaxed, the call quality can be returned to normal to enable a natural conversation. If the user is excited, the volume can be increased to enable a clear conversation. This allows for the optimization of call quality and volume according to the user's emotions.

[0119] The music playback unit can estimate the user's emotions and adjust music selection and playback methods based on those estimates. For example, if the user is tense, it will select relaxing music. If the user is relaxed, it will select upbeat music. If the user is excited, it will select energetic music. This allows for the optimization of music selection and playback methods according to the user's emotions.

[0120] The emergency response unit can estimate the user's emotions and adjust the emergency response method based on the estimated emotions. For example, if the user is panicking, it generates a calming response. If the user is calm, it provides quick and specific instructions. If the user is confused, it provides concise and easy-to-understand instructions. This allows the emergency response method to be optimized according to the user's emotions.

[0121] The response generation unit can estimate the user's emotions and adjust the tone and expression of the response based on the estimated emotions. For example, if the user is nervous, it will generate a response in a calm tone. If the user is relaxed, it will generate a response in a bright tone. If the user is excited, it will generate a response in an energetic tone. This allows the tone and expression of the response to be optimized according to the user's emotions.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The speech recognition unit recognizes speech. For example, it analyzes speech using a speech recognition algorithm and recognizes voice commands. The speech recognition unit uses a microphone to input speech and uses noise cancellation technology to remove ambient noise and improve the accuracy of speech recognition. Step 2: The response generation unit generates a response based on the speech recognized by the speech recognition unit. For example, it uses natural language processing technology or generative AI to generate an appropriate response, understanding the user's intent and generating a response accordingly. Step 3: The emergency response unit responds to the emergency based on the response generated by the response generation unit. For example, it automatically contacts emergency contacts, instructs the user on appropriate actions to take in the emergency, and automatically notifies emergency services.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the voice recognition unit, response generation unit, emergency response unit, navigation unit, call unit, and music playback unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the voice recognition unit inputs voice using the microphone of the smart device 14 and applies a voice recognition algorithm using the specific processing unit 290 of the data processing unit 12. The response generation unit generates an appropriate response using natural language processing technology using the specific processing unit 290 of the data processing unit 12. The emergency response unit automatically contacts emergency contacts using the specific processing unit 290 of the data processing unit 12. The navigation unit determines the current location using the GPS function of the smart device 14 and calculates the optimal route using the specific processing unit 290 of the data processing unit 12. The call unit establishes a call using the communication protocol of the smart device 14 and compresses the voice data using the specific processing unit 290 of the data processing unit 12. The music playback unit, for example, selects songs that match the user's preferences from the music library of the smart device 14, and the specific processing unit 290 of the data processing unit 12 optimizes the sound quality. The correspondence between each unit and the device and control unit is not limited to the example described above, and various changes are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the voice recognition unit, response generation unit, emergency response unit, navigation unit, call unit, and music playback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the voice recognition unit inputs voice using the microphone of the smart glasses 214 and applies a voice recognition algorithm using the specific processing unit 290 of the data processing unit 12. The response generation unit generates an appropriate response using natural language processing technology using the specific processing unit 290 of the data processing unit 12. The emergency response unit automatically contacts emergency contacts using the specific processing unit 290 of the data processing unit 12. The navigation unit determines the current location using the GPS function of the smart glasses 214 and calculates the optimal route using the specific processing unit 290 of the data processing unit 12. The call unit establishes a call using the communication protocol of the smart glasses 214 and compresses the voice data using the specific processing unit 290 of the data processing unit 12. The music playback unit, for example, selects songs that match the user's preferences from the music library of the smart glasses 214, and the specific processing unit 290 of the data processing unit 12 optimizes the sound quality. The correspondence between each unit and the device and control unit is not limited to the example described above, and various changes are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the voice recognition unit, response generation unit, emergency response unit, navigation unit, call unit, and music playback unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the voice recognition unit inputs voice using the microphone of the headset terminal 314 and applies a voice recognition algorithm by the specific processing unit 290 of the data processing unit 12. The response generation unit generates an appropriate response using natural language processing technology by the specific processing unit 290 of the data processing unit 12. The emergency response unit automatically contacts emergency contacts by the specific processing unit 290 of the data processing unit 12. The navigation unit determines the current location using the GPS function of the headset terminal 314 and calculates the optimal route by the specific processing unit 290 of the data processing unit 12. The call unit establishes a call using the communication protocol of the headset terminal 314 and compresses the voice data by the specific processing unit 290 of the data processing unit 12. The music playback unit, for example, selects songs that match the user's preferences from the music library of the headset terminal 314, and the specific processing unit 290 of the data processing unit 12 optimizes the sound quality. The correspondence between each unit and the device and control unit is not limited to the example described above, and various changes are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the voice recognition unit, response generation unit, emergency response unit, navigation unit, call unit, and music playback unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the voice recognition unit inputs voice using the microphone of the robot 414 and applies a voice recognition algorithm using the specific processing unit 290 of the data processing unit 12. The response generation unit generates an appropriate response using natural language processing techniques, for example, using the specific processing unit 290 of the data processing unit 12. The emergency response unit automatically contacts emergency contacts, for example, using the specific processing unit 290 of the data processing unit 12. The navigation unit determines the current location using the GPS function of the robot 414 and calculates the optimal route using the specific processing unit 290 of the data processing unit 12. The call unit establishes a call using the communication protocol of the robot 414 and compresses the voice data using the specific processing unit 290 of the data processing unit 12. The music playback unit, for example, selects songs that match the user's preferences from the robot 414's music library, and the specific processing unit 290 of the data processing unit 12 optimizes the sound quality. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) Voice recognition unit, A response generation unit that generates a response based on the speech recognized by the speech recognition unit, An emergency response unit that responds to an emergency based on the response generated by the response generation unit, Equipped with A system characterized by the following features. (Note 2) Equipped with a navigation unit The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with a telephone unit The system described in Appendix 1, characterized by the features described herein. (Note 4) Equipped with a music playback unit The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned speech recognition unit, It estimates the user's emotions and adjusts the accuracy of speech recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned speech recognition unit, During voice recognition, the system analyzes ambient noise levels in real time and optimizes noise cancellation. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned speech recognition unit, During speech recognition, the system learns the user's speech patterns and generates a speech recognition model that is individually optimized for that user. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned speech recognition unit, It estimates the user's emotions and prioritizes voice commands based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned speech recognition unit, During speech recognition, the system takes the user's geographical location into consideration to recognize regional languages ​​and dialects. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned speech recognition unit, During speech recognition, the system references the user's past speech history to improve the accuracy of recognizing specific commands. The system described in Appendix 1, characterized by the features described herein. (Note 11) The response generation unit, It estimates the user's emotions and adjusts the tone and expression of responses based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The response generation unit, When generating a response, the system considers the user's current situation and context to generate the most appropriate response. The system described in Appendix 1, characterized by the features described herein. (Note 13) The response generation unit, When generating a response, the system refers to the user's past response history to provide an individually optimized response. The system described in Appendix 1, characterized by the features described herein. (Note 14) The response generation unit, It estimates the user's emotions and determines the priority of responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The response generation unit, When generating a response, the system takes the user's geographical location into consideration and generates a response that includes region-specific information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The response generation unit, When generating a response, the system analyzes the user's social media activity and generates a response that includes relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned emergency response unit, It estimates the user's emotions and adjusts emergency response methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned emergency response unit, During emergency situations, the system monitors the user's current health status and vital signs in real time to provide the most appropriate response. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned emergency response unit, During emergency response, the system references the user's past emergency history to provide individually optimized responses. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned emergency response unit, It estimates the user's emotions and determines the priority of emergency responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned emergency response unit, In emergency situations, the system automatically notifies the user of the nearest emergency service, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned emergency response unit, During emergency response, we analyze users' social media activity and take appropriate action based on relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned navigation unit is It estimates the user's emotions and adjusts the navigation instructions based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned navigation unit is During navigation, it analyzes real-time traffic information to provide the optimal route. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned navigation unit is During navigation, the system references the user's past travel history to provide individually optimized routes. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned navigation unit is It estimates the user's emotions and determines navigation priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned navigation unit is During navigation, the system takes the user's geographical location into consideration and provides navigation that includes region-specific information. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned navigation unit is During navigation, the system analyzes the user's social media activity and provides navigation that includes relevant information. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned communication unit is, It estimates the user's emotions and adjusts the call quality and volume based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned communication unit is, During calls, the system analyzes ambient noise levels in real time and optimizes noise cancellation. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned communication unit is, During a call, the system references the user's past call history to provide individually optimized call settings. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned communication unit is, It estimates the user's emotions and determines call priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned communication unit is, During a call, the system takes the user's geographical location into account and provides region-specific call settings. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned communication unit is, During a call, the system analyzes the user's social media activity and provides call settings that include relevant information. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned music playback unit is It estimates the user's emotions and adjusts music selection and playback methods based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned music playback unit is When playing music, the system considers the user's current situation and context to provide the most suitable music. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned music playback unit is When playing music, the system references the user's past music history to provide individually optimized music. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned music playback unit is It estimates the user's emotions and determines the music playback order based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned music playback unit is When playing music, the system takes the user's geographical location into consideration to provide region-specific music. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned music playback unit is When playing music, the system analyzes the user's social media activity and provides relevant music. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0196] 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 voice recognition unit that recognizes speech, A response generation unit that generates a response based on the speech recognized by the speech recognition unit, An emergency response unit that responds to an emergency based on the response generated by the response generation unit, Equipped with A system characterized by the following features.

2. Equipped with a navigation unit The system according to feature 1.

3. The aforementioned speech recognition unit, It estimates the user's emotions and adjusts the accuracy of speech recognition based on the estimated emotions. The system according to feature 1.

4. The aforementioned speech recognition unit, During voice recognition, the system analyzes ambient noise levels in real time and optimizes noise cancellation. The system according to feature 1.

5. The aforementioned speech recognition unit, During speech recognition, the system learns the user's speech patterns and generates a speech recognition model that is individually optimized for that user. The system according to feature 1.

6. The aforementioned speech recognition unit, It estimates the user's emotions and prioritizes voice commands based on those emotions. The system according to feature 1.

7. The aforementioned speech recognition unit, During speech recognition, the system takes the user's geographical location into consideration to recognize regional languages ​​and dialects. The system according to feature 1.

8. The aforementioned speech recognition unit, During speech recognition, the system references the user's past speech history to improve the accuracy of recognizing specific commands. The system according to feature 1.

9. The response generation unit, It estimates the user's emotions and adjusts the tone and expression of responses based on the estimated emotions. The system according to feature 1.

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