system

Abstract

The system according to this embodiment aims to enable elderly people to easily use smartphones through voice control. [Solution] The system according to this embodiment comprises a reception unit, a recognition unit, an operation unit, and a provision unit. The reception unit receives voice input. The recognition unit recognizes the voice received by the reception unit and performs natural language processing. The operation unit sends and receives messages and acquires information based on the content recognized by the recognition unit. The provision unit provides the information acquired by the operation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, when elderly people use a smartphone, there are problems such as difficulty in touch operations and visibility problems, and there is a problem of lack of convenience.

[0005] The system according to the embodiment aims to enable elderly people to easily use a smartphone through voice operations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a recognition unit, an operation unit, and a provision unit. The reception unit receives voice input. The recognition unit recognizes the voice received by the reception unit and performs natural language processing. The operation unit sends and receives messages and acquires information based on the content recognized by the recognition unit. The provision unit provides the information acquired by the operation unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable elderly people to easily use smartphones through voice control. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The voice-operated support system according to an embodiment of the present invention is a system that provides voice-operated support utilizing an AI agent to alleviate the inconveniences elderly people face when using smartphones. The voice-operated support system allows users to initiate operation by voice. For example, if a user says, "I want to send a message," the AI ​​agent recognizes the voice and launches a messaging app. Next, the user inputs the message they want to send by voice, the AI ​​agent converts the content into text, and sends the message to the designated recipient. Furthermore, information such as news, weather forecasts, and health information can also be obtained by voice. For example, if a user says, "Tell me today's weather," the AI ​​agent obtains the weather forecast and informs the user by voice. This eliminates visibility issues and allows for easy information acquisition. In addition, emergency contact can be made by voice, and the GPS function can be used to check the location of family members. For example, if a user says, "I want to make an emergency contact," the AI ​​agent calls the emergency contact and shares GPS information. This system addresses scenarios where elderly people want to use smartphones (communication with family and friends, obtaining news and weather forecasts, emergency contact, etc.) and provides a convenient and secure digital experience through voice operation. Furthermore, the voice AI agent recognizes the voices of elderly people and uses natural language processing to engage in conversation, improving ease of use. The voice AI agent can also support multiple languages, making it usable in care facilities in multilingual regions. In addition, customizable responses and voice commands allow for operation tailored to the preferences of elderly people. For example, it can be set to speak in specific words or styles. This system can also replace the roles of customer support, care workers, and system engineers. For example, customer support could provide user-friendly smartphone support services for the elderly and provide support and assistance during use. Care workers could design and support solutions to alleviate the daily inconveniences of the elderly, and system engineers could design and develop voice AI agents for the elderly.This system makes it easier for seniors to use smartphones and increases their opportunities to actively utilize digital devices in their daily lives. This will bridge the information gap and allow seniors to participate in the digital society with confidence. As a result, the voice-controlled support system can eliminate the inconveniences seniors face when using smartphones, providing a convenient and safe digital experience.

[0029] The voice-operated support system according to this embodiment comprises a reception unit, a recognition unit, an operation unit, and a provision unit. The reception unit receives voice input. The reception unit can receive voice input using, for example, a microphone. The reception unit can also receive voice input using specific voice commands. The recognition unit recognizes the voice received by the reception unit and performs natural language processing. The recognition unit can analyze the voice using, for example, morphological analysis. The recognition unit can also analyze the voice using grammatical analysis. Furthermore, the recognition unit can also analyze the voice using semantic analysis. The operation unit sends and receives messages and retrieves information based on the content recognized by the recognition unit. The operation unit can send and receive messages using, for example, SMS. The operation unit can also send and receive messages using email. Furthermore, the operation unit can send and receive messages using a chat application. The operation unit can retrieve information using, for example, internet search. The operation unit can also retrieve information using database access. The provision unit provides the information retrieved by the operation unit. The provision unit can provide information using, for example, voice output. Furthermore, the information provision unit can also provide information using a screen display. Thus, the voice-operated support system according to this embodiment can eliminate the inconveniences elderly people face when using smartphones by receiving, recognizing, operating, and providing voice input. Some or all of the above-described processes in the reception unit, recognition unit, operation unit, and information provision unit may be performed using, for example, AI, or without AI. For example, the reception unit can receive voice input using an AI model that accepts voice input. The recognition unit can recognize voice and perform natural language processing using an AI model that recognizes voice and performs natural language processing. The operation unit can send and receive messages and acquire information using an AI model that performs message sending and receiving and information acquisition. The information provision unit can provide information using an AI model that provides the acquired information.

[0030] The reception unit accepts voice input. For example, the reception unit can accept voice input using a microphone. Specifically, the microphone is highly sensitive and equipped with noise-canceling capabilities to eliminate ambient noise. This allows for clear capture of the user's voice. The reception unit can also accept voice input using specific voice commands. For example, a wake word such as "Hey, support system" can be set, and voice input can only begin when this wake word is detected. This prevents malfunctions and ensures that the user's intended actions are reliably received. Furthermore, the reception unit can support multiple languages ​​and accept voice input in the user's selected language. This allows for flexible operation in environments requiring multilingual support. To improve the quality of voice input, the reception unit can utilize voice filtering and echo cancellation technologies. This improves the accuracy of voice input, leading to more precise analysis by the recognition unit.

[0031] The recognition unit recognizes the speech received by the reception unit and performs natural language processing. For example, the recognition unit can analyze speech using morphological analysis. Morphological analysis is a technique that divides speech data into word units and analyzes the meaning and grammatical role of each word. The recognition unit can also analyze speech using grammatical analysis. Grammar analysis is a technique that analyzes the grammatical structure of speech data and clarifies the relationships between subjects, predicates, objects, etc., in sentences. Furthermore, the recognition unit can also analyze speech using semantic analysis. Semantic analysis is a technique that understands the meaning of speech data and accurately grasps the user's intent. By combining these analysis techniques, the recognition unit can analyze the user's voice input with high accuracy and generate appropriate operation instructions. The recognition unit can perform speech recognition and natural language processing using AI. Specifically, it uses a deep learning-based speech recognition model to convert speech data into text data. Next, it uses a natural language processing model to analyze the meaning of the text data and understand the user's intent. As a result, the recognition unit can handle complex speech input and perform appropriate operations according to the user's requests.

[0032] The control unit performs message sending and receiving and information retrieval based on the content recognized by the recognition unit. For example, the control unit can send and receive messages using SMS. Specifically, if a user instructs "Send a message" by voice, the control unit calls an API for sending SMS and sends a message to the specified recipient. The control unit can also send and receive messages using email. If a user instructs "Send an email," the control unit connects to an email server and sends an email to the specified recipient. Furthermore, the control unit can send and receive messages using a chat application. For example, if a user instructs "Send a message via chat," the control unit calls the chat application's API and sends a message to the specified recipient. The control unit can also retrieve information using internet search. If a user instructs "Tell me the weather," the control unit uses an internet search engine to retrieve the latest weather information and passes it to the information provider. The control unit can also retrieve information using database access. For example, if a user instructs "Tell me my schedule," the control unit accesses a database and retrieves the user's schedule information. This allows the control unit to retrieve information and send and receive messages in response to a variety of user requests.

[0033] The information provider provides information acquired by the operation unit. The information provider can provide information using, for example, voice output. Specifically, it uses speech synthesis technology to convey acquired information to the user in a natural voice. This allows the user to obtain information without relying on sight. The information provider can also provide information using screen display. For example, it can display acquired information on the screen of a smartphone or tablet so that the user can visually confirm it. Furthermore, the information provider can provide information using a notification function. For example, if important information is acquired, it can notify the user using the smartphone's notification function. The information provider can flexibly select the method of information provision to improve user convenience. For example, if the user prefers voice output, it will provide information by voice; if the user prefers screen display, it will display the information on the screen. The information provider can also receive user feedback and improve the method of information provision. For example, if the user wants to adjust the speed of voice output, the information provider will adjust the speed of speech synthesis to provide information according to the user's preference. In this way, the information provider can provide information to the user in the most optimal way and improve the overall usability of the system.

[0034] The customization section allows for the customization of voice commands. For example, it can customize specific phrases. The customization section can also configure the customization procedure. For example, the customization section can be configured to respond with "Do you want to send a message?" when the user says "I want to send a message." This allows for operation tailored to the user's preferences by customizing voice commands. Some or all of the above processing in the customization section may be performed using AI, for example, or without AI. For example, the customization section can customize voice commands using an AI model that customizes voice commands.

[0035] The multilingual unit can perform multilingual support. For example, the multilingual unit can perform multilingual support using a translation engine. The multilingual unit can also set a list of supported languages. For example, the multilingual unit can support languages ​​such as English, Japanese, and French. This allows the system to be used in care facilities in multilingual regions. Some or all of the above-described processes in the multilingual unit may be performed using AI, for example, or without AI. For example, the multilingual unit can perform multilingual support using an AI model that performs multilingual support using a translation engine.

