system

Abstract

The system according to this embodiment aims to understand the context of a conversation and suggest appropriate responses in multiple languages. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a translation unit. The collection unit collects the other party's spoken and nonverbal information. The analysis unit analyzes the information collected by the collection unit and understands the context of the conversation. The suggestion unit suggests an appropriate response based on the context understood by the analysis unit. The translation unit translates the response suggested by the suggestion unit into multiple languages.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

[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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to understand the context of a conversation and propose an appropriate response, and there are particularly problems in multi-language communication.

[0005] The system according to the embodiment aims to understand the context of a conversation and propose an appropriate response in multiple languages.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a translation unit. The collection unit collects the other party's spoken and nonverbal information. The analysis unit analyzes the information collected by the collection unit and understands the context of the conversation. The suggestion unit proposes an appropriate response based on the context understood by the analysis unit. The translation unit translates the response proposed by the suggestion unit into multiple languages. [Effects of the Invention]

[0007] The system according to this embodiment can understand the context of a conversation and suggest appropriate responses in multiple languages. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The next-generation communication device according to an embodiment of the present invention is an AI-equipped earphone with a high-performance microphone and camera. This earphone has the function of understanding the context of a conversation and suggesting an appropriate response. It can also analyze the other party's nonverbal information (such as facial expressions and hand gestures) and suggest conversational responses. Furthermore, it is equipped with a multilingual translation function and is a next-generation communication device that supports participation in meetings. For example, this earphone operates in the following steps: First, it uses a high-performance microphone and camera to collect the other party's statements and nonverbal information. Next, the AI ​​analyzes the collected information and understands the context of the conversation. For example, if the other party is speaking with a smile, it can suggest a positive response based on their facial expression. It can also analyze the other party's hand gestures and gestures and suggest appropriate conversational responses. Furthermore, this earphone is equipped with a multilingual translation function and can translate foreign languages ​​in real time. For example, if spoken to in English, it can be translated into Japanese and conveyed to the user. This allows even users who are not proficient in foreign languages ​​to communicate smoothly. These earphones target business professionals who lack confidence in meetings and conversations with clients, addressing challenges such as insufficient knowledge and experience, difficulty with real-time communication, poor nonverbal communication skills, and foreign language proficiency. Specifically, they support real-time communication by understanding what the other person is saying and the context of the meeting, and suggesting appropriate responses. They can also analyze nonverbal communication (facial expressions, gestures) and suggest the most appropriate responses. Furthermore, they can suggest the most appropriate responses based on information from the internet. In this way, AI-powered earphones equipped with high-performance microphones and cameras can improve business professionals' communication skills as a next-generation communication device that supports participation in meetings. Thus, next-generation communication devices can improve the communication skills of business professionals.

[0029] The next-generation communication device according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a translation unit. The collection unit collects the other party's speech and nonverbal information. For example, the collection unit collects the other party's speech using a high-performance microphone. The collection unit can also collect nonverbal information such as the other party's facial expressions and hand gestures using a camera. Furthermore, the collection unit can analyze the other party's gestures in real time and extract important information. For example, the collection unit collects the other party's speech using a high-performance microphone and obtains clear audio data using noise-canceling technology. The collection unit can also capture the other party's facial expressions with a camera and analyze emotions using facial expression recognition technology. Furthermore, the collection unit can track the other party's gestures with a camera and extract important information using gesture recognition technology. The analysis unit analyzes the information collected by the collection unit and understands the context of the conversation. For example, the analysis unit analyzes the content of the other party's speech using speech analysis technology. The analysis unit can also analyze the other party's emotions using facial expression analysis technology. Furthermore, the analysis unit can analyze the other party's intentions using gesture analysis technology. For example, the analysis unit uses speech analysis technology to analyze the content of what the other party says and extract important keywords. The analysis unit can also use facial expression analysis technology to analyze the other party's emotions and detect changes in those emotions. Furthermore, the analysis unit can use gesture analysis technology to analyze the other party's intentions and predict the flow of the conversation. The suggestion unit proposes an appropriate response based on the context understood by the analysis unit. For example, the suggestion unit may refer to information on the internet to propose the best response. It can also refer to past conversation history to propose an appropriate response. Furthermore, the suggestion unit can adjust the expression of the response based on the user's emotions. For example, it may refer to news articles on the internet to propose a response based on the latest information. It can also refer to past conversation history to propose a response tailored to the other party's preferences. Furthermore, the suggestion unit can use expressions to help the user relax if they are nervous, and expressions to help them calm down if they are excited. The translation unit translates the responses proposed by the suggestion unit into multiple languages.The translation unit can, for example, translate foreign languages ​​in real time. It can also adjust the translation's expression based on the user's emotions. Furthermore, it can provide appropriate translations considering the recipient's cultural background and linguistic characteristics. For example, the translation unit can translate English into Japanese in real time. It can also use expressions to help the user relax if they are nervous, and expressions to help them calm down if they are excited. Furthermore, it can provide appropriate translations considering the recipient's cultural background and linguistic characteristics. This allows next-generation communication devices to support next-generation communication by collecting and analyzing the recipient's spoken and nonverbal information, suggesting appropriate responses, and translating them into multiple languages.

[0030] The data collection unit collects both spoken and nonverbal information from the other party. For example, the collection unit collects the other party's speech using a high-performance microphone. The collection unit can also collect nonverbal information such as the other party's facial expressions and hand gestures using a camera. Furthermore, the collection unit can analyze the other party's gestures in real time and extract important information. Specifically, the collection unit utilizes noise cancellation technology to clearly collect the other party's speech using a high-performance microphone. This technology effectively removes ambient noise, allowing for accurate acquisition of the content of the speech. It also uses a camera to capture the other party's facial expressions and analyzes their emotions using facial recognition technology. For example, it can detect expressions such as smiles, anger, and surprise in real time, allowing for an understanding of the other party's emotional state. In addition, the camera tracks the other party's gestures, and gesture recognition technology is used to extract important information from hand gestures and body movements. This makes it possible to understand the other party's intentions and emotions more deeply. The collection unit centrally manages this data and transmits it to the analysis unit in real time. The frequency and accuracy of data collection can be adjusted according to the situation, allowing for flexible responses under specific conditions. For example, data can be collected frequently and analyzed in detail during particularly important situations such as meetings or presentations. This allows the data collection unit to gather a wide range of data from various devices and understand the situation in real time.

