system

Abstract

The system according to this embodiment aims to support people with developmental disabilities in communicating smoothly. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the content of conversations. The analysis unit analyzes the content of conversations collected by the collection unit. The generation unit generates an appropriate response based on the content analyzed by the analysis unit. The provision unit provides the response generated by the generation unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for people with developmental disabilities to communicate smoothly and there is a lack of appropriate support.

[0005] The system according to the embodiment aims to assist people with developmental disabilities in communicating smoothly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the content of a conversation. The analysis unit analyzes the content of the conversation collected by the collection unit. The generation unit generates an appropriate response based on the content analyzed by the analysis unit. The provision unit provides the response generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can support people with developmental disabilities in communicating smoothly. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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 communication support system according to an embodiment of the present invention proposes smart glasses in the form of eyeglasses as a support device for people with developmental disabilities such as ASD and ADHD to communicate smoothly. In the communication support system, the user puts on the smart glasses and starts a conversation. A microphone built into the smart glasses collects the content of the conversation, and a generating AI analyzes the content. The generating AI understands the context of the conversation and what the other person says, and generates examples of appropriate responses and topics. The generated responses and topics are displayed on the smart glasses' display or provided to the user by voice. This allows the user to give appropriate responses in real time and supports the smooth progress of communication. For example, if a user is chatting with a friend, the smart glasses collect the content of the conversation. The collected content of the conversation is sent to the generating AI and analyzed. The generating AI understands the context of the conversation and what the other person says, and generates examples of appropriate responses and topics. For example, if the other person asks, "How have you been lately?", the generating AI generates examples of responses such as "Talk about recent events" or "Talk about your hobbies". The generated responses and topics are displayed on the smart glasses' display or provided to the user by voice. For example, the display might show "Tell us about recent events," or the user might receive a voice prompt saying, "Try telling us about recent events." This allows the user to provide appropriate responses in real time. Furthermore, the generative AI learns the content of the user's communication and provides feedback to reduce reliance on the device. For example, if the user thinks for themselves and gives a response, the AI ​​learns from that and incorporates it into future advice. This allows the user to gradually communicate without relying on the device. This mechanism allows people with developmental disabilities such as ASD and ADHD to experience smoother communication and build up successful experiences. Ultimately, the ideal is to make communication easier without relying on the device. In this way, the communication support system enables people with developmental disabilities such as ASD and ADHD to communicate smoothly.

[0029] The communication support system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the content of conversations. The collection unit collects the content of conversations using, for example, a microphone built into smart glasses. The collection unit can collect the content of conversations in real time. The analysis unit analyzes the content of conversations collected by the collection unit. The analysis unit uses a generation AI to understand the context of the conversation and the content of what the other person says. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the content of the conversation. The generation AI understands the context of the conversation and provides information for generating an appropriate response. The generation unit generates an appropriate response based on the content analyzed by the analysis unit. The generation unit uses the generation AI to generate examples of appropriate responses and topics. The generation AI, for example, understands the context of the conversation and what the other person says and generates an appropriate response. The generation unit uses the generation AI to generate examples of responses, for example, when the other person asks "How have you been lately?", such as "Talk about recent events" or "Talk about your hobbies." The provision unit provides the responses generated by the generation unit to the user. The providing unit displays the generated responses and topics on the smart glasses' display. The providing unit can also provide the generated responses and topics to the user via voice. For example, the providing unit may display "Talk about recent events" on the display or provide voice advice such as "Try talking about recent events." In this way, the communication support system according to the embodiment can facilitate communication by collecting and analyzing the content of the conversation, generating appropriate responses, and providing them to the user.

[0030] The data collection unit collects the content of conversations. For example, the data collection unit uses a microphone built into smart glasses to collect the content of conversations. Specifically, the microphone in the smart glasses captures ambient sound with high precision and removes unwanted background noise using noise cancellation technology. This allows for clear collection of the content of conversations. The data collection unit can collect the content of conversations in real time. The collected audio data is immediately converted into a digital signal and transmitted to a central database. Furthermore, the data collection unit adds a timestamp to the audio data and accurately records the flow of the conversation. This makes it easier for the subsequent analysis unit to understand the context. The data collection unit can also use multiple microphones and sound source localization technology to pinpoint the location of speakers. This makes it possible to accurately understand who is saying what, even when there are multiple speakers. By utilizing these functions, the data collection unit can collect the content of conversations with high precision and in real time, improving the overall performance of the system.