[0036] The Emergency Contact Department can make emergency contacts. For example, the Emergency Contact Department can make emergency contacts using telephones. It can also make emergency contacts using emails. Furthermore, the Emergency Contact Department can make emergency contacts using emergency notification apps. This allows for a quick response in emergencies by enabling emergency contacts. Some or all of the above processes in the Emergency Contact Department may be performed using AI, for example, or not. For example, the Emergency Contact Department can make emergency contacts using an AI model.

[0037] The GPS unit can utilize GPS functionality. The GPS unit can, for example, configure how location information is acquired. It can also configure which applications to use. For example, the GPS unit can acquire location information using a specific application. This allows users to check the location of family members by utilizing GPS functionality. Some or all of the above-described processes in the GPS unit may be performed using AI, or not. For example, the GPS unit can acquire location information using an AI model for acquiring location information.

[0038] The recognition unit can recognize the voices of elderly people and perform natural language processing. For example, the recognition unit can analyze the frequency band of the elderly person's voice. The recognition unit can also analyze specific pronunciation patterns. This improves usability by recognizing the voices of elderly people and performing natural language processing. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can recognize the voices of elderly people and perform natural language processing using an AI model that recognizes the voices of elderly people and performs natural language processing.

[0039] The control unit can send and receive messages and retrieve information. For example, the control unit can send and receive messages using SMS. It can also send and receive messages using email. Furthermore, the control unit can send and receive messages using a chat application. For example, the control unit can retrieve information using internet search. It can also retrieve information using database access. This allows for a convenient digital experience even for elderly people who are not comfortable using smartphones, by enabling message sending and receiving and information retrieval. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can perform message sending and receiving and information retrieval using an AI model.

[0040] The information provider can provide the acquired information in voice. The information provider can provide the information in voice, for example, using speech synthesis technology. The information provider can also provide the information in voice using a voice output device. By providing the acquired information in voice, the problem of visibility can be resolved. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide the information in voice using an AI model that provides information in voice using speech synthesis technology.

[0041] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the reception unit prioritizes receiving voice commands that the user has frequently used in the past. The reception unit can also predict commands to be used during specific time periods based on the user's past voice input history and receive them. Furthermore, the reception unit can analyze the user's voice input history and suggest the optimal voice input method. This allows the reception unit to select the optimal reception method by analyzing the user's past voice input history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can select the optimal reception method using an AI model that analyzes the user's past voice input history and selects the optimal reception method.

[0042] The reception unit can filter voice input based on the user's current situation and environment. For example, if the user is in a noisy environment, the reception unit can apply noise cancellation when receiving voice input. Alternatively, if the user is in a quiet environment, the reception unit can accept normal voice input. Furthermore, if the user is moving, the reception unit can adjust the sensitivity of the voice input. This allows for the reception of appropriate voice input by filtering based on the user's current situation and environment. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can use an AI model that filters voice input based on the user's current situation and environment when receiving it.

[0043] The reception unit can prioritize receiving voice input by considering the user's geographical location when receiving voice input. For example, if the user is in a specific location, the reception unit will prioritize receiving voice input related to that location. Furthermore, if the user is on the move, the reception unit can prioritize receiving voice input related to their destination. Additionally, if the user is at home, the reception unit can prioritize receiving voice input related to their home. This allows for the prioritization of highly relevant voice input by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can receive voice input using an AI model that prioritizes receiving highly relevant voice input by considering the user's geographical location when receiving voice input.

[0044] The reception unit can analyze the user's social media activity when receiving voice input and accept relevant voice input. For example, the reception unit can prioritize receiving keywords that the user frequently uses on social media. The reception unit can also prioritize receiving voice input related to specific topics from the user's social media activity. Furthermore, the reception unit can analyze the user's social media activity and suggest the optimal voice input method. In this way, relevant voice input can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can receive voice input using an AI model that analyzes the user's social media activity and accepts relevant voice input when receiving voice input.

[0045] The recognition unit can improve recognition accuracy by referring to the user's past speech patterns during speech recognition. For example, the recognition unit can improve speech recognition accuracy based on speech patterns previously used by the user. The recognition unit can also prioritize the recognition of specific phrases from the user's past speech history. Furthermore, the recognition unit can analyze the user's speech patterns and propose the optimal speech recognition method. This allows for improved speech recognition accuracy by referring to the user's past speech patterns. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can improve speech recognition accuracy by using an AI model that improves recognition accuracy by referring to the user's past speech patterns during speech recognition.

[0046] The recognition unit can apply a recognition algorithm according to the user's speaking speed and accent during speech recognition. For example, if the user speaks slowly, the recognition unit applies a recognition algorithm that corresponds to the speaking speed. The recognition unit can also apply a recognition algorithm that corresponds to the speaking speed if the user speaks quickly. Furthermore, the recognition unit can apply an optimal recognition algorithm according to the user's accent. This improves the accuracy of speech recognition by applying a recognition algorithm according to the user's speaking speed and accent. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can improve the accuracy of speech recognition by using an AI model that applies a recognition algorithm according to the user's speaking speed and accent during speech recognition.

[0047] The recognition unit can improve recognition accuracy by considering the user's geographical location information during speech recognition. For example, if the user is in a specific location, the recognition unit can prioritize speech recognition related to that location. Furthermore, if the user is on the move, the recognition unit can prioritize speech recognition related to the user's destination. Additionally, if the user is at home, the recognition unit can prioritize speech recognition related to home. This improves the accuracy of speech recognition by considering the user's geographical location information. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can improve the accuracy of speech recognition by using an AI model that improves recognition accuracy by considering the user's geographical location information during speech recognition.

[0048] The recognition unit can improve recognition accuracy by referring to relevant literature used by the user during speech recognition. For example, if the user is talking about a specific topic, the recognition unit can improve recognition accuracy by referring to literature related to that topic. Furthermore, if the user uses technical terms, the recognition unit can also improve recognition accuracy by referring to relevant literature. In addition, the recognition unit can suggest the optimal speech recognition method by referring to literature related to the user's utterances. This allows for improved speech recognition accuracy by referring to relevant literature used by the user. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can improve speech recognition accuracy by using an AI model that improves recognition accuracy by referring to relevant literature used by the user during speech recognition.

[0049] The control unit can select the optimal operation method by referring to the user's past operation history during operation. For example, the control unit can prioritize providing operation methods that the user has frequently used in the past. The control unit can also predict and provide operation methods to be used during a specific time period based on the user's past operation history. Furthermore, the control unit can analyze the user's operation history and propose the optimal operation method. This allows the optimal operation method to be selected by referring to the user's past operation history. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can select the optimal operation method by using an AI model that selects the optimal operation method by referring to the user's past operation history during operation.

[0050] The control unit can customize the means of operation based on the user's current situation during operation. For example, if the user is on the move, the control unit can provide simplified means of operation. Alternatively, if the user is at home, the control unit can provide detailed means of operation. Furthermore, the control unit can suggest the optimal means of operation depending on the user's situation. This allows for the provision of appropriate means of operation by customizing them based on the user's current situation. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can customize the means of operation using an AI model that customizes the means of operation based on the user's current situation during operation.

[0051] The control unit can select the optimal operation method by considering the user's geographical location information during operation. For example, if the user is in a specific location, the control unit can provide an operation method related to that location. Furthermore, if the user is on the move, the control unit can also provide an operation method related to the destination. In addition, if the user is at home, the control unit can provide an operation method related to home. This allows for the selection of the optimal operation method by considering the user's geographical location information. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can select the optimal operation method by using an AI model that selects the optimal operation method by considering the user's geographical location information during operation.

[0052] The control unit can analyze the user's social media activity during operation and suggest methods of operation. For example, the control unit can prioritize providing methods of operation that the user frequently uses on social media. The control unit can also provide methods of operation related to specific topics based on the user's social media activity. Furthermore, the control unit can analyze the user's social media activity and suggest the optimal method of operation. This allows the control unit to suggest the optimal method of operation by analyzing the user's social media activity. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can suggest methods of operation using an AI model that analyzes the user's social media activity and suggests methods of operation during operation.

[0053] The information delivery unit can select the optimal delivery method by referring to the user's past information acquisition history when providing information. For example, the delivery unit can prioritize providing information that the user has frequently acquired in the past. The delivery unit can also predict and provide information that the user will acquire at a specific time period based on the user's past information acquisition history. Furthermore, the delivery unit can analyze the user's information acquisition history and propose the optimal information delivery method. This allows the optimal information delivery method to be selected by referring to the user's past information acquisition history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can select the optimal delivery method by using an AI model that selects the optimal delivery method by referring to the user's past information acquisition history when providing information.