[0031] The analysis unit analyzes the information collected by the collection unit to understand the context of the conversation. For example, the analysis unit uses speech analysis technology to analyze the content of what the other person says. The analysis unit can also analyze the other person's emotions using facial expression analysis technology. Furthermore, the analysis unit can analyze the other person's intentions using gesture analysis technology. Specifically, it uses speech analysis technology to analyze the content of what the other person says and extract important keywords. This allows for the understanding of the topic and important points of the conversation. It can also use facial expression analysis technology to analyze the other person's emotions and detect changes in emotion. For example, a smile can be detected as a positive emotion, and a frown as a negative emotion. Furthermore, gesture analysis technology can be used to analyze the other person's intentions and predict the flow of the conversation. For example, a raised hand can be interpreted as an indication of the speaker's intention, and crossed arms can be interpreted as a defensive attitude. The analysis unit integrates this information to gain a deep understanding of the context of the conversation. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term trend analysis and risk assessment. For example, by analyzing conversation patterns with specific individuals based on past conversation history, the system can formulate future countermeasures. Furthermore, anomaly detection algorithms can be used to detect unusual patterns or abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0032] The suggestion unit proposes appropriate responses based on the context understood by the analysis unit. For example, the suggestion unit can suggest the best response by referring to information on the internet. It can also suggest appropriate responses by referring to past conversation history. Furthermore, the suggestion unit can adjust the expression of responses based on the user's emotions. Specifically, the suggestion unit refers to news articles and expert information sources on the internet to suggest responses based on the latest information. This ensures that users can always provide appropriate responses based on the latest information. The suggestion unit can also suggest responses tailored to the other party's preferences and interests by referring to past conversation history. For example, it can provide relevant information based on topics and questions the other party has shown interest in in the past. Furthermore, the suggestion unit can adjust the expression of responses according to the user's emotional state. For example, it can use expressions to help the user relax if they are nervous, and expressions to help them calm down if they are excited. The suggestion unit integrates this information and proposes the best response in real time. Additionally, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can record how the user reacted to the suggested responses and reflect this in future suggestions. This allows the proposal department to consistently provide users with the best possible responses and improve the quality of communication.

[0033] The translation department translates the responses proposed by the proposal department into multiple languages. For example, the translation department translates foreign languages ​​in real time. It can also adjust the translation's expression based on the user's emotions. Furthermore, the translation department can provide appropriate translations considering the recipient's cultural background and linguistic characteristics. Specifically, the translation department utilizes machine translation technology to translate English into Japanese in real time. This technology enables rapid and accurate translation. The translation department can also adjust the translation's expression according to the user's emotional state. For example, it can use expressions to help the user relax if they are nervous, and expressions to help them calm down if they are excited. Furthermore, the translation department can provide appropriate translations considering the recipient's cultural background and linguistic characteristics. For example, if the recipient is from a specific cultural background, using expressions and phrases specific to that culture can lead to more natural communication. The translation department integrates this information to provide the optimal translation in real time. Additionally, the translation department can collect user feedback and continuously improve the accuracy and effectiveness of the translations. For example, it's possible to record how users reacted to the translated content and incorporate that feedback into future translations. This allows the translation team to consistently provide users with the best possible translations and improve the quality of communication.

[0034] The collection unit can collect the other party's speech and nonverbal information using a high-performance microphone and camera. For example, the collection unit can collect the other party's speech using a high-performance microphone. The collection unit can also collect nonverbal information such as the other party's facial expressions and hand gestures using a camera. For example, the collection unit can collect the other party's speech with a high-performance microphone and obtain clear audio data using noise cancellation technology. For example, the collection unit can also photograph the other party's facial expressions with a camera and analyze their emotions using facial recognition technology. For example, the collection unit can track the other party's gestures with a camera and extract important information using gesture recognition technology. In this way, by using a high-performance microphone and camera, the other party's speech and nonverbal information can be collected with high accuracy. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the audio data collected by the high-performance microphone into a generating AI and have the generating AI perform the analysis of the audio data.

[0035] The analysis unit can analyze nonverbal information such as the other person's facial expressions and hand gestures. For example, the analysis unit can analyze the other person's facial expressions using facial expression analysis technology. The analysis unit can also analyze the other person's hand gestures using gesture analysis technology. For example, the analysis unit can analyze the other person's facial expressions using facial expression analysis technology and detect changes in emotion. For example, the analysis unit can analyze the other person's hand gestures using gesture analysis technology and estimate their intentions. This allows for a deeper understanding of the conversation context by analyzing the other person's nonverbal information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input facial expression data into a generating AI and have the generating AI perform emotion analysis.

[0036] The suggestion unit can propose the most appropriate statement from information on the internet. For example, the suggestion unit can propose a statement based on the latest information by referring to news articles on the internet. The suggestion unit can also propose a statement based on relevant information by referring to databases on the internet. For example, the suggestion unit can propose a statement based on the latest information by referring to news articles on the internet. The suggestion unit can also propose a statement based on relevant information by referring to databases on the internet. This makes it possible to propose more appropriate statements by utilizing information on the internet. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information from the internet into a generation AI and have the generation AI execute the proposal of the most appropriate statement.

[0037] The translation unit can translate foreign languages ​​in real time. For example, the translation unit can translate English to Japanese in real time. For example, the translation unit can also translate Chinese to English in real time. For example, the translation unit can translate English to Japanese in real time. For example, the translation unit can also translate Chinese to English in real time. This enables communication that transcends language barriers through real-time translation. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input foreign language audio data into a generating AI and have the generating AI perform real-time translation.

[0038] The collection unit can filter ambient sounds to remove noise and collect clearer audio information. For example, the collection unit can analyze ambient noise in real time and perform noise cancellation. The collection unit can also, for example, emphasize sounds in specific frequency bands to clarify important statements. The collection unit can also, for example, reduce background noise to collect conversational audio clearly. This allows for the collection of clear audio information by removing noise. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input ambient sound data into a generating AI and have the generating AI perform noise reduction.

[0039] The data collection unit can track the movement of the subject's gaze and prioritize the collection of important information based on the direction of their gaze. For example, the data collection unit can prioritize the collection of information about objects or people the subject is focusing on. For example, the data collection unit can also collect information about the direction the subject's gaze frequently points. For example, the data collection unit can estimate topics of interest from the movement of the subject's gaze and collect that information. In this way, by tracking the movement of the subject's gaze, important information can be prioritized for collection. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input gaze data into a generating AI and have the generating AI perform the collection of important information.

[0040] The data collection unit can collect additional information from the other person's clothing and accessories to complement the conversation context. For example, the data collection unit can collect information about the other person's occupation and hobbies from their clothing. For example, the data collection unit can estimate the other person's preferences and interests from their accessories and collect related information. For example, the data collection unit can estimate the other person's social background from the brands of their clothing and accessories and complement the conversation context. In this way, the conversation context can be complemented by collecting information from clothing and accessories. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input clothing and accessory data into a generating AI and have the generating AI perform the task of complementing the conversation context.