[0031] The analysis unit analyzes the content of conversations collected by the collection unit. The analysis unit uses generative AI to understand the context of the conversation and the content of what the other person is saying. Specifically, the generative AI uses natural language processing technology to convert the audio data into text and then analyzes that text. The generative AI analyzes the content of conversations using, for example, text generation AI (e.g., LLM). LLM has learned from a large amount of text data and has a high ability to understand context. The analysis unit uses LLM to understand the context of the conversation and provides information to generate appropriate responses. Specifically, LLM extracts keywords and phrases used in the conversation and analyzes their relationships. Furthermore, the analysis unit can also estimate the emotional state of the speaker using sentiment analysis technology. This allows it to understand whether the speaker is happy, sad, or angry and provides information to generate appropriate responses accordingly. By combining these technologies, the analysis unit can analyze the content of conversations from multiple angles and provide highly accurate information to the generation unit.

[0032] The generation unit generates appropriate responses based on the analysis performed by the analysis unit. The generation unit uses a generation AI to generate examples of appropriate responses and topics. Specifically, the generation AI understands the context of the conversation and what the other person has said, and generates appropriate responses. For example, if the other person asks, "How have you been lately?", the generation AI will generate example responses such as "Talk about recent events" or "Talk about your hobbies." The generation AI can also refer to past conversation data and user profile information to generate more personalized responses. For example, if it knows that the user has recently traveled, it will generate a specific response such as "Talk about your recent trip." The generation unit also has an algorithm that generates multiple response candidates and selects the most appropriate one. This allows the generation unit to always provide the user with the best possible response. Furthermore, the generation unit has a function to evaluate the naturalness and consistency of the generated responses and make corrections as needed. This allows the generation unit to provide users with high-quality responses and facilitate smooth communication.

[0033] The provider unit delivers responses generated by the generator unit to the user. The provider unit displays the generated responses and topics on the smart glasses' display. Specifically, the smart glasses' display is designed to blend naturally into the user's field of vision, providing the generated responses and topics visually. The provider unit can also provide the generated responses and topics to the user via voice. Voice output is performed using speakers built into the smart glasses or bone conduction technology, providing information to the user in clear voice. For example, the provider unit may display "Talk about recent events" on the display or advise "Try talking about recent events" via voice. Furthermore, the provider unit also has the function of collecting user feedback and providing feedback to the generator and analysis units. This allows for continuous improvement of the overall system accuracy and user experience. For example, it can evaluate whether the user is satisfied with the provided responses and adjust the generation algorithm based on the results. In addition, the provider unit can flexibly change the way information is delivered depending on the user's situation and environment. For example, it may prioritize voice output in noisy environments and display in quiet environments. This allows the service provider to deliver information to users in the most optimal way and facilitate smooth communication.

[0034] The collection unit can collect the content of conversations using a microphone built into the smart glasses. The collection unit can, for example, collect the content of conversations in real time using a microphone built into the smart glasses. The collection unit can also remove ambient noise using noise cancellation technology when collecting the content of conversations. The collection unit can, for example, use noise cancellation technology to collect the content of conversations clearly. The collection unit can also convert the audio data into text data using speech recognition technology when collecting the content of conversations. The collection unit can, for example, use speech recognition technology to convert the content of conversations into text data. This allows the collection unit to collect the content of conversations in real time using a microphone built into the smart glasses. 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 using the microphone built into the smart glasses into a generating AI and have the generating AI perform the conversion from audio data to text data.