[0054] The information provider can customize the means of information provision based on the user's current situation when providing information. For example, if the user is on the move, the information provider can provide a simplified means of information provision. If the user is at home, the information provider can also provide a more detailed means of information provision. Furthermore, the information provider can suggest the most suitable means of information provision depending on the user's situation. In this way, by customizing the means of information provision based on the user's current situation, the appropriate means of information provision can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can customize the means of information provision using an AI model that customizes the means of information provision based on the user's current situation when providing information.

[0055] The information provider can select the optimal information delivery method by considering the user's geographical location when providing information. For example, if the user is in a specific location, the provider can prioritize providing information related to that location. Furthermore, if the user is on the move, the provider can prioritize providing information related to their destination. Additionally, if the user is at home, the provider can prioritize providing information related to their home. This allows the provider to select the optimal information delivery method by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can select the optimal information delivery method by using an AI model that considers the user's geographical location when providing information.

[0056] The information provider can analyze the user's social media activity and provide relevant information when providing information. For example, the provider can prioritize providing information related to topics that the user frequently shows interest in on social media. The provider can also provide information related to specific topics based on the user's social media activity. Furthermore, the provider can analyze the user's social media activity and propose the optimal method of providing information. This allows the provider to provide relevant information by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can provide information using an AI model that analyzes the user's social media activity and provides relevant information when providing information.

[0057] The customization unit can select the optimal customization method when customizing voice commands by referring to the user's past usage history. For example, the customization unit can prioritize providing voice commands that the user has frequently used in the past. The customization unit can also predict and provide voice commands to be used during specific time periods based on the user's past usage history. Furthermore, the customization unit can analyze the user's usage history and suggest the optimal voice commands. This allows the optimal voice command customization method to be selected by referring to the user's past usage history. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can select the optimal customization method when customizing voice commands by using an AI model that selects the optimal customization method by referring to the user's past usage history.

[0058] The customization unit can select the optimal customization method for voice commands by considering the user's geographical location information. For example, if the user is in a specific location, the customization unit can prioritize providing voice commands related to that location. Furthermore, if the user is on the move, the customization unit can prioritize providing voice commands related to their destination. Additionally, if the user is at home, the customization unit can prioritize providing voice commands related to their home. This allows the system to select the optimal voice command customization method by considering the user's geographical location information. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can select the voice command customization method using an AI model that selects the optimal customization method by considering the user's geographical location information when customizing voice commands.

[0059] The multilingual unit can select the optimal support method by referring to the user's past language usage history when providing multilingual support. For example, the multilingual unit can prioritize providing languages ​​that the user has frequently used in the past. Furthermore, the multilingual unit can predict and provide languages ​​that the user will use at specific times based on their past language usage history. In addition, the multilingual unit can analyze the user's language usage history and propose the optimal multilingual support method. This allows for the selection of the optimal multilingual support method by referring to the user's past language usage history. Some or all of the above processing in the multilingual unit may be performed using AI, for example, or without AI. For example, the multilingual unit can select a support method using an AI model that selects the optimal support method by referring to the user's past language usage history when providing multilingual support.

[0060] The multilingual unit can select the optimal support method when providing multilingual support, taking into account the user's geographical location. For example, if the user is in a specific location, the multilingual unit can prioritize providing the language relevant to that location. Furthermore, if the user is on the move, the multilingual unit can prioritize providing the language relevant to their destination. Additionally, if the user is at home, the multilingual unit can prioritize providing the language relevant to their home. This allows the system to select the optimal multilingual support method by considering the user's geographical location. Some or all of the above processing in the multilingual unit may be performed using AI, for example, or without AI. For instance, the multilingual unit can select the optimal support method when providing multilingual support using an AI model that considers the user's geographical location.

[0061] The emergency contact unit can select the most suitable contact method by referring to the user's past emergency contact history during an emergency. For example, the emergency contact unit may prioritize providing emergency contact methods that the user has frequently used in the past. The emergency contact unit can also predict and provide contact methods to be used during specific time periods based on the user's past emergency contact history. Furthermore, the emergency contact unit can analyze the user's emergency contact history and suggest the most suitable contact method. This allows the unit to select the most suitable contact method by referring to the user's past emergency contact history. Some or all of the above processes in the emergency contact unit may be performed using AI, for example, or without AI. For example, the emergency contact unit may select a contact method by using an AI model that selects the most suitable contact method by referring to the user's past emergency contact history during an emergency.

[0062] The emergency contact unit can select the most appropriate contact method when an emergency occurs, taking into account the user's geographical location. For example, if the user is in a specific location, the emergency contact unit can prioritize providing an emergency contact method relevant to that location. Furthermore, if the user is on the move, the emergency contact unit can prioritize providing an emergency contact method relevant to their destination. Additionally, if the user is at home, the emergency contact unit can prioritize providing an emergency contact method relevant to their home. This allows the system to select the most appropriate contact method by considering the user's geographical location. Some or all of the above processing in the emergency contact unit may be performed using AI, for example, or without AI. For instance, the emergency contact unit can select a contact method using an AI model that selects the most appropriate contact method when an emergency occurs, taking into account the user's geographical location.

[0063] The GPS unit can select the optimal usage method by referring to the user's past location history when using the GPS function. For example, the GPS unit may prioritize providing locations that the user has frequently visited in the past. The GPS unit can also predict and provide locations to be used during specific time periods based on the user's past location history. Furthermore, the GPS unit can analyze the user's location history and propose the optimal GPS usage method. This allows the optimal usage method of the GPS function to be selected by referring to the user's past location history. Some or all of the above processing in the GPS unit may be performed using AI, for example, or without AI. For example, the GPS unit may select the usage method by using an AI model that selects the optimal usage method by referring to the user's past location history when using the GPS function.

[0064] The GPS unit can select the optimal usage method when using GPS functions, taking into account the user's geographical location information. For example, if the user is in a specific location, the GPS unit can prioritize providing GPS functions related to that location. Furthermore, if the user is on the move, the GPS unit can prioritize providing GPS functions related to the user's destination. Additionally, if the user is at home, the GPS unit can prioritize providing GPS functions related to home. This allows the GPS unit to select the optimal usage method by considering the user's geographical location information. Some or all of the above processing in the GPS unit may be performed using AI, for example, or without AI. For example, the GPS unit can select the optimal usage method when using GPS functions, using an AI model that selects the optimal usage method by considering the user's geographical location information.

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

[0066] Voice-controlled support systems can also monitor a user's health and provide health information. For example, if a user says, "Tell me my blood pressure today," the system can retrieve the user's blood pressure data and provide the result verbally. If a user says, "Give me exercise advice," the system can provide appropriate exercise advice based on the user's health data. Furthermore, if a user says, "Tell me when to take my medication," the system can check the user's medication schedule and provide a verbal reminder. This helps support the user's health management and contributes to maintaining good health.

[0067] Voice-controlled support systems can assist users with schedule management. For example, if a user says, "Tell me what I have to do today," the system can check the user's schedule and provide the schedule verbally. Similarly, if a user says, "Add something for tomorrow," the system can add an appointment to the schedule according to the user's instructions. Furthermore, if a user says, "Set a reminder for a meeting," the system can set a reminder based on the user's schedule and notify them verbally. This streamlines user schedule management and helps them avoid forgetting appointments.

[0068] Voice-controlled support systems can manage users' shopping lists. For example, if a user says, "Add milk to my shopping list," the system can add milk to the list. If a user says, "Tell me my shopping list," the system can also verbally provide the current shopping list. Furthermore, if a user says, "Remove eggs from my shopping list," the system can remove eggs from the list. This allows users to efficiently manage their shopping lists and ensure they don't forget anything they need while shopping.

[0069] Voice-controlled support systems can provide information based on the user's hobbies and interests. For example, if a user says, "Tell me some gardening information," the system can provide the latest information on gardening. If a user says, "Tell me some cooking recipes," the system can provide recipes tailored to the user's preferences. Furthermore, if a user says, "Tell me some travel recommendations," the system can provide travel recommendations based on the user's interests. This allows for the provision of information tailored to the user's hobbies and interests, enriching their daily lives.

[0070] Voice-controlled support systems allow users to operate their home appliances using voice commands. For example, if a user says, "Turn on the TV," the system can turn it on. Similarly, if a user says, "Lower the air conditioner temperature," the system can adjust the air conditioner temperature. Furthermore, if a user says, "Turn off the lights," the system can turn them off. This allows users to easily control their home appliances with their voice, improving the convenience of their daily lives.

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

[0072] Step 1: The reception desk accepts voice input. The reception desk can accept voice input using, for example, a microphone. Alternatively, the reception desk can accept voice input using specific voice commands. Step 2: The recognition unit recognizes the speech received by the reception unit and performs natural language processing. The recognition unit can analyze the speech using, for example, morphological analysis, grammatical analysis, and semantic analysis. Step 3: The operation unit sends and receives messages and retrieves information based on the content recognized by the recognition unit. The operation unit can send and receive messages using, for example, SMS, email, or chat applications. The operation unit can also retrieve information using internet searches or database access. Step 4: The providing unit provides the information acquired by the operating unit. The providing unit can provide information using, for example, audio output or screen display.