[0041] The data collection unit can analyze the tone and speed of the other person's voice and estimate their emotions and level of tension. For example, if the voice tone is high, the data collection unit can estimate that the person is excited and collect related information. For example, if the voice speed is fast, the data collection unit can estimate that the person is tense and collect information to help them relax. For example, if the voice tone is low, the data collection unit can estimate that the person is calm and collect detailed information. In this way, by analyzing the tone and speed of the voice, the emotions and level of tension of the other person can be estimated. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input voice tone and speed data into a generating AI and have the generating AI perform the estimation of emotions and tension levels.

[0042] The analysis unit can perform a more accurate contextual understanding by considering the context of the other party's statement and comparing it with past conversation history. For example, the analysis unit can estimate the intent of the other party's statement from past conversation history and compare it with the current statement. The analysis unit can also supplement background information of the other party's statement based on past conversation history. For example, the analysis unit can analyze past conversation history and understand patterns in the other party's statements. This enables a more accurate contextual understanding by comparing it with past conversation history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past conversation history data into a generating AI and have the generating AI perform contextual understanding.

[0043] The analysis unit can interpret nonverbal information by considering the other party's cultural background and linguistic characteristics. For example, the analysis unit can accurately interpret the meaning of gestures by considering the other party's cultural background. The analysis unit can also accurately interpret the meaning of facial expressions by considering the other party's linguistic characteristics. The analysis unit can also adjust the interpretation of nonverbal information based on the other party's cultural background. This makes the interpretation of nonverbal information more accurate by considering cultural background and linguistic characteristics. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on cultural background and linguistic characteristics into a generating AI and have the generating AI perform the interpretation of nonverbal information.

[0044] The analysis unit can analyze the frequency and patterns of the other party's gestures and predict the flow of the conversation. For example, the analysis unit can analyze the frequency of the other party's gestures and predict the timing of the next statement. For example, the analysis unit can also analyze the patterns of the other party's gestures and predict the direction of the conversation. For example, the analysis unit can analyze changes in gestures and estimate the other party's intentions. In this way, the flow of the conversation can be predicted by analyzing the frequency and patterns of gestures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input gesture data into a generating AI and have the generating AI perform the prediction of the flow of the conversation.

[0045] The analysis unit can analyze the tone and speed of the other person's voice and estimate their emotions and level of tension. For example, if the tone of voice is high, the analysis unit can estimate that the person is excited and analyze related information. For example, if the speed of the voice is fast, the analysis unit can estimate that the person is tense and analyze information to help them relax. For example, if the tone of voice is low, the analysis unit can estimate that the person is calm and analyze detailed information. In this way, by analyzing the tone and speed of the voice, the emotions and level of tension of the other person can be estimated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input voice tone and speed data into a generating AI and have the generating AI perform the estimation of emotions and level of tension.

[0046] The proposal unit can adjust the level of detail of its proposals based on the importance of the other party's statement. For example, if the other party's statement is important, the proposal unit will make a detailed proposal. For example, if the other party's statement is general, the proposal unit may make a concise proposal. For example, if the other party's statement is urgent, the proposal unit may make a quick proposal. By adjusting the level of detail of the proposal according to the importance of the statement, it becomes possible to make more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input statement importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0047] The suggestion unit can propose the most appropriate statement by considering the recipient's cultural background and linguistic characteristics. For example, the suggestion unit can propose an appropriate statement by considering the recipient's cultural background. The suggestion unit can also propose an appropriate statement by considering the recipient's linguistic characteristics. For example, the suggestion unit can adjust the interpretation of nonverbal information based on the recipient's cultural background. This allows for the proposal of more appropriate statements by considering cultural background and linguistic characteristics. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on cultural background and linguistic characteristics into a generating AI and have the generating AI propose the most appropriate statement.

[0048] The proposal unit can determine the priority of proposals based on the timing of the other party's statements. For example, if the other party's statement is important, the proposal unit will prioritize the proposal. For example, if the other party's statement is general, the proposal unit may postpone the proposal. For example, if the other party's statement is urgent, the proposal unit may make a proposal quickly. This allows for more appropriate proposals by determining the priority of proposals according to the timing of statements. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input statement timing data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0049] The proposed unit can analyze the frequency and patterns of the other party's gestures and predict the flow of the conversation. For example, the proposed unit can analyze the frequency of the other party's gestures and predict the timing of the next statement. For example, the proposed unit can analyze the patterns of the other party's gestures and predict the direction of the conversation. For example, the proposed unit can analyze changes in gestures and estimate the other party's intentions. In this way, the flow of the conversation can be predicted by analyzing the frequency and patterns of gestures. Some or all of the above processing in the proposed unit may be performed using AI, for example, or without AI. For example, the proposed unit can input gesture data into a generating AI and have the generating AI perform the prediction of the flow of the conversation.

[0050] The translation unit can produce a more natural translation by considering the context of the other party's statement. For example, the translation unit can produce a natural translation by considering the context of the other party's statement. The translation unit can also produce a natural translation by supplementing background information on the other party's statement. The translation unit can also produce a natural translation by estimating the intent of the other party's statement. This makes a more natural translation possible by considering the context of the statement. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input contextual data of the statement into a generating AI and have the generating AI perform a natural translation.

[0051] The translation unit can perform appropriate translations by considering the cultural background and linguistic characteristics of the recipient. For example, the translation unit can perform appropriate translations by considering the cultural background of the recipient. The translation unit can also perform appropriate translations by considering the linguistic characteristics of the recipient. For example, the translation unit can adjust the interpretation of nonverbal information based on the cultural background of the recipient. This makes it possible to perform more appropriate translations by considering cultural background and linguistic characteristics. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input data on cultural background and linguistic characteristics into a generating AI and have the generating AI perform an appropriate translation.

[0052] The translation unit can adjust the order of translations based on the timing of the other party's statements. For example, if the other party's statement is important, the translation unit will prioritize translating it. For example, if the other party's statement is general, the translation unit may postpone translating it. For example, if the other party's statement is urgent, the translation unit may translate it quickly. By adjusting the order of translations according to the timing of the statements, more appropriate translations can be achieved. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input statement timing data into a generating AI and have the generating AI perform the adjustment of the translation order.