[0035] The analysis unit can understand the context of a conversation and the content of the other party's statements using a generation AI. For example, the analysis unit uses a generation AI to understand the context of the conversation and analyze the content of the other party's statements. The generation AI analyzes the content of the conversation using a text generation AI (e.g., LLM). The generation AI understands the context of the conversation and provides information to generate an appropriate response. The analysis unit uses the generation AI to provide information to generate example responses, for example, when the other party asks "How have you been lately?", such as "Talk about recent events" or "Talk about your hobbies." In this way, the analysis unit can accurately understand the context of the conversation and the content of the other party's statements by using the generation AI. Some or all of the above processing in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit can input the content of the conversation collected by the collection unit into the generation AI and have the generation AI perform the analysis of the context of the conversation and the content of the other party's statements.

[0036] The generation unit can generate examples of appropriate responses and topics using a generation AI. For example, the generation unit generates examples of appropriate responses and topics using a generation AI. The generation AI analyzes the content of a conversation using a text generation AI (e.g., LLM) and generates appropriate responses. The generation AI understands the context of the conversation and provides information to generate appropriate responses. Using the generation AI, the generation unit generates examples of responses such as "Talk about recent events" or "Talk about your hobbies" when the other person asks "How have you been lately?". In this way, the generation unit can quickly generate examples of appropriate responses and topics by using a generation AI. Some or all of the above processing in the generation unit may be performed using an AI, for example, or without an AI. For example, the generation unit can input the content of the conversation analyzed by the analysis unit into the generation AI and have the generation AI perform the generation of examples of appropriate responses and topics.

[0037] The provider unit can display the generated responses and topics on the smart glasses' display. For example, the provider unit displays the generated responses and topics on the smart glasses' display. The provider unit makes it easier for the user to confirm the responses and topics by visually displaying them. For example, the provider unit displays "Talk about recent events" on the display. When displaying the generated responses and topics, the provider unit can also improve visibility by adjusting the font size and color. For example, the provider unit adjusts the font size and color to make it easier for the user to confirm the responses and topics. This allows the user to visually confirm the responses and topics by displaying them on the smart glasses' display. Some or all of the above processing in the provider unit may be performed using AI, for example, or without AI. For example, the provider unit can input the responses and topics generated by the generation unit into a generation AI and have the generation AI perform adjustments for visual display.

[0038] The delivery unit can provide the generated responses and topics to the user in voice. For example, the delivery unit can provide the generated responses and topics to the user in voice. By providing them in voice, the delivery unit can ensure that the user receives the responses and topics even in situations where they cannot visually confirm them. For example, the delivery unit can advise the user in voice, "Tell me about something that's happened recently." When providing in voice, the delivery unit can also adjust the volume and sound quality to make it easier to hear. For example, the delivery unit can adjust the volume and sound quality to make the responses and topics easier for the user to hear. This allows the delivery unit to ensure that the user receives the responses and topics even in situations where they cannot visually confirm them. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the responses and topics generated by the generation unit into a generation AI and have the generation AI perform adjustments for providing them in voice.

[0039] The analysis unit can learn the content of user communication and provide feedback. For example, the analysis unit can learn the content of user communication using a generative AI and provide feedback. The generative AI can analyze the content of user communication using a text generation AI (e.g., LLM) and generate feedback. The generative AI learns the patterns of user communication and provides feedback to reduce device dependency. The analysis unit can use the generative AI to learn the content of responses made by the user, for example, and reflect it in advice for future interactions. In this way, the analysis unit can learn the content of user communication and provide feedback to reduce device dependency. 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 the content of user communication into the generative AI and have the generative AI generate feedback.

[0040] The collection unit can analyze the user's past conversation history and select the optimal collection method when collecting conversations. For example, the collection unit can use AI to analyze the user's past conversation history and select the optimal collection method. For example, the collection unit can prioritize collecting conversation topics that the user has preferred to use in the past. The collection unit can exclude topics that the user has avoided in the past from collection. The collection unit can select a collection method suitable for a specific time period from the user's past conversation history. In this way, the collection unit can select the optimal collection method by analyzing the user's past conversation history. 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 user's past conversation history into a generating AI and have the generating AI select the optimal collection method.