[0073] (Example of form 2) The voice-operated support system according to an embodiment of the present invention is a system that provides voice-operated support utilizing an AI agent to alleviate the inconveniences elderly people face when using smartphones. The voice-operated support system allows users to initiate operation by voice. For example, if a user says, "I want to send a message," the AI ​​agent recognizes the voice and launches a messaging app. Next, the user inputs the message they want to send by voice, the AI ​​agent converts the content into text, and sends the message to the designated recipient. Furthermore, information such as news, weather forecasts, and health information can also be obtained by voice. For example, if a user says, "Tell me today's weather," the AI ​​agent obtains the weather forecast and informs the user by voice. This eliminates visibility issues and allows for easy information acquisition. In addition, emergency contact can be made by voice, and the GPS function can be used to check the location of family members. For example, if a user says, "I want to make an emergency contact," the AI ​​agent calls the emergency contact and shares GPS information. This system addresses scenarios where elderly people want to use smartphones (communication with family and friends, obtaining news and weather forecasts, emergency contact, etc.) and provides a convenient and secure digital experience through voice operation. Furthermore, the voice AI agent recognizes the voices of elderly people and uses natural language processing to engage in conversation, improving ease of use. The voice AI agent can also support multiple languages, making it usable in care facilities in multilingual regions. In addition, customizable responses and voice commands allow for operation tailored to the preferences of elderly people. For example, it can be set to speak in specific words or styles. This system can also replace the roles of customer support, care workers, and system engineers. For example, customer support could provide user-friendly smartphone support services for the elderly and provide support and assistance during use. Care workers could design and support solutions to alleviate the daily inconveniences of the elderly, and system engineers could design and develop voice AI agents for the elderly.This system makes it easier for seniors to use smartphones and increases their opportunities to actively utilize digital devices in their daily lives. This will bridge the information gap and allow seniors to participate in the digital society with confidence. As a result, the voice-controlled support system can eliminate the inconveniences seniors face when using smartphones, providing a convenient and safe digital experience.

[0074] The voice-operated support system according to this embodiment comprises a reception unit, a recognition unit, an operation unit, and a provision unit. The reception unit receives voice input. The reception unit can receive voice input using, for example, a microphone. The reception unit can also receive voice input using specific voice commands. The recognition unit recognizes the voice received by the reception unit and performs natural language processing. The recognition unit can analyze the voice using, for example, morphological analysis. The recognition unit can also analyze the voice using grammatical analysis. Furthermore, the recognition unit can also analyze the voice using semantic analysis. The operation unit sends and receives messages and retrieves information based on the content recognized by the recognition unit. The operation unit can send and receive messages using, for example, SMS. The operation unit can also send and receive messages using email. Furthermore, the operation unit can send and receive messages using a chat application. The operation unit can retrieve information using, for example, internet search. The operation unit can also retrieve information using database access. The provision unit provides the information retrieved by the operation unit. The provision unit can provide information using, for example, voice output. Furthermore, the information provision unit can also provide information using a screen display. Thus, the voice-operated support system according to this embodiment can eliminate the inconveniences elderly people face when using smartphones by receiving, recognizing, operating, and providing voice input. Some or all of the above-described processes in the reception unit, recognition unit, operation unit, and information provision unit may be performed using, for example, AI, or without AI. For example, the reception unit can receive voice input using an AI model that accepts voice input. The recognition unit can recognize voice and perform natural language processing using an AI model that recognizes voice and performs natural language processing. The operation unit can send and receive messages and acquire information using an AI model that performs message sending and receiving and information acquisition. The information provision unit can provide information using an AI model that provides the acquired information.

[0075] The reception unit accepts voice input. For example, the reception unit can accept voice input using a microphone. Specifically, the microphone is highly sensitive and equipped with noise-canceling capabilities to eliminate ambient noise. This allows for clear capture of the user's voice. The reception unit can also accept voice input using specific voice commands. For example, a wake word such as "Hey, support system" can be set, and voice input can only begin when this wake word is detected. This prevents malfunctions and ensures that the user's intended actions are reliably received. Furthermore, the reception unit can support multiple languages ​​and accept voice input in the user's selected language. This allows for flexible operation in environments requiring multilingual support. To improve the quality of voice input, the reception unit can utilize voice filtering and echo cancellation technologies. This improves the accuracy of voice input, leading to more precise analysis by the recognition unit.

[0076] The recognition unit recognizes the speech received by the reception unit and performs natural language processing. For example, the recognition unit can analyze speech using morphological analysis. Morphological analysis is a technique that divides speech data into word units and analyzes the meaning and grammatical role of each word. The recognition unit can also analyze speech using grammatical analysis. Grammar analysis is a technique that analyzes the grammatical structure of speech data and clarifies the relationships between subjects, predicates, objects, etc., in sentences. Furthermore, the recognition unit can also analyze speech using semantic analysis. Semantic analysis is a technique that understands the meaning of speech data and accurately grasps the user's intent. By combining these analysis techniques, the recognition unit can analyze the user's voice input with high accuracy and generate appropriate operation instructions. The recognition unit can perform speech recognition and natural language processing using AI. Specifically, it uses a deep learning-based speech recognition model to convert speech data into text data. Next, it uses a natural language processing model to analyze the meaning of the text data and understand the user's intent. As a result, the recognition unit can handle complex speech input and perform appropriate operations according to the user's requests.

[0077] The control unit performs message sending and receiving and information retrieval based on the content recognized by the recognition unit. For example, the control unit can send and receive messages using SMS. Specifically, if a user instructs "Send a message" by voice, the control unit calls an API for sending SMS and sends a message to the specified recipient. The control unit can also send and receive messages using email. If a user instructs "Send an email," the control unit connects to an email server and sends an email to the specified recipient. Furthermore, the control unit can send and receive messages using a chat application. For example, if a user instructs "Send a message via chat," the control unit calls the chat application's API and sends a message to the specified recipient. The control unit can also retrieve information using internet search. If a user instructs "Tell me the weather," the control unit uses an internet search engine to retrieve the latest weather information and passes it to the information provider. The control unit can also retrieve information using database access. For example, if a user instructs "Tell me my schedule," the control unit accesses a database and retrieves the user's schedule information. This allows the control unit to retrieve information and send and receive messages in response to a variety of user requests.

[0078] The information provider provides information acquired by the operation unit. The information provider can provide information using, for example, voice output. Specifically, it uses speech synthesis technology to convey acquired information to the user in a natural voice. This allows the user to obtain information without relying on sight. The information provider can also provide information using screen display. For example, it can display acquired information on the screen of a smartphone or tablet so that the user can visually confirm it. Furthermore, the information provider can provide information using a notification function. For example, if important information is acquired, it can notify the user using the smartphone's notification function. The information provider can flexibly select the method of information provision to improve user convenience. For example, if the user prefers voice output, it will provide information by voice; if the user prefers screen display, it will display the information on the screen. The information provider can also receive user feedback and improve the method of information provision. For example, if the user wants to adjust the speed of voice output, the information provider will adjust the speed of speech synthesis to provide information according to the user's preference. In this way, the information provider can provide information to the user in the most optimal way and improve the overall usability of the system.

[0079] The customization section allows for the customization of voice commands. For example, it can customize specific phrases. The customization section can also configure the customization procedure. For example, the customization section can be configured to respond with "Do you want to send a message?" when the user says "I want to send a message." This allows for operation tailored to the user's preferences by customizing voice commands. Some or all of the above processing in the customization section may be performed using AI, for example, or without AI. For example, the customization section can customize voice commands using an AI model that customizes voice commands.

[0080] The multilingual unit can perform multilingual support. For example, the multilingual unit can perform multilingual support using a translation engine. The multilingual unit can also set a list of supported languages. For example, the multilingual unit can support languages ​​such as English, Japanese, and French. This allows the system to be used in care facilities in multilingual regions. Some or all of the above-described processes in the multilingual unit may be performed using AI, for example, or without AI. For example, the multilingual unit can perform multilingual support using an AI model that performs multilingual support using a translation engine.

[0081] The Emergency Contact Department can make emergency contacts. For example, the Emergency Contact Department can make emergency contacts using telephones. It can also make emergency contacts using emails. Furthermore, the Emergency Contact Department can make emergency contacts using emergency notification apps. This allows for a quick response in emergencies by enabling emergency contacts. Some or all of the above processes in the Emergency Contact Department may be performed using AI, for example, or not. For example, the Emergency Contact Department can make emergency contacts using an AI model.