[0053] The translation unit can analyze the frequency and patterns of the other party's gestures and predict the flow of the conversation. For example, the translation unit can analyze the frequency of the other party's gestures and predict the timing of the next statement. For example, the translation unit can analyze the patterns of the other party's gestures and predict the direction of the conversation. For example, the translation unit can analyze changes in gestures and estimate the other party's intentions. In this way, the flow of the conversation can be predicted by analyzing the frequency and patterns of gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input gesture data into a generating AI and have the generating AI perform the prediction of the flow of the conversation.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] Next-generation communication devices can also include a schedule management unit to manage the user's schedule. This unit can, for example, integrate with the user's calendar app to automatically retrieve appointments. It can also, for example, display reminders when a meeting is about to begin. Furthermore, it can automatically notify users of any changes to their schedules. This allows users to efficiently manage their schedules and ensure they don't miss important appointments.

[0056] Next-generation communication devices can also be equipped with a fitness support unit to assist users with their exercise. This unit can, for example, collect user exercise data and monitor exercise progress. It can also suggest appropriate exercise plans based on the user's exercise goals. Furthermore, it can provide real-time feedback during exercise to help users maintain correct form. This effectively supports user exercise and promotes a healthy lifestyle.

[0057] Next-generation communication devices can also be equipped with a learning support unit to further assist user learning. This unit can, for example, monitor the user's learning progress and suggest an appropriate learning plan. It can also provide problems with adjusted difficulty levels based on the user's understanding. Furthermore, it can provide real-time feedback during user learning to support deeper understanding. This effectively supports user learning and improves learning outcomes.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The collection unit collects the other party's spoken and nonverbal information. For example, the collection unit uses a high-performance microphone to collect the other party's spoken words and noise-canceling technology to obtain clear audio data. The collection unit also uses a camera to collect nonverbal information such as the other party's facial expressions and hand gestures, and extracts important information using facial recognition technology and gesture recognition technology. Step 2: The analysis unit analyzes the information collected by the collection unit to understand the context of the conversation. The analysis unit uses speech analysis technology to analyze the content of what the other person is saying and extracts important keywords. It also uses facial expression analysis technology to analyze the other person's emotions and detect changes in those emotions. Furthermore, it uses gesture analysis technology to analyze the other person's intentions and predict the flow of the conversation. Step 3: The suggestion unit proposes an appropriate response based on the context understood by the analysis unit. The suggestion unit proposes the best response by referring to information on the internet and past conversation history. It can also adjust the expression of the response based on the user's emotions. For example, if the user is nervous, it will use expressions that help them relax, and if the user is excited, it will use expressions that help them calm down. Step 4: The translation department translates the responses proposed by the proposal department into multiple languages. The translation department translates foreign languages ​​in real time and adjusts the expression of the translation based on the user's sentiment. It also takes into account the cultural background and linguistic characteristics of the recipient to ensure an appropriate translation.

[0060] (Example of form 2) The next-generation communication device according to an embodiment of the present invention is an AI-equipped earphone with a high-performance microphone and camera. This earphone has the function of understanding the context of a conversation and suggesting an appropriate response. It can also analyze the other party's nonverbal information (such as facial expressions and hand gestures) and suggest conversational responses. Furthermore, it is equipped with a multilingual translation function and is a next-generation communication device that supports participation in meetings. For example, this earphone operates in the following steps: First, it uses a high-performance microphone and camera to collect the other party's statements and nonverbal information. Next, the AI ​​analyzes the collected information and understands the context of the conversation. For example, if the other party is speaking with a smile, it can suggest a positive response based on their facial expression. It can also analyze the other party's hand gestures and gestures and suggest appropriate conversational responses. Furthermore, this earphone is equipped with a multilingual translation function and can translate foreign languages ​​in real time. For example, if spoken to in English, it can be translated into Japanese and conveyed to the user. This allows even users who are not proficient in foreign languages ​​to communicate smoothly. These earphones target business professionals who lack confidence in meetings and conversations with clients, addressing challenges such as insufficient knowledge and experience, difficulty with real-time communication, poor nonverbal communication skills, and foreign language proficiency. Specifically, they support real-time communication by understanding what the other person is saying and the context of the meeting, and suggesting appropriate responses. They can also analyze nonverbal communication (facial expressions, gestures) and suggest the most appropriate responses. Furthermore, they can suggest the most appropriate responses based on information from the internet. In this way, AI-powered earphones equipped with high-performance microphones and cameras can improve business professionals' communication skills as a next-generation communication device that supports participation in meetings. Thus, next-generation communication devices can improve the communication skills of business professionals.

[0061] The next-generation communication device according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a translation unit. The collection unit collects the other party's speech and nonverbal information. For example, the collection unit collects the other party's speech using a high-performance microphone. The collection unit can also collect nonverbal information such as the other party's facial expressions and hand gestures using a camera. Furthermore, the collection unit can analyze the other party's gestures in real time and extract important information. For example, the collection unit collects the other party's speech using a high-performance microphone and obtains clear audio data using noise-canceling technology. The collection unit can also capture the other party's facial expressions with a camera and analyze emotions using facial expression recognition technology. Furthermore, the collection unit can track the other party's gestures with a camera and extract important information using gesture recognition technology. The analysis unit analyzes the information collected by the collection unit and understands the context of the conversation. For example, the analysis unit analyzes the content of the other party's speech using speech analysis technology. The analysis unit can also analyze the other party's emotions using facial expression analysis technology. Furthermore, the analysis unit can analyze the other party's intentions using gesture analysis technology. For example, the analysis unit uses speech analysis technology to analyze the content of what the other party says and extract important keywords. The analysis unit can also use facial expression analysis technology to analyze the other party's emotions and detect changes in those emotions. Furthermore, the analysis unit can use gesture analysis technology to analyze the other party's intentions and predict the flow of the conversation. The suggestion unit proposes an appropriate response based on the context understood by the analysis unit. For example, the suggestion unit may refer to information on the internet to propose the best response. It can also refer to past conversation history to propose an appropriate response. Furthermore, the suggestion unit can adjust the expression of the response based on the user's emotions. For example, it may refer to news articles on the internet to propose a response based on the latest information. It can also refer to past conversation history to propose a response tailored to the other party's preferences. Furthermore, the suggestion unit can use expressions to help the user relax if they are nervous, and expressions to help them calm down if they are excited. The translation unit translates the responses proposed by the suggestion unit into multiple languages.The translation unit can, for example, translate foreign languages ​​in real time. It can also adjust the translation's expression based on the user's emotions. Furthermore, it can provide appropriate translations considering the recipient's cultural background and linguistic characteristics. For example, the translation unit can translate English into Japanese in real time. It can also use expressions to help the user relax if they are nervous, and expressions to help them calm down if they are excited. Furthermore, it can provide appropriate translations considering the recipient's cultural background and linguistic characteristics. This allows next-generation communication devices to support next-generation communication by collecting and analyzing the recipient's spoken and nonverbal information, suggesting appropriate responses, and translating them into multiple languages.

[0062] The data collection unit collects both spoken and nonverbal information from the other party. For example, the collection unit collects the other party's speech using a high-performance microphone. The collection unit can also collect nonverbal information such as the other party's facial expressions and hand gestures using a camera. Furthermore, the collection unit can analyze the other party's gestures in real time and extract important information. Specifically, the collection unit utilizes noise cancellation technology to clearly collect the other party's speech using a high-performance microphone. This technology effectively removes ambient noise, allowing for accurate acquisition of the content of the speech. It also uses a camera to capture the other party's facial expressions and analyzes their emotions using facial recognition technology. For example, it can detect expressions such as smiles, anger, and surprise in real time, allowing for an understanding of the other party's emotional state. In addition, the camera tracks the other party's gestures, and gesture recognition technology is used to extract important information from hand gestures and body movements. This makes it possible to understand the other party's intentions and emotions more deeply. The collection unit centrally manages this data and transmits it to the analysis unit in real time. The frequency and accuracy of data collection can be adjusted according to the situation, allowing for flexible responses under specific conditions. For example, data can be collected frequently and analyzed in detail during particularly important situations such as meetings or presentations. This allows the data collection unit to gather a wide range of data from various devices and understand the situation in real time.

[0063] The analysis unit analyzes the information collected by the collection unit to understand the context of the conversation. For example, the analysis unit uses speech analysis technology to analyze the content of what the other person says. The analysis unit can also analyze the other person's emotions using facial expression analysis technology. Furthermore, the analysis unit can analyze the other person's intentions using gesture analysis technology. Specifically, it uses speech analysis technology to analyze the content of what the other person says and extract important keywords. This allows for the understanding of the topic and important points of the conversation. It can also use facial expression analysis technology to analyze the other person's emotions and detect changes in emotion. For example, a smile can be detected as a positive emotion, and a frown as a negative emotion. Furthermore, gesture analysis technology can be used to analyze the other person's intentions and predict the flow of the conversation. For example, a raised hand can be interpreted as an indication of the speaker's intention, and crossed arms can be interpreted as a defensive attitude. The analysis unit integrates this information to gain a deep understanding of the context of the conversation. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term trend analysis and risk assessment. For example, by analyzing conversation patterns with specific individuals based on past conversation history, the system can formulate future countermeasures. Furthermore, anomaly detection algorithms can be used to detect unusual patterns or abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0064] The suggestion unit proposes appropriate responses based on the context understood by the analysis unit. For example, the suggestion unit can suggest the best response by referring to information on the internet. It can also suggest appropriate responses by referring to past conversation history. Furthermore, the suggestion unit can adjust the expression of responses based on the user's emotions. Specifically, the suggestion unit refers to news articles and expert information sources on the internet to suggest responses based on the latest information. This ensures that users can always provide appropriate responses based on the latest information. The suggestion unit can also suggest responses tailored to the other party's preferences and interests by referring to past conversation history. For example, it can provide relevant information based on topics and questions the other party has shown interest in in the past. Furthermore, the suggestion unit can adjust the expression of responses according to the user's emotional state. For example, it can use expressions to help the user relax if they are nervous, and expressions to help them calm down if they are excited. The suggestion unit integrates this information and proposes the best response in real time. Additionally, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can record how the user reacted to the suggested responses and reflect this in future suggestions. This allows the proposal department to consistently provide users with the best possible responses and improve the quality of communication.

[0065] The translation department translates the responses proposed by the proposal department into multiple languages. For example, the translation department translates foreign languages ​​in real time. It can also adjust the translation's expression based on the user's emotions. Furthermore, the translation department can provide appropriate translations considering the recipient's cultural background and linguistic characteristics. Specifically, the translation department utilizes machine translation technology to translate English into Japanese in real time. This technology enables rapid and accurate translation. The translation department can also adjust the translation's expression according to the user's emotional state. For example, it can use expressions to help the user relax if they are nervous, and expressions to help them calm down if they are excited. Furthermore, the translation department can provide appropriate translations considering the recipient's cultural background and linguistic characteristics. For example, if the recipient is from a specific cultural background, using expressions and phrases specific to that culture can lead to more natural communication. The translation department integrates this information to provide the optimal translation in real time. Additionally, the translation department can collect user feedback and continuously improve the accuracy and effectiveness of the translations. For example, it's possible to record how users reacted to the translated content and incorporate that feedback into future translations. This allows the translation team to consistently provide users with the best possible translations and improve the quality of communication.

[0066] The collection unit can collect the other party's speech and nonverbal information using a high-performance microphone and camera. For example, the collection unit can collect the other party's speech using a high-performance microphone. The collection unit can also collect nonverbal information such as the other party's facial expressions and hand gestures using a camera. For example, the collection unit can collect the other party's speech with a high-performance microphone and obtain clear audio data using noise cancellation technology. For example, the collection unit can also photograph the other party's facial expressions with a camera and analyze their emotions using facial recognition technology. For example, the collection unit can track the other party's gestures with a camera and extract important information using gesture recognition technology. In this way, by using a high-performance microphone and camera, the other party's speech and nonverbal information can be collected with high accuracy. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the audio data collected by the high-performance microphone into a generating AI and have the generating AI perform the analysis of the audio data.

[0067] The analysis unit can analyze nonverbal information such as the other person's facial expressions and hand gestures. For example, the analysis unit can analyze the other person's facial expressions using facial expression analysis technology. The analysis unit can also analyze the other person's hand gestures using gesture analysis technology. For example, the analysis unit can analyze the other person's facial expressions using facial expression analysis technology and detect changes in emotion. For example, the analysis unit can analyze the other person's hand gestures using gesture analysis technology and estimate their intentions. This allows for a deeper understanding of the conversation context by analyzing the other person's nonverbal information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input facial expression data into a generating AI and have the generating AI perform emotion analysis.

[0068] The suggestion unit can propose the most appropriate statement from information on the internet. For example, the suggestion unit can propose a statement based on the latest information by referring to news articles on the internet. The suggestion unit can also propose a statement based on relevant information by referring to databases on the internet. For example, the suggestion unit can propose a statement based on the latest information by referring to news articles on the internet. The suggestion unit can also propose a statement based on relevant information by referring to databases on the internet. This makes it possible to propose more appropriate statements by utilizing information on the internet. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information from the internet into a generation AI and have the generation AI execute the proposal of the most appropriate statement.

[0069] The translation unit can translate foreign languages ​​in real time. For example, the translation unit can translate English to Japanese in real time. For example, the translation unit can also translate Chinese to English in real time. For example, the translation unit can translate English to Japanese in real time. For example, the translation unit can also translate Chinese to English in real time. This enables communication that transcends language barriers through real-time translation. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input foreign language audio data into a generating AI and have the generating AI perform real-time translation.