[0041] The collection unit can filter conversations based on the user's current situation and areas of interest. For example, the collection unit can use AI to filter based on the user's current situation and areas of interest. For example, the collection unit can prioritize collecting work-related conversations depending on the user's current situation. The collection unit can filter and collect conversations related to hobbies based on the user's areas of interest. The collection unit can collect relaxing conversations to reduce stress depending on the user's current situation. In this way, the collection unit can collect highly relevant conversations by filtering based on the user's current situation and areas of interest. 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 data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0042] The collection unit can prioritize collecting conversations that are highly relevant by considering the user's geographical location information when collecting conversations. For example, the collection unit can use AI to prioritize collecting conversations that are highly relevant by considering the user's geographical location information. For example, if the user is in a specific region, the collection unit will prioritize collecting topics related to that region. If the user is traveling, the collection unit will prioritize collecting topics related to the travel destination. If the user is participating in a specific event, the collection unit will prioritize collecting topics related to that event. In this way, the collection unit can prioritize collecting conversations that are highly relevant by considering the user's geographical location information. 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 user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant conversations.

[0043] The collection unit can analyze the user's social media activity and collect relevant conversations when collecting conversations. For example, the collection unit can use AI to analyze the user's social media activity and collect relevant conversations. For example, the collection unit can prioritize collecting topics that the user frequently mentions on social media. The collection unit can collect topics that the user's social media friends are interested in. The collection unit can collect relevant conversations based on the content of the user's social media posts. In this way, the collection unit can collect relevant conversations by analyzing the user's social media activity. 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 data on the user's social media activity into a generating AI and have the generating AI perform the collection of relevant conversations.

[0044] The analysis unit can adjust the level of detail of its analysis based on the importance of the conversation during analysis. For example, the analysis unit can use AI to adjust the level of detail of its analysis based on the importance of the conversation. For example, in the case of an important conversation, the analysis unit performs a detailed analysis and generates a specific response. In the case of a general conversation, the analysis unit performs a concise analysis and generates a simple response. In the case of a short conversation, the analysis unit performs a rapid analysis and generates a response immediately. In this way, the analysis unit can provide appropriate analysis results by adjusting the level of detail of its analysis based on the importance of the conversation. 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 conversation importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of its analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the conversation during analysis. For example, the analysis unit can use AI to apply different analysis algorithms depending on the category of the conversation. For example, in the case of business conversations, the analysis unit can apply an analysis algorithm that takes technical terms into account. In the case of casual conversations, the analysis unit can apply an analysis algorithm that takes everyday language into account. In the case of technical conversations, the analysis unit can apply an analysis algorithm that takes technical terms into account. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of the conversation. 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 conversation category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the timing of conversation submission during analysis. For example, the analysis unit can use AI to determine the priority of analysis based on the timing of conversation submission. For example, the analysis unit can prioritize analyzing recent conversations and generate responses immediately. The analysis unit can postpone the analysis of older conversations. The analysis unit can prioritize analyzing conversations that took place during a specific time period. In this way, the analysis unit can provide analysis results at the appropriate time by determining the priority of analysis based on the timing of conversation submission. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the timing of conversation submission into a generating AI and have the generating AI perform the determination of the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of conversations during analysis. For example, the analysis unit can use AI to adjust the order of analysis based on the relevance of conversations. For example, the analysis unit can prioritize the analysis of important conversations. The analysis unit can postpone the analysis of less relevant conversations. The analysis unit can prioritize the analysis of conversations related to a specific topic. In this way, the analysis unit can prioritize the analysis of important conversations by adjusting the order of analysis based on the relevance of conversations. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input conversation relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The generation unit can adjust the level of detail of the responses it generates based on the importance of the conversation during generation. For example, the generation unit can use AI to adjust the level of detail of the responses it generates based on the importance of the conversation. For example, the generation unit generates detailed responses for important conversations. For general conversations, the generation unit generates concise responses. For short conversations, the generation unit generates responses quickly. In this way, the generation unit can provide appropriate responses by adjusting the level of detail of the responses based on the importance of the conversation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input conversation importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the responses.

[0049] The generation unit can apply different generation algorithms depending on the category of the conversation during generation. For example, the generation unit can use AI to apply different generation algorithms depending on the category of the conversation. For example, in the case of a business conversation, the generation unit can apply a generation algorithm that takes technical terms into account. In the case of a casual conversation, the generation unit can apply a generation algorithm that takes everyday language into account. In the case of a technical conversation, the generation unit can apply a generation algorithm that takes technical terms into account. In this way, the generation unit can provide more appropriate responses by applying different generation algorithms depending on the category of the conversation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input conversation category data into a generation AI and have the generation AI execute the application of different generation algorithms.