[0082] The GPS unit can utilize GPS functionality. The GPS unit can, for example, configure how location information is acquired. It can also configure which applications to use. For example, the GPS unit can acquire location information using a specific application. This allows users to check the location of family members by utilizing GPS functionality. Some or all of the above-described processes in the GPS unit may be performed using AI, or not. For example, the GPS unit can acquire location information using an AI model for acquiring location information.

[0083] The recognition unit can recognize the voices of elderly people and perform natural language processing. For example, the recognition unit can analyze the frequency band of the elderly person's voice. The recognition unit can also analyze specific pronunciation patterns. This improves usability by recognizing the voices of elderly people and performing natural language processing. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can recognize the voices of elderly people and perform natural language processing using an AI model that recognizes the voices of elderly people and performs natural language processing.

[0084] The control unit can send and receive messages and retrieve information. For example, the control unit can send and receive messages using SMS. It can also send and receive messages using email. Furthermore, the control unit can send and receive messages using a chat application. For example, the control unit can retrieve information using internet search. It can also retrieve information using database access. This allows for a convenient digital experience even for elderly people who are not comfortable using smartphones, by enabling message sending and receiving and information retrieval. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can perform message sending and receiving and information retrieval using an AI model.

[0085] The information provider can provide the acquired information in voice. The information provider can provide the information in voice, for example, using speech synthesis technology. The information provider can also provide the information in voice using a voice output device. By providing the acquired information in voice, the problem of visibility can be resolved. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide the information in voice using an AI model that provides information in voice using speech synthesis technology.

[0086] The reception unit can estimate the user's emotions and adjust the timing of voice input acceptance based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the timing of voice input acceptance to allow the user to input in a relaxed state. The reception unit can also advance the timing of voice input acceptance if the user is in a hurry to allow them to start operating quickly. Furthermore, if the user is tired, the reception unit can adjust the timing of voice input acceptance to allow for comfortable operation. By adjusting the timing of voice input acceptance according to the user's emotions, voice input can be accepted at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can adjust the timing of voice input acceptance using an AI model that estimates the user's emotions and adjusts the timing of voice input acceptance.

[0087] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the reception unit prioritizes receiving voice commands that the user has frequently used in the past. The reception unit can also predict commands to be used during specific time periods based on the user's past voice input history and receive them. Furthermore, the reception unit can analyze the user's voice input history and suggest the optimal voice input method. This allows the reception unit to select the optimal reception method by analyzing the user's past voice input history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can select the optimal reception method using an AI model that analyzes the user's past voice input history and selects the optimal reception method.

[0088] The reception unit can filter voice input based on the user's current situation and environment. For example, if the user is in a noisy environment, the reception unit can apply noise cancellation when receiving voice input. Alternatively, if the user is in a quiet environment, the reception unit can accept normal voice input. Furthermore, if the user is moving, the reception unit can adjust the sensitivity of the voice input. This allows for the reception of appropriate voice input by filtering based on the user's current situation and environment. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can use an AI model that filters voice input based on the user's current situation and environment when receiving it.

[0089] The reception unit can estimate the user's emotions and determine the priority of voice input to be received based on the estimated emotions. For example, if the user is nervous, the reception unit will prioritize important voice input. Conversely, if the user is relaxed, the reception unit can prioritize normal voice input. Furthermore, if the user is in a hurry, the reception unit can prioritize urgent voice input. This allows for prioritizing important voice input by determining the priority of voice input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can determine the priority of voice input using an AI model that estimates the user's emotions and determines the priority of voice input.

[0090] The reception unit can prioritize receiving voice input by considering the user's geographical location when receiving voice input. For example, if the user is in a specific location, the reception unit will prioritize receiving voice input related to that location. Furthermore, if the user is on the move, the reception unit can prioritize receiving voice input related to their destination. Additionally, if the user is at home, the reception unit can prioritize receiving voice input related to their home. This allows for the prioritization of highly relevant voice input by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can receive voice input using an AI model that prioritizes receiving highly relevant voice input by considering the user's geographical location when receiving voice input.

[0091] The reception unit can analyze the user's social media activity when receiving voice input and accept relevant voice input. For example, the reception unit can prioritize receiving keywords that the user frequently uses on social media. The reception unit can also prioritize receiving voice input related to specific topics from the user's social media activity. Furthermore, the reception unit can analyze the user's social media activity and suggest the optimal voice input method. In this way, relevant voice input can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can receive voice input using an AI model that analyzes the user's social media activity and accepts relevant voice input when receiving voice input.

[0092] The 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 recognition unit can increase the accuracy of speech recognition to prevent misrecognition. The recognition unit can also apply normal speech recognition accuracy if the user is relaxed. Furthermore, if the user is in a hurry, the recognition unit can adjust the accuracy of speech recognition to enable faster recognition. This prevents misrecognition by adjusting the accuracy of speech recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI, or not. For example, the recognition unit can adjust the accuracy of speech recognition using an AI model that estimates the user's emotions and adjusts the accuracy of speech recognition.

[0093] The recognition unit can improve recognition accuracy by referring to the user's past speech patterns during speech recognition. For example, the recognition unit can improve speech recognition accuracy based on speech patterns previously used by the user. The recognition unit can also prioritize the recognition of specific phrases from the user's past speech history. Furthermore, the recognition unit can analyze the user's speech patterns and propose the optimal speech recognition method. This allows for improved speech recognition accuracy by referring to the user's past speech patterns. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can improve speech recognition accuracy by using an AI model that improves recognition accuracy by referring to the user's past speech patterns during speech recognition.

[0094] The recognition unit can apply a recognition algorithm according to the user's speaking speed and accent during speech recognition. For example, if the user speaks slowly, the recognition unit applies a recognition algorithm that corresponds to the speaking speed. The recognition unit can also apply a recognition algorithm that corresponds to the speaking speed if the user speaks quickly. Furthermore, the recognition unit can apply an optimal recognition algorithm according to the user's accent. This improves the accuracy of speech recognition by applying a recognition algorithm according to the user's speaking speed and accent. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can improve the accuracy of speech recognition by using an AI model that applies a recognition algorithm according to the user's speaking speed and accent during speech recognition.

[0095] The recognition unit can estimate the user's emotions and determine the priority of speech recognition based on the estimated emotions. For example, if the user is nervous, the recognition unit may prioritize important speech recognition. It may also prioritize normal speech recognition if the user is relaxed. Furthermore, if the user is in a hurry, the recognition unit may prioritize urgent speech recognition. This allows for prioritizing important speech recognition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI, or not. For example, the recognition unit may determine the priority of speech recognition using an AI model that estimates the user's emotions and determines the priority of speech recognition.

[0096] The recognition unit can improve recognition accuracy by considering the user's geographical location information during speech recognition. For example, if the user is in a specific location, the recognition unit can prioritize speech recognition related to that location. Furthermore, if the user is on the move, the recognition unit can prioritize speech recognition related to the user's destination. Additionally, if the user is at home, the recognition unit can prioritize speech recognition related to home. This improves the accuracy of speech recognition by considering the user's geographical location information. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can improve the accuracy of speech recognition by using an AI model that improves recognition accuracy by considering the user's geographical location information during speech recognition.

[0097] The recognition unit can improve recognition accuracy by referring to relevant literature used by the user during speech recognition. For example, if the user is talking about a specific topic, the recognition unit can improve recognition accuracy by referring to literature related to that topic. Furthermore, if the user uses technical terms, the recognition unit can also improve recognition accuracy by referring to relevant literature. In addition, the recognition unit can suggest the optimal speech recognition method by referring to literature related to the user's utterances. This allows for improved speech recognition accuracy by referring to relevant literature used by the user. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can improve speech recognition accuracy by using an AI model that improves recognition accuracy by referring to relevant literature used by the user during speech recognition.

[0098] The control unit can estimate the user's emotions and adjust the operation method based on the estimated emotions. For example, if the user is nervous, the control unit can provide a simple operation method. It can also provide a more detailed operation method if the user is relaxed. Furthermore, if the user is in a hurry, it can provide a quick operation method. This allows for the provision of a more appropriate operation method by adjusting the operation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 processes in the control unit may be performed using AI, or not. For example, the control unit can adjust the operation method using an AI model that estimates the user's emotions and adjusts the operation method accordingly.

[0099] The control unit can select the optimal operation method by referring to the user's past operation history during operation. For example, the control unit can prioritize providing operation methods that the user has frequently used in the past. The control unit can also predict and provide operation methods to be used during a specific time period based on the user's past operation history. Furthermore, the control unit can analyze the user's operation history and propose the optimal operation method. This allows the optimal operation method to be selected by referring to the user's past operation history. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can select the optimal operation method by using an AI model that selects the optimal operation method by referring to the user's past operation history during operation.