[0070] The data collection unit can estimate the user's emotions and adjust the type of information it collects based on the estimated emotions. For example, if the user is tense, the data collection unit will prioritize collecting information to help them relax. For example, if the user is excited, the data collection unit can also collect information to help them calm down. For example, if the user is tired, the data collection unit can also collect concise and important information. By adjusting the type of information collected according to the user's emotions, more appropriate information can be collected. 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the type of information to collect.

[0071] The collection unit can filter ambient sounds to remove noise and collect clearer audio information. For example, the collection unit can analyze ambient noise in real time and perform noise cancellation. The collection unit can also, for example, emphasize sounds in specific frequency bands to clarify important statements. The collection unit can also, for example, reduce background noise to collect conversational audio clearly. This allows for the collection of clear audio information by removing noise. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input ambient sound data into a generating AI and have the generating AI perform noise reduction.

[0072] The data collection unit can track the movement of the subject's gaze and prioritize the collection of important information based on the direction of their gaze. For example, the data collection unit can prioritize the collection of information about objects or people the subject is focusing on. For example, the data collection unit can also collect information about the direction the subject's gaze frequently points. For example, the data collection unit can estimate topics of interest from the movement of the subject's gaze and collect that information. In this way, by tracking the movement of the subject's gaze, important information can be prioritized for collection. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input gaze data into a generating AI and have the generating AI perform the collection of important information.

[0073] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is tense, the data collection unit will prioritize collecting information to help them relax. For example, if the user is excited, the data collection unit can also collect information to help them calm down. For example, if the user is tired, the data collection unit can also collect concise and important information. This allows for the collection of more appropriate information by prioritizing information 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 processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0074] The data collection unit can collect additional information from the other person's clothing and accessories to complement the conversation context. For example, the data collection unit can collect information about the other person's occupation and hobbies from their clothing. For example, the data collection unit can estimate the other person's preferences and interests from their accessories and collect related information. For example, the data collection unit can estimate the other person's social background from the brands of their clothing and accessories and complement the conversation context. In this way, the conversation context can be complemented by collecting information from clothing and accessories. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input clothing and accessory data into a generating AI and have the generating AI perform the task of complementing the conversation context.

[0075] The data collection unit can analyze the tone and speed of the other person's voice and estimate their emotions and level of tension. For example, if the voice tone is high, the data collection unit can estimate that the person is excited and collect related information. For example, if the voice speed is fast, the data collection unit can estimate that the person is tense and collect information to help them relax. For example, if the voice tone is low, the data collection unit can estimate that the person is calm and collect detailed information. In this way, by analyzing the tone and speed of the voice, the emotions and level of tension of the other person can be estimated. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input voice tone and speed data into a generating AI and have the generating AI perform the estimation of emotions and tension levels.

[0076] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is tense, the analysis unit may use an analysis algorithm to help them relax. For example, if the user is excited, the analysis unit may use an analysis algorithm to help them calm down. For example, if the user is tired, the analysis unit may use an analysis algorithm that prioritizes concise and important information. By adjusting the analysis algorithm according to the user's emotions, more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0077] The analysis unit can perform a more accurate contextual understanding by considering the context of the other party's statement and comparing it with past conversation history. For example, the analysis unit can estimate the intent of the other party's statement from past conversation history and compare it with the current statement. The analysis unit can also supplement background information of the other party's statement based on past conversation history. For example, the analysis unit can analyze past conversation history and understand patterns in the other party's statements. This enables a more accurate contextual understanding by comparing it with past conversation history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past conversation history data into a generating AI and have the generating AI perform contextual understanding.

[0078] The analysis unit can interpret nonverbal information by considering the other party's cultural background and linguistic characteristics. For example, the analysis unit can accurately interpret the meaning of gestures by considering the other party's cultural background. The analysis unit can also accurately interpret the meaning of facial expressions by considering the other party's linguistic characteristics. The analysis unit can also adjust the interpretation of nonverbal information based on the other party's cultural background. This makes the interpretation of nonverbal information more accurate by considering cultural background and linguistic characteristics. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on cultural background and linguistic characteristics into a generating AI and have the generating AI perform the interpretation of nonverbal information.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0080] The analysis unit can analyze the frequency and patterns of the other party's gestures and predict the flow of the conversation. For example, the analysis unit can analyze the frequency of the other party's gestures and predict the timing of the next statement. For example, the analysis unit can also analyze the patterns of the other party's gestures and predict the direction of the conversation. For example, the analysis unit can analyze changes in gestures and estimate the other party's intentions. In this way, the flow of the conversation can be predicted by analyzing the frequency and patterns of gestures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input gesture data into a generating AI and have the generating AI perform the prediction of the flow of the conversation.

[0081] The analysis unit can analyze the tone and speed of the other person's voice and estimate their emotions and level of tension. For example, if the tone of voice is high, the analysis unit can estimate that the person is excited and analyze related information. For example, if the speed of the voice is fast, the analysis unit can estimate that the person is tense and analyze information to help them relax. For example, if the tone of voice is low, the analysis unit can estimate that the person is calm and analyze detailed information. In this way, by analyzing the tone and speed of the voice, the emotions and level of tension of the other person can be estimated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input voice tone and speed data into a generating AI and have the generating AI perform the estimation of emotions and level of tension.

[0082] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on those emotions. For example, if the user is tense, the suggestion unit may use a relaxing approach. If the user is excited, the suggestion unit may also use a calming approach. If the user is tired, the suggestion unit may also use a concise approach that prioritizes important information. By adjusting the presentation of suggestions according to the user's emotions, more appropriate suggestions can be provided. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation.

[0083] The proposal unit can adjust the level of detail of its proposals based on the importance of the other party's statement. For example, if the other party's statement is important, the proposal unit will make a detailed proposal. For example, if the other party's statement is general, the proposal unit may make a concise proposal. For example, if the other party's statement is urgent, the proposal unit may make a quick proposal. By adjusting the level of detail of the proposal according to the importance of the statement, it becomes possible to make more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input statement importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0084] The suggestion unit can propose the most appropriate statement by considering the recipient's cultural background and linguistic characteristics. For example, the suggestion unit can propose an appropriate statement by considering the recipient's cultural background. The suggestion unit can also propose an appropriate statement by considering the recipient's linguistic characteristics. For example, the suggestion unit can adjust the interpretation of nonverbal information based on the recipient's cultural background. This allows for the proposal of more appropriate statements by considering cultural background and linguistic characteristics. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on cultural background and linguistic characteristics into a generating AI and have the generating AI propose the most appropriate statement.

[0085] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit may provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit may provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be provided. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0086] The proposal unit can determine the priority of proposals based on the timing of the other party's statements. For example, if the other party's statement is important, the proposal unit will prioritize the proposal. For example, if the other party's statement is general, the proposal unit may postpone the proposal. For example, if the other party's statement is urgent, the proposal unit may make a proposal quickly. This allows for more appropriate proposals by determining the priority of proposals according to the timing of statements. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input statement timing data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0087] The proposed unit can analyze the frequency and patterns of the other party's gestures and predict the flow of the conversation. For example, the proposed unit can analyze the frequency of the other party's gestures and predict the timing of the next statement. For example, the proposed unit can analyze the patterns of the other party's gestures and predict the direction of the conversation. For example, the proposed unit can analyze changes in gestures and estimate the other party's intentions. In this way, the flow of the conversation can be predicted by analyzing the frequency and patterns of gestures. Some or all of the above processing in the proposed unit may be performed using AI, for example, or without AI. For example, the proposed unit can input gesture data into a generating AI and have the generating AI perform the prediction of the flow of the conversation.

[0088] The translation unit can estimate the user's emotions and adjust the translation's expression based on the estimated emotions. For example, if the user is tense, the translation unit may use expressions that promote relaxation. For example, if the user is excited, the translation unit may use expressions that promote calmness. For example, if the user is tired, the translation unit may use expressions that prioritize concise and important information. By adjusting the translation's expression according to the user's emotions, a more appropriate translation becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input user emotion data into a generative AI and have the generative AI adjust the translation's expression.

[0089] The translation unit can produce a more natural translation by considering the context of the other party's statement. For example, the translation unit can produce a natural translation by considering the context of the other party's statement. The translation unit can also produce a natural translation by supplementing background information on the other party's statement. The translation unit can also produce a natural translation by estimating the intent of the other party's statement. This makes a more natural translation possible by considering the context of the statement. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input contextual data of the statement into a generating AI and have the generating AI perform a natural translation.

[0090] The translation unit can perform appropriate translations by considering the cultural background and linguistic characteristics of the recipient. For example, the translation unit can perform appropriate translations by considering the cultural background of the recipient. The translation unit can also perform appropriate translations by considering the linguistic characteristics of the recipient. For example, the translation unit can adjust the interpretation of nonverbal information based on the cultural background of the recipient. This makes it possible to perform more appropriate translations by considering cultural background and linguistic characteristics. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input data on cultural background and linguistic characteristics into a generating AI and have the generating AI perform an appropriate translation.

[0091] The translation unit can estimate the user's emotions and determine translation priorities based on the estimated emotions. For example, if the user is tense, the translation unit may prioritize translations that help them relax. If the user is excited, the translation unit may also prioritize translations that help them calm down. If the user is tired, the translation unit may also prioritize translating concise and important information. This allows for more appropriate translations by prioritizing translations 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 translation unit may be performed using AI or not. For example, the translation unit can input user emotion data into a generative AI and have the generative AI determine the translation priorities.

[0092] The translation unit can adjust the order of translations based on the timing of the other party's statements. For example, if the other party's statement is important, the translation unit will prioritize translating it. For example, if the other party's statement is general, the translation unit may postpone translating it. For example, if the other party's statement is urgent, the translation unit may translate it quickly. By adjusting the order of translations according to the timing of the statements, more appropriate translations can be achieved. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input statement timing data into a generating AI and have the generating AI perform the adjustment of the translation order.

[0093] The translation unit can analyze the frequency and patterns of the other party's gestures and predict the flow of the conversation. For example, the translation unit can analyze the frequency of the other party's gestures and predict the timing of the next statement. For example, the translation unit can analyze the patterns of the other party's gestures and predict the direction of the conversation. For example, the translation unit can analyze changes in gestures and estimate the other party's intentions. In this way, the flow of the conversation can be predicted by analyzing the frequency and patterns of gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input gesture data into a generating AI and have the generating AI perform the prediction of the flow of the conversation.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] Next-generation communication devices can also be equipped with a health monitoring unit that monitors the user's health status. This unit can, for example, measure the user's heart rate and blood pressure, monitoring their health in real time. It can also measure the user's stress level and, if stress levels are high, suggest ways to relax. Furthermore, it can monitor the user's sleep patterns and, if sleep deprivation is occurring, suggest ways to encourage rest. This allows for monitoring the user's health status and providing appropriate suggestions, thereby supporting the user's well-being.

[0096] Next-generation communication devices can also include a schedule management unit to manage the user's schedule. This unit can, for example, integrate with the user's calendar app to automatically retrieve appointments. It can also, for example, display reminders when a meeting is about to begin. Furthermore, it can automatically notify users of any changes to their schedules. This allows users to efficiently manage their schedules and ensure they don't miss important appointments.

[0097] Next-generation communication devices can also be equipped with a music recommendation unit that suggests music based on user preferences. For example, this unit could analyze the user's past playback history and recommend music that matches their preferences. It could also recommend relaxing music based on the user's current emotional state, or energetic music to match the user's activity level. This would allow for the provision of music tailored to the user's preferences and emotional state, creating a more comfortable environment.

[0098] Next-generation communication devices can also be equipped with a fitness support unit to assist users with their exercise. This unit can, for example, collect user exercise data and monitor exercise progress. It can also suggest appropriate exercise plans based on the user's exercise goals. Furthermore, it can provide real-time feedback during exercise to help users maintain correct form. This effectively supports user exercise and promotes a healthy lifestyle.

[0099] Next-generation communication devices can also be equipped with a learning support unit to further assist user learning. This unit can, for example, monitor the user's learning progress and suggest an appropriate learning plan. It can also provide problems with adjusted difficulty levels based on the user's understanding. Furthermore, it can provide real-time feedback during user learning to support deeper understanding. This effectively supports user learning and improves learning outcomes.

[0100] Next-generation communication devices can also include a reminder unit that estimates the user's emotions and provides appropriate reminders based on those emotions. For example, if the user is feeling stressed, the reminder unit might suggest taking a break to relax. If the user is tired, the reminder unit might also provide a reminder to go to bed earlier. If the user is excited, the reminder unit might suggest taking deep breaths to calm down. This allows for the provision of reminders tailored to the user's emotions, supporting a healthy lifestyle.

[0101] Next-generation communication devices can also be equipped with a news delivery unit that estimates the user's emotions and provides appropriate news based on those emotions. For example, if the user is relaxed, the news delivery unit can provide interesting news. If the user is stressed, the news delivery unit can also provide positive news. If the user is excited, the news delivery unit can also provide news to help them calm down. This allows for the provision of news tailored to the user's emotions and supports information gathering.

[0102] Next-generation communication devices can also include an exercise suggestion unit that estimates the user's emotions and suggests appropriate exercises based on those emotions. For example, if the user is feeling stressed, the exercise suggestion unit might suggest yoga to relax. If the user is feeling tired, it might suggest light stretching. If the user is feeling excited, it might suggest running to release energy. This allows for exercise suggestions tailored to the user's emotions, supporting a healthy lifestyle.

[0103] Next-generation communication devices can also be equipped with a meal suggestion unit that estimates the user's emotions and suggests appropriate meals based on those emotions. For example, if the user is feeling stressed, the meal suggestion unit might suggest a relaxing herbal tea. If the user is feeling tired, it might suggest a nutritionally balanced meal to replenish energy. If the user is feeling excited, it might suggest a light snack to calm down. This allows for meal suggestions tailored to the user's emotions and supports a healthy eating lifestyle.

[0104] Next-generation communication devices can also be equipped with a relaxation suggestion unit that estimates the user's emotions and proposes appropriate relaxation methods based on those emotions. For example, if the user is feeling stressed, the relaxation suggestion unit may suggest meditation. If the user is feeling tired, the relaxation suggestion unit may also suggest aromatherapy. If the user is feeling agitated, the relaxation suggestion unit may also suggest deep breathing. This allows for the suggestion of relaxation methods tailored to the user's emotions, supporting mental and physical refreshment.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The collection unit collects the other party's spoken and nonverbal information. For example, the collection unit uses a high-performance microphone to collect the other party's spoken words and noise-canceling technology to obtain clear audio data. The collection unit also uses a camera to collect nonverbal information such as the other party's facial expressions and hand gestures, and extracts important information using facial recognition technology and gesture recognition technology. Step 2: The analysis unit analyzes the information collected by the collection unit to understand the context of the conversation. The analysis unit uses speech analysis technology to analyze the content of what the other person is saying and extracts important keywords. It also uses facial expression analysis technology to analyze the other person's emotions and detect changes in those emotions. Furthermore, it uses gesture analysis technology to analyze the other person's intentions and predict the flow of the conversation. Step 3: The suggestion unit proposes an appropriate response based on the context understood by the analysis unit. The suggestion unit proposes the best response by referring to information on the internet and past conversation history. It can also adjust the expression of the response based on the user's emotions. For example, if the user is nervous, it will use expressions that help them relax, and if the user is excited, it will use expressions that help them calm down. Step 4: The translation department translates the responses proposed by the proposal department into multiple languages. The translation department translates foreign languages ​​in real time and adjusts the expression of the translation based on the user's sentiment. It also takes into account the cultural background and linguistic characteristics of the recipient to ensure an appropriate translation.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and translation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the high-performance microphone and camera of the smart device 14 to collect the other party's spoken and nonverbal information. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected information and understand the context of the conversation. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose an appropriate response based on the analysis results. The translation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to translate the proposed response into multiple languages. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and translation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the high-performance microphone and camera of the smart glasses 214 to collect the other party's spoken and nonverbal information. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected information and understand the context of the conversation. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose an appropriate response based on the analysis results. The translation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to translate the proposed response into multiple languages. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and translation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the high-performance microphone and camera of the headset terminal 314 to collect the other party's spoken and nonverbal information. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected information and understand the context of the conversation. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose an appropriate response based on the analysis results. The translation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to translate the proposed response into multiple languages. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[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 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.

[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 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).

[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] 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.

[0151] 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.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In 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.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and translation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the robot 414's high-performance microphone and camera to collect the other party's spoken and nonverbal information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to understand the context of the conversation. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes an appropriate response based on the analysis results. The translation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and translates the proposed response into multiple languages. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A collection unit that collects the other party's statements and nonverbal information, An analysis unit analyzes the information collected by the aforementioned collection unit and understands the context of the conversation, A proposal unit proposes an appropriate response based on the context understood by the analysis unit, The system includes a translation unit that translates the response proposed by the proposal unit into multiple languages. A system characterized by the following features. (Note 2) The aforementioned collection unit is High-performance microphones and cameras are used to collect information about what the other person says and what they don't say. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze nonverbal information such as the other person's facial expressions and hand gestures. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We suggest the most appropriate statements based on information from the internet. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned translation department, Translate foreign languages ​​in real time The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is By filtering ambient noise and removing it, clearer audio information is collected. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It tracks the opponent's eye movements and prioritizes the collection of important information based on the direction of their gaze. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Gather additional information from the other person's clothing and accessories to complement the context of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It analyzes the tone and speed of the other person's voice to estimate their emotions and level of tension. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, By considering the context of the other person's statement and comparing it with past conversation history, a more accurate understanding of the context can be achieved. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Interpreting nonverbal information takes into account the other person's cultural background and linguistic characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, By analyzing the frequency and patterns of the other person's gestures, we can predict the flow of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It analyzes the tone and speed of the other person's voice to estimate their emotions and level of tension. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, Adjust the level of detail in your proposal based on the importance of what the other party said. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, Considering the other person's cultural background and linguistic characteristics, propose the most appropriate statement. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Prioritize proposals based on the timing of the other party's statements. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, By analyzing the frequency and patterns of the other person's gestures, we can predict the flow of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned translation department, To create a more natural translation, take into account the context of what the other person said. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned translation department, Perform appropriate translations by taking into account the recipient's cultural background and linguistic characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned translation department, It estimates the user's emotions and determines translation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned translation department, The order of translations is adjusted based on the timing of the other party's statements. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned translation department, By analyzing the frequency and patterns of the other person's gestures, we can predict the flow of the conversation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 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 collection unit that collects the other party's statements and nonverbal information, An analysis unit analyzes the information collected by the aforementioned collection unit and understands the context of the conversation, A proposal unit proposes an appropriate response based on the context understood by the analysis unit, The system includes a translation unit that translates the response proposed by the proposal unit into multiple languages. A system characterized by the following features.

2. The aforementioned collection unit is High-performance microphones and cameras are used to collect information about what the other person says and what they don't say. The system according to feature 1.

3. The aforementioned analysis unit, Analyze nonverbal information such as the other person's facial expressions and hand gestures. The system according to feature 1.

4. The aforementioned proposal section is, We suggest the most appropriate statements based on information from the internet. The system according to feature 1.

5. The aforementioned translation department, Translate foreign languages ​​in real time The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of information collected based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is By filtering ambient noise and removing it, clearer audio information is collected. The system according to feature 1.

8. The aforementioned collection unit is It tracks the opponent's eye movements and prioritizes the collection of important information based on the direction of their gaze. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is Gather additional information from the other person's clothing and accessories to complement the context of the conversation. The system according to feature 1.

Images