[0050] The generation unit can determine the priority of responses to generate based on the timing of conversation submissions during the generation process. For example, the generation unit may use AI to determine the priority of responses based on the timing of conversation submissions. For example, the generation unit may prioritize generating responses to recent conversations. The generation unit may postpone generating responses to past conversations. The generation unit may prioritize generating responses to conversations that took place during a specific time period. This allows the generation unit to provide responses at the appropriate time by prioritizing responses based on the timing of conversation submissions. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may input conversation submission timing data into a generation AI and have the generation AI perform the task of determining the priority of responses.

[0051] The generation unit can adjust the order of responses it generates based on the relevance of the conversation during generation. For example, the generation unit can use AI to adjust the order of responses based on the relevance of the conversation. For example, the generation unit can prioritize generating responses to important conversations. The generation unit can postpone generating responses to less relevant conversations. The generation unit can prioritize generating responses to conversations related to specific topics. In this way, the generation unit can prioritize responses to important conversations by adjusting the order of responses based on the relevance of the conversations. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input conversation relevance data into a generation AI and have the generation AI perform the adjustment of the response order.

[0052] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. The service provider can, for example, use AI to refer to the user's past operation history and select the optimal display method. The service provider can, for example, prioritize providing display methods that the user has preferred to use in the past. The service provider selects the optimal display method from the user's past operation history. The service provider analyzes the user's past operation history and provides a display method with high visibility. In this way, the service provider can select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data of the user's past operation history into a generating AI and have the generating AI perform the selection of the optimal display method.

[0053] The service provider can customize the means of providing responses based on the user's current situation at the time of delivery. For example, the service provider can use AI to customize the means of providing responses based on the user's current situation. For example, if the user is in a meeting, the service provider can prioritize providing text responses. If the user is driving, the service provider can provide voice responses. If the user is in a public place, the service provider can notify the user with vibration and provide visual responses. This allows the service provider to provide responses in a more appropriate manner by customizing the means of response based on the user's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's current situation into a generating AI and have the generating AI perform the customization of the means of response.

[0054] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can use AI to select the optimal delivery method by considering the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize providing information related to that region. If the user is traveling, the service provider can prioritize providing information related to the travel destination. If the user is participating in a specific event, the service provider can prioritize providing information related to that event. In this way, the service provider can select the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal delivery method.

[0055] The service provider can analyze the user's social media activity at the time of service provision and propose a means of providing a response. For example, the service provider can use AI to analyze the user's social media activity and propose a means of providing a response. For example, the service provider can prioritize providing the platforms that the user frequently uses on social media. The service provider can provide topics that the user's social media friends are interested in. The service provider can provide relevant responses based on the content of the user's social media posts. In this way, the service provider can propose a more appropriate means of response by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's social media activity into a generating AI and have the generating AI propose a means of response.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can analyze the user's past conversation history and select the optimal collection method. For example, it can prioritize collecting conversation topics that the user has preferred to use in the past, and exclude topics that the user has avoided in the past. It can also select a collection method suitable for a specific time period based on the user's past conversation history. In this way, the data collection unit can select the optimal collection method by analyzing the user's past conversation history.

[0058] The generation unit can apply different generation algorithms depending on the category of the conversation during generation. For example, in the case of business conversations, a generation algorithm that takes technical terms into account is applied. In the case of casual conversations, a generation algorithm that takes everyday language into account is applied. In the case of technical conversations, a generation algorithm that takes technical terms into account is applied. In this way, the generation unit can provide more appropriate responses by applying different generation algorithms depending on the category of the conversation.

[0059] The data collection unit can prioritize collecting conversations that are highly relevant by considering the user's geographical location. For example, if the user is in a specific region, it will prioritize collecting topics related to that region. If the user is traveling, it will prioritize collecting topics related to their travel destination. If the user is participating in a specific event, it will prioritize collecting topics related to that event. In this way, the data collection unit can prioritize collecting conversations that are highly relevant by considering the user's geographical location.