[0100] The control unit can customize the means of operation based on the user's current situation during operation. For example, if the user is on the move, the control unit can provide simplified means of operation. Alternatively, if the user is at home, the control unit can provide detailed means of operation. Furthermore, the control unit can suggest the optimal means of operation depending on the user's situation. This allows for the provision of appropriate means of operation by customizing them based on the user's current situation. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can customize the means of operation using an AI model that customizes the means of operation based on the user's current situation during operation.

[0101] The control unit can estimate the user's emotions and determine the priority of operations based on the estimated emotions. For example, if the user is tense, the control unit may prioritize important operations. It may also prioritize normal operations if the user is relaxed. Furthermore, if the user is in a hurry, the control unit may prioritize urgent operations. This allows for prioritizing important operations by determining the priority of operations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the control unit may be performed using AI, or not. For example, the control unit may determine the priority of operations using an AI model that estimates the user's emotions and determines the priority of operations.

[0102] The control unit can select the optimal operation method by considering the user's geographical location information during operation. For example, if the user is in a specific location, the control unit can provide an operation method related to that location. Furthermore, if the user is on the move, the control unit can also provide an operation method related to the destination. In addition, if the user is at home, the control unit can provide an operation method related to home. This allows for the selection of the optimal operation method by considering the user's geographical location information. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can select the optimal operation method by using an AI model that selects the optimal operation method by considering the user's geographical location information during operation.

[0103] The control unit can analyze the user's social media activity during operation and suggest methods of operation. For example, the control unit can prioritize providing methods of operation that the user frequently uses on social media. The control unit can also provide methods of operation related to specific topics based on the user's social media activity. Furthermore, the control unit can analyze the user's social media activity and suggest the optimal method of operation. This allows the control unit to suggest the optimal method of operation by analyzing the user's social media activity. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can suggest methods of operation using an AI model that analyzes the user's social media activity and suggests methods of operation during operation.

[0104] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is nervous, the information provider can provide a simple and highly visible method of information delivery. If the user is relaxed, the information provider can also provide a method of information delivery that includes detailed information. Furthermore, if the user is in a hurry, the information provider can provide a concise method of information delivery. This allows for more appropriate information delivery by adjusting the method of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can adjust the method of information delivery using an AI model that estimates the user's emotions and adjusts the method of information delivery.

[0105] The information delivery unit can select the optimal delivery method by referring to the user's past information acquisition history when providing information. For example, the delivery unit can prioritize providing information that the user has frequently acquired in the past. The delivery unit can also predict and provide information that the user will acquire at a specific time period based on the user's past information acquisition history. Furthermore, the delivery unit can analyze the user's information acquisition history and propose the optimal information delivery method. This allows the optimal information delivery method to be selected by referring to the user's past information acquisition history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can select the optimal delivery method by using an AI model that selects the optimal delivery method by referring to the user's past information acquisition history when providing information.

[0106] The information provider can customize the means of information provision based on the user's current situation when providing information. For example, if the user is on the move, the information provider can provide a simplified means of information provision. If the user is at home, the information provider can also provide a more detailed means of information provision. Furthermore, the information provider can suggest the most suitable means of information provision depending on the user's situation. In this way, by customizing the means of information provision based on the user's current situation, the appropriate means of information provision can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can customize the means of information provision using an AI model that customizes the means of information provision based on the user's current situation when providing information.

[0107] The information provider can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is stressed, the information provider can prioritize providing important information. It can also prioritize providing normal information if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize providing urgent information. This allows for the prioritization of important information by determining the priority of information delivery 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 information provider may be performed using AI, or not. For example, the information provider can determine the priority of information delivery using an AI model that estimates the user's emotions and determines the priority of information delivery.

[0108] The information provider can select the optimal information delivery method by considering the user's geographical location when providing information. For example, if the user is in a specific location, the provider can prioritize providing information related to that location. Furthermore, if the user is on the move, the provider can prioritize providing information related to their destination. Additionally, if the user is at home, the provider can prioritize providing information related to their home. This allows the provider to select the optimal information delivery method by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can select the optimal information delivery method by using an AI model that considers the user's geographical location when providing information.

[0109] The information provider can analyze the user's social media activity and provide relevant information when providing information. For example, the provider can prioritize providing information related to topics that the user frequently shows interest in on social media. The provider can also provide information related to specific topics based on the user's social media activity. Furthermore, the provider can analyze the user's social media activity and propose the optimal method of providing information. This allows the provider to provide relevant information by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can provide information using an AI model that analyzes the user's social media activity and provides relevant information when providing information.

[0110] The customization unit can estimate the user's emotions and customize voice commands based on those emotions. For example, if the user is nervous, the customization unit can provide a simple voice command. If the user is relaxed, it can also provide a more detailed voice command. Furthermore, if the user is in a hurry, it can provide a rapid voice command. This allows for the provision of more appropriate voice commands by customizing them 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-described processes in the customization unit may be performed using AI, or not. For example, the customization unit can customize voice commands using an AI model that estimates the user's emotions and customizes voice commands.

[0111] The customization unit can select the optimal customization method when customizing voice commands by referring to the user's past usage history. For example, the customization unit can prioritize providing voice commands that the user has frequently used in the past. The customization unit can also predict and provide voice commands to be used during specific time periods based on the user's past usage history. Furthermore, the customization unit can analyze the user's usage history and suggest the optimal voice commands. This allows the optimal voice command customization method to be selected by referring to the user's past usage history. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can select the optimal customization method when customizing voice commands by using an AI model that selects the optimal customization method by referring to the user's past usage history.

[0112] The customization 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 nervous, the customization unit can prioritize important voice commands. It can also prioritize normal voice commands if the user is relaxed. Furthermore, if the user is in a hurry, the customization unit can prioritize urgent voice commands. This allows for the prioritization of important voice commands based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can determine the priority of voice commands using an AI model that estimates the user's emotions and determines the priority of voice commands.

[0113] The customization unit can select the optimal customization method for voice commands by considering the user's geographical location information. For example, if the user is in a specific location, the customization unit can prioritize providing voice commands related to that location. Furthermore, if the user is on the move, the customization unit can prioritize providing voice commands related to their destination. Additionally, if the user is at home, the customization unit can prioritize providing voice commands related to their home. This allows the system to select the optimal voice command customization method by considering the user's geographical location information. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can select the voice command customization method using an AI model that selects the optimal customization method by considering the user's geographical location information when customizing voice commands.

[0114] The multilingual unit can estimate the user's emotions and adjust the multilingual support method based on the estimated emotions. For example, if the user is nervous, the multilingual unit can provide a simple multilingual support method. If the user is relaxed, it can also provide a more detailed multilingual support method. Furthermore, if the user is in a hurry, it can provide a rapid multilingual support method. This allows for more appropriate multilingual support by adjusting the multilingual support method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multilingual unit may be performed using AI, for example, or not using AI. For example, the multilingual unit can adjust the multilingual support method using an AI model that estimates the user's emotions and adjusts the multilingual support method.

[0115] The multilingual unit can select the optimal support method by referring to the user's past language usage history when providing multilingual support. For example, the multilingual unit can prioritize providing languages ​​that the user has frequently used in the past. Furthermore, the multilingual unit can predict and provide languages ​​that the user will use at specific times based on their past language usage history. In addition, the multilingual unit can analyze the user's language usage history and propose the optimal multilingual support method. This allows for the selection of the optimal multilingual support method by referring to the user's past language usage history. Some or all of the above processing in the multilingual unit may be performed using AI, for example, or without AI. For example, the multilingual unit can select a support method using an AI model that selects the optimal support method by referring to the user's past language usage history when providing multilingual support.

[0116] The multilingual unit can estimate the user's emotions and determine the priority of multilingual support based on the estimated emotions. For example, if the user is stressed, the multilingual unit can prioritize important multilingual support. It can also prioritize normal multilingual support if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize urgent multilingual support. This allows for the prioritization of important multilingual support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multilingual unit may be performed using AI or not. For example, the multilingual unit can determine the priority of multilingual support using an AI model that estimates the user's emotions and determines the priority of multilingual support.

[0117] The multilingual unit can select the optimal support method when providing multilingual support, taking into account the user's geographical location. For example, if the user is in a specific location, the multilingual unit can prioritize providing the language relevant to that location. Furthermore, if the user is on the move, the multilingual unit can prioritize providing the language relevant to their destination. Additionally, if the user is at home, the multilingual unit can prioritize providing the language relevant to their home. This allows the system to select the optimal multilingual support method by considering the user's geographical location. Some or all of the above processing in the multilingual unit may be performed using AI, for example, or without AI. For instance, the multilingual unit can select the optimal support method when providing multilingual support using an AI model that considers the user's geographical location.

[0118] The emergency contact unit can estimate the user's emotions and adjust the method of emergency contact based on the estimated emotions. For example, if the user is stressed, the emergency contact unit can provide a quick and concise method of emergency contact. If the user is relaxed, the emergency contact unit can also provide a detailed method of emergency contact. Furthermore, if the user is in a hurry, the emergency contact unit can provide a method of contact that will get in touch in the shortest possible time. This allows for quick and appropriate emergency contact by adjusting the method of emergency contact according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emergency contact unit may be performed using AI, for example, or not using AI. For example, the emergency contact unit can adjust the method of emergency contact using an AI model that estimates the user's emotions and adjusts the method of emergency contact.

[0119] The emergency contact unit can select the most suitable contact method by referring to the user's past emergency contact history during an emergency. For example, the emergency contact unit may prioritize providing emergency contact methods that the user has frequently used in the past. The emergency contact unit can also predict and provide contact methods to be used during specific time periods based on the user's past emergency contact history. Furthermore, the emergency contact unit can analyze the user's emergency contact history and suggest the most suitable contact method. This allows the unit to select the most suitable contact method by referring to the user's past emergency contact history. Some or all of the above processes in the emergency contact unit may be performed using AI, for example, or without AI. For example, the emergency contact unit may select a contact method by using an AI model that selects the most suitable contact method by referring to the user's past emergency contact history during an emergency.

[0120] The emergency contact unit can estimate the user's emotions and determine the priority of emergency contacts based on the estimated emotions. For example, if the user is stressed, the emergency contact unit will prioritize important emergency contacts. It can also prioritize regular emergency contacts if the user is relaxed. Furthermore, if the user is in a hurry, the emergency contact unit can prioritize urgent contacts. This allows for prioritizing important emergency contacts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emergency contact unit may be performed using AI or not. For example, the emergency contact unit can determine the priority of emergency contacts using an AI model that estimates the user's emotions and determines the priority of emergency contacts.

[0121] The emergency contact unit can select the most appropriate contact method when an emergency occurs, taking into account the user's geographical location. For example, if the user is in a specific location, the emergency contact unit can prioritize providing an emergency contact method relevant to that location. Furthermore, if the user is on the move, the emergency contact unit can prioritize providing an emergency contact method relevant to their destination. Additionally, if the user is at home, the emergency contact unit can prioritize providing an emergency contact method relevant to their home. This allows the system to select the most appropriate contact method by considering the user's geographical location. Some or all of the above processing in the emergency contact unit may be performed using AI, for example, or without AI. For instance, the emergency contact unit can select a contact method using an AI model that selects the most appropriate contact method when an emergency occurs, taking into account the user's geographical location.

[0122] The GPS unit can estimate the user's emotions and adjust how the GPS function is used based on the estimated emotions. For example, if the user is nervous, the GPS unit can provide a simple GPS usage method. It can also provide a more detailed GPS usage method if the user is relaxed. Furthermore, if the user is in a hurry, it can provide a rapid GPS usage method. This allows for more appropriate use of the GPS function by adjusting its usage 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 GPS unit may be performed using AI, or not. For example, the GPS unit can adjust how the GPS function is used by using an AI model that estimates the user's emotions and adjusts how the GPS function is used.

[0123] The GPS unit can select the optimal usage method by referring to the user's past location history when using the GPS function. For example, the GPS unit may prioritize providing locations that the user has frequently visited in the past. The GPS unit can also predict and provide locations to be used during specific time periods based on the user's past location history. Furthermore, the GPS unit can analyze the user's location history and propose the optimal GPS usage method. This allows the optimal usage method of the GPS function to be selected by referring to the user's past location history. Some or all of the above processing in the GPS unit may be performed using AI, for example, or without AI. For example, the GPS unit may select the usage method by using an AI model that selects the optimal usage method by referring to the user's past location history when using the GPS function.

[0124] The GPS unit can estimate the user's emotions and prioritize GPS functions based on those emotions. For example, if the user is stressed, the GPS unit will prioritize important GPS functions. It can also prioritize normal GPS functions if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize urgent GPS functions. This ensures that important GPS functions are prioritized based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the GPS unit may be performed using AI or not. For example, the GPS unit can determine the priority of GPS functions using an AI model that estimates the user's emotions and determines the priority of GPS functions.

[0125] The GPS unit can select the optimal usage method when using GPS functions, taking into account the user's geographical location information. For example, if the user is in a specific location, the GPS unit can prioritize providing GPS functions related to that location. Furthermore, if the user is on the move, the GPS unit can prioritize providing GPS functions related to the user's destination. Additionally, if the user is at home, the GPS unit can prioritize providing GPS functions related to home. This allows the GPS unit to select the optimal usage method by considering the user's geographical location information. Some or all of the above processing in the GPS unit may be performed using AI, for example, or without AI. For example, the GPS unit can select the optimal usage method when using GPS functions, using an AI model that selects the optimal usage method by considering the user's geographical location information.

[0126] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0127] Voice-controlled support systems can also monitor a user's health and provide health information. For example, if a user says, "Tell me my blood pressure today," the system can retrieve the user's blood pressure data and provide the result verbally. If a user says, "Give me exercise advice," the system can provide appropriate exercise advice based on the user's health data. Furthermore, if a user says, "Tell me when to take my medication," the system can check the user's medication schedule and provide a verbal reminder. This helps support the user's health management and contributes to maintaining good health.

[0128] Voice-controlled support systems can estimate a user's emotions and recommend music based on those estimates. For example, if a user says, "I want to relax," the system can estimate the user's emotion as relaxed and recommend relaxing music. Similarly, if a user says, "I want to feel energized," the system can estimate the user's emotion as energized and recommend uplifting music. Furthermore, if a user says, "I want to calm down," the system can estimate the user's emotion as calm and recommend calming music. This allows the system to provide music that matches the user's emotions and improve their mood.

[0129] Voice-controlled support systems can assist users with schedule management. For example, if a user says, "Tell me what I have to do today," the system can check the user's schedule and provide the schedule verbally. Similarly, if a user says, "Add something for tomorrow," the system can add an appointment to the schedule according to the user's instructions. Furthermore, if a user says, "Set a reminder for a meeting," the system can set a reminder based on the user's schedule and notify them verbally. This streamlines user schedule management and helps them avoid forgetting appointments.

[0130] Voice-controlled support systems can estimate a user's emotions and suggest relaxation methods based on those estimations. For example, if a user says, "I'm feeling stressed," the system can estimate the user's emotion as stress and suggest relaxation techniques such as deep breathing or meditation. Similarly, if a user says, "I'm tired," the system can estimate the user's emotion as fatigue and suggest relaxation techniques such as stretching or light exercise. Furthermore, if a user says, "I'm feeling anxious," the system can estimate the user's emotion as anxiety and suggest relaxation techniques such as aromatherapy or music therapy. This allows the system to provide relaxation methods tailored to the user's emotions, supporting their mental and physical well-being.

[0131] Voice-controlled support systems can manage users' shopping lists. For example, if a user says, "Add milk to my shopping list," the system can add milk to the list. If a user says, "Tell me my shopping list," the system can also verbally provide the current shopping list. Furthermore, if a user says, "Remove eggs from my shopping list," the system can remove eggs from the list. This allows users to efficiently manage their shopping lists and ensure they don't forget anything they need while shopping.

[0132] Voice-activated support systems can estimate a user's emotions and provide encouraging messages based on those estimates. For example, if a user says, "I'm feeling down," the system can estimate that the user is feeling down and provide an encouraging message. Similarly, if a user says, "I'm not motivated," the system can estimate that the user is feeling unmotivated and provide a message to boost their motivation. Furthermore, if a user says, "I'm feeling anxious," the system can estimate that the user is feeling anxious and provide a reassuring message. This allows for the provision of encouraging messages tailored to the user's emotions, helping them to feel more positive.

[0133] Voice-controlled support systems can provide information based on the user's hobbies and interests. For example, if a user says, "Tell me some gardening information," the system can provide the latest information on gardening. If a user says, "Tell me some cooking recipes," the system can provide recipes tailored to the user's preferences. Furthermore, if a user says, "Tell me some travel recommendations," the system can provide travel recommendations based on the user's interests. This allows for the provision of information tailored to the user's hobbies and interests, enriching their daily lives.

[0134] Voice-controlled support systems can estimate a user's emotions and suggest appropriate activities based on those estimates. For example, if a user says, "I'm bored," the system can estimate the user's emotion as boredom and suggest enjoyable activities. Similarly, if a user says, "I'm stressed," the system can estimate the user's emotion as stress and suggest relaxing activities. Furthermore, if a user says, "I'm feeling down," the system can estimate the user's emotion as low energy and suggest activities to boost energy. This allows for the provision of activities tailored to the user's emotions, thereby improving their mood.

[0135] Voice-controlled support systems allow users to operate their home appliances using voice commands. For example, if a user says, "Turn on the TV," the system can turn it on. Similarly, if a user says, "Lower the air conditioner temperature," the system can adjust the air conditioner temperature. Furthermore, if a user says, "Turn off the lights," the system can turn them off. This allows users to easily control their home appliances with their voice, improving the convenience of their daily lives.

[0136] Voice-controlled support systems can estimate a user's emotions and provide appropriate news based on those estimates. For example, if a user says, "I'm feeling down," the system can estimate that the user is feeling down and provide positive news. Similarly, if a user says, "I'm not feeling well," the system can estimate that the user is feeling unwell and provide uplifting news. Furthermore, if a user says, "I'm feeling anxious," the system can estimate that the user is feeling anxious and provide reassuring news. This allows the system to provide news tailored to the user's emotions, helping them to feel more positive.

[0137] The following briefly describes the processing flow for example form 2.

[0138] Step 1: The reception desk accepts voice input. The reception desk can accept voice input using, for example, a microphone. Alternatively, the reception desk can accept voice input using specific voice commands. Step 2: The recognition unit recognizes the speech received by the reception unit and performs natural language processing. The recognition unit can analyze the speech using, for example, morphological analysis, grammatical analysis, and semantic analysis. Step 3: The operation unit sends and receives messages and retrieves information based on the content recognized by the recognition unit. The operation unit can send and receive messages using, for example, SMS, email, or chat applications. The operation unit can also retrieve information using internet searches or database access. Step 4: The providing unit provides the information acquired by the operating unit. The providing unit can provide information using, for example, audio output or screen display.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, recognition unit, operation unit, provision unit, customization unit, multilingual unit, emergency contact unit, and GPS unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 38B of the smart device 14. The recognition unit recognizes the voice using the specific processing unit 290 of the data processing unit 12 and performs natural language processing. The operation unit sends and receives messages and acquires information using the specific processing unit 290 of the data processing unit 12. The provision unit provides information using the speaker 40B of the smart device 14. The customization unit customizes voice commands using the specific processing unit 290 of the data processing unit 12. The multilingual unit supports multiple languages ​​using the specific processing unit 290 of the data processing unit 12. The emergency contact unit makes emergency contacts using the telephone function of the smart device 14. The GPS unit acquires location information using the GPS function of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, recognition unit, operation unit, provision unit, customization unit, multilingual unit, emergency contact unit, and GPS unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 238 of the smart glasses 214. The recognition unit recognizes voice and performs natural language processing using the specific processing unit 290 of the data processing unit 12. The operation unit sends and receives messages and acquires information using the specific processing unit 290 of the data processing unit 12. The provision unit provides information using the speaker 240 of the smart glasses 214. The customization unit customizes voice commands using the specific processing unit 290 of the data processing unit 12. The multilingual unit supports multiple languages ​​using the specific processing unit 290 of the data processing unit 12. The emergency contact unit makes emergency contacts using the telephone function of the smart glasses 214. The GPS unit acquires location information using the GPS function of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the reception unit, recognition unit, operation unit, provision unit, customization unit, multilingual unit, emergency contact unit, and GPS unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 238 of the headset terminal 314. The recognition unit recognizes the voice using the specific processing unit 290 of the data processing unit 12 and performs natural language processing. The operation unit sends and receives messages and acquires information using the specific processing unit 290 of the data processing unit 12. The provision unit provides information using the speaker 240 of the headset terminal 314. The customization unit customizes voice commands using the specific processing unit 290 of the data processing unit 12. The multilingual unit supports multiple languages ​​using the specific processing unit 290 of the data processing unit 12. The emergency contact unit makes emergency contacts using the telephone function of the headset terminal 314. The GPS unit acquires location information using the GPS function of the headset terminal 314. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0175] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Each of the multiple elements described above, including the reception unit, recognition unit, operation unit, provision unit, customization unit, multilingual unit, emergency contact unit, and GPS unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 238 of the robot 414. The recognition unit recognizes voice and performs natural language processing using the specific processing unit 290 of the data processing unit 12. The operation unit sends and receives messages and acquires information using the specific processing unit 290 of the data processing unit 12. The provision unit provides information using the speaker 240 of the robot 414. The customization unit customizes voice commands using the specific processing unit 290 of the data processing unit 12. The multilingual unit supports multiple languages ​​using the specific processing unit 290 of the data processing unit 12. The emergency contact unit makes emergency contacts using the telephone function of the robot 414. The GPS unit acquires location information using the GPS function of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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."

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] (Note 1) A reception desk that accepts voice input, A recognition unit that recognizes the voice received by the reception unit and performs natural language processing, An operation unit that sends and receives messages and acquires information based on the content recognized by the recognition unit, The system includes a providing unit that provides information acquired by the aforementioned operating unit. A system characterized by the following features. (Note 2) It includes a customization section for customizing voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a multilingual section that supports multiple languages. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with an emergency liaison department to handle emergency communications. The system described in Appendix 1, characterized by the features described herein. (Note 5) It is equipped with a GPS unit that utilizes GPS functionality. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recognition unit, Recognizing the voices of elderly people and performing natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned operating unit is Sending and receiving messages and retrieving information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, The acquired information is provided in audio format. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system analyzes the user's past voice input history and selects the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving voice input, filtering is performed based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice input to accept based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving voice input, the system prioritizes accepting voice input that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When receiving voice input, the system analyzes the user's social media activity and accepts relevant voice input. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned 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 16) The aforementioned recognition unit, During speech recognition, the system improves recognition accuracy by referencing the user's past speech patterns. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recognition unit, During speech recognition, the recognition algorithm is applied according to the user's speaking speed and accent. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recognition unit, It estimates the user's emotions and determines the priority of speech recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recognition unit, When performing speech recognition, the accuracy of recognition is improved by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recognition unit, During speech recognition, the system improves recognition accuracy by referencing relevant literature used by the user. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned operating unit is It estimates the user's emotions and adjusts the way it interacts with the user based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned operating unit is During operation, the system selects the optimal operation method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned operating unit is During operation, the means of operation are customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned operating unit is It estimates the user's emotions and determines the priority of actions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned operating unit is During operation, the system selects the optimal operation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned operating unit is During operation, the system analyzes the user's social media activity and suggests appropriate actions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, the system selects the most suitable method of delivery by referring to the user's past information acquisition history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, customize the method of information delivery based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing information, the optimal method of information delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned customization unit is It estimates the user's emotions and customizes voice commands based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned customization unit is When customizing voice commands, the system selects the optimal customization method by referring to the user's past usage history. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned customization unit is It estimates the user's emotions and prioritizes voice commands based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned customization unit is When customizing voice commands, the optimal customization method is selected by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned multilingual unit is It estimates the user's emotions and adjusts the multilingual support method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned multilingual unit is When implementing multilingual support, the system selects the optimal support method by referring to the user's past language usage history. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned multilingual unit is It estimates user sentiment and determines the priority of multilingual support based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned multilingual unit is When providing multilingual support, the optimal support method is selected by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned emergency liaison department, The system estimates the user's emotions and adjusts the method of emergency contact based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned emergency liaison department, In the event of an emergency, the system will refer to the user's past emergency contact history to select the most appropriate contact method. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned emergency liaison department, It estimates the user's emotions and prioritizes emergency contacts based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned emergency liaison department, In emergency situations, the system selects the most appropriate contact method, taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 45) The GPS unit is, It estimates the user's emotions and adjusts how GPS functionality is used based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 46) The GPS unit is, When using the GPS function, the system selects the optimal usage method by referring to the user's past location history. The system described in Appendix 5, characterized by the features described herein. (Note 47) The GPS unit is, It estimates the user's emotions and prioritizes GPS functions based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 48) The GPS unit is, When using the GPS function, the system selects the optimal usage method by considering the user's geographical location. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0211] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts voice input, A recognition unit that recognizes the voice received by the reception unit and performs natural language processing, An operation unit that sends and receives messages and acquires information based on the content recognized by the recognition unit, The system includes a providing unit that provides information acquired by the aforementioned operating unit. A system characterized by the following features.

2. It includes a customization section for customizing voice commands. The system according to feature 1.

3. It has a multilingual section that supports multiple languages. The system according to feature 1.

4. It is equipped with an emergency liaison department to handle emergency communications. The system according to feature 1.

5. It is equipped with a GPS unit that utilizes GPS functionality. The system according to feature 1.

6. The aforementioned recognition unit, Recognizing the voices of elderly people and performing natural language processing. The system according to feature 1.

7. The aforementioned operating unit is Sending and receiving messages and retrieving information. The system according to feature 1.

8. The aforementioned supply unit is, The acquired information is provided in audio format. The system according to feature 1.

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

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

11. The aforementioned reception unit is When receiving voice input, filtering is performed based on the user's current situation and environment. The system according to feature 1.

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

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