[0060] The analysis unit can adjust the level of detail in its analysis based on the importance of the conversation. For example, in the case of an important conversation, it performs a detailed analysis and generates a specific response. In the case of a general conversation, it performs a concise analysis and generates a simple response. In the case of a short conversation, it performs a rapid analysis and generates a response immediately. In this way, the analysis unit can provide appropriate analysis results by adjusting the level of detail in its analysis based on the importance of the conversation.

[0061] The service provider can select the optimal display method by referring to the user's past operation history at the time of delivery. For example, it can prioritize providing the display method that the user has previously preferred. It selects the optimal display method from the user's past operation history. It analyzes the user's past operation history and provides a display method with high visibility. In this way, the service provider can select the optimal display method by referring to the user's past operation history.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects the content of the conversation. The collection unit collects the content of the conversation using, for example, a microphone built into smart glasses. The collection unit can collect the content of the conversation in real time. Step 2: The analysis unit analyzes the content of the conversation collected by the collection unit. The analysis unit uses a generation AI to understand the context of the conversation and the content of what the other party said. The generation AI analyzes the content of the conversation using, for example, a text generation AI (e.g., LLM). The generation AI understands the context of the conversation and provides information to generate an appropriate response. Step 3: The generation unit generates an appropriate response based on the analysis performed by the analysis unit. The generation unit uses the generation AI to generate examples of appropriate responses and topics. The generation AI, for example, understands the context of the conversation and what the other person has said, and generates an appropriate response. Using the generation AI, the generation unit generates examples of responses such as "Talk about recent events" or "Talk about your hobbies" when the other person asks, for example, "How have you been lately?" Step 4: The provider unit provides the user with the responses generated by the generator unit. The provider unit displays the generated responses and topics on the smart glasses' display. The provider unit can also provide the generated responses and topics to the user via voice. For example, the provider unit may display "Talk about recent events" on the display or advise "Try talking about recent events" via voice.

[0064] (Example of form 2) The communication support system according to an embodiment of the present invention proposes smart glasses in the form of eyeglasses as a support device for people with developmental disabilities such as ASD and ADHD to communicate smoothly. In the communication support system, the user puts on the smart glasses and starts a conversation. A microphone built into the smart glasses collects the content of the conversation, and a generating AI analyzes the content. The generating AI understands the context of the conversation and what the other person says, and generates examples of appropriate responses and topics. The generated responses and topics are displayed on the smart glasses' display or provided to the user by voice. This allows the user to give appropriate responses in real time and supports the smooth progress of communication. For example, if a user is chatting with a friend, the smart glasses collect the content of the conversation. The collected content of the conversation is sent to the generating AI and analyzed. The generating AI understands the context of the conversation and what the other person says, and generates examples of appropriate responses and topics. For example, if the other person asks, "How have you been lately?", the generating AI generates examples of responses such as "Talk about recent events" or "Talk about your hobbies". The generated responses and topics are displayed on the smart glasses' display or provided to the user by voice. For example, the display might show "Tell us about recent events," or the user might receive a voice prompt saying, "Try telling us about recent events." This allows the user to provide appropriate responses in real time. Furthermore, the generative AI learns the content of the user's communication and provides feedback to reduce reliance on the device. For example, if the user thinks for themselves and gives a response, the AI ​​learns from that and incorporates it into future advice. This allows the user to gradually communicate without relying on the device. This mechanism allows people with developmental disabilities such as ASD and ADHD to experience smoother communication and build up successful experiences. Ultimately, the ideal is to make communication easier without relying on the device. In this way, the communication support system enables people with developmental disabilities such as ASD and ADHD to communicate smoothly.

[0065] The communication support system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the content of conversations. The collection unit collects the content of conversations using, for example, a microphone built into smart glasses. The collection unit can collect the content of conversations in real time. The analysis unit analyzes the content of conversations collected by the collection unit. The analysis unit uses a generation AI to understand the context of the conversation and the content of what the other person says. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the content of the conversation. The generation AI understands the context of the conversation and provides information for generating an appropriate response. The generation unit generates an appropriate response based on the content analyzed by the analysis unit. The generation unit uses the generation AI to generate examples of appropriate responses and topics. The generation AI, for example, understands the context of the conversation and what the other person says and generates an appropriate response. The generation unit uses the generation AI to generate examples of responses, for example, when the other person asks "How have you been lately?", such as "Talk about recent events" or "Talk about your hobbies." The provision unit provides the responses generated by the generation unit to the user. The providing unit displays the generated responses and topics on the smart glasses' display. The providing unit can also provide the generated responses and topics to the user via voice. For example, the providing unit may display "Talk about recent events" on the display or provide voice advice such as "Try talking about recent events." In this way, the communication support system according to the embodiment can facilitate communication by collecting and analyzing the content of the conversation, generating appropriate responses, and providing them to the user.

[0066] The data collection unit collects the content of conversations. For example, the data collection unit uses a microphone built into smart glasses to collect the content of conversations. Specifically, the microphone in the smart glasses captures ambient sound with high precision and removes unwanted background noise using noise cancellation technology. This allows for clear collection of the content of conversations. The data collection unit can collect the content of conversations in real time. The collected audio data is immediately converted into a digital signal and transmitted to a central database. Furthermore, the data collection unit adds a timestamp to the audio data and accurately records the flow of the conversation. This makes it easier for the subsequent analysis unit to understand the context. The data collection unit can also use multiple microphones and sound source localization technology to pinpoint the location of speakers. This makes it possible to accurately understand who is saying what, even when there are multiple speakers. By utilizing these functions, the data collection unit can collect the content of conversations with high precision and in real time, improving the overall performance of the system.

[0067] The analysis unit analyzes the content of conversations collected by the collection unit. The analysis unit uses generative AI to understand the context of the conversation and the content of what the other person is saying. Specifically, the generative AI uses natural language processing technology to convert the audio data into text and then analyzes that text. The generative AI analyzes the content of conversations using, for example, text generation AI (e.g., LLM). LLM has learned from a large amount of text data and has a high ability to understand context. The analysis unit uses LLM to understand the context of the conversation and provides information to generate appropriate responses. Specifically, LLM extracts keywords and phrases used in the conversation and analyzes their relationships. Furthermore, the analysis unit can also estimate the emotional state of the speaker using sentiment analysis technology. This allows it to understand whether the speaker is happy, sad, or angry and provides information to generate appropriate responses accordingly. By combining these technologies, the analysis unit can analyze the content of conversations from multiple angles and provide highly accurate information to the generation unit.

[0068] The generation unit generates appropriate responses based on the analysis performed by the analysis unit. The generation unit uses a generation AI to generate examples of appropriate responses and topics. Specifically, the generation AI understands the context of the conversation and what the other person has said, and generates appropriate responses. For example, if the other person asks, "How have you been lately?", the generation AI will generate example responses such as "Talk about recent events" or "Talk about your hobbies." The generation AI can also refer to past conversation data and user profile information to generate more personalized responses. For example, if it knows that the user has recently traveled, it will generate a specific response such as "Talk about your recent trip." The generation unit also has an algorithm that generates multiple response candidates and selects the most appropriate one. This allows the generation unit to always provide the user with the best possible response. Furthermore, the generation unit has a function to evaluate the naturalness and consistency of the generated responses and make corrections as needed. This allows the generation unit to provide users with high-quality responses and facilitate smooth communication.

[0069] The provider unit delivers responses generated by the generator unit to the user. The provider unit displays the generated responses and topics on the smart glasses' display. Specifically, the smart glasses' display is designed to blend naturally into the user's field of vision, providing the generated responses and topics visually. The provider unit can also provide the generated responses and topics to the user via voice. Voice output is performed using speakers built into the smart glasses or bone conduction technology, providing information to the user in clear voice. For example, the provider unit may display "Talk about recent events" on the display or advise "Try talking about recent events" via voice. Furthermore, the provider unit also has the function of collecting user feedback and providing feedback to the generator and analysis units. This allows for continuous improvement of the overall system accuracy and user experience. For example, it can evaluate whether the user is satisfied with the provided responses and adjust the generation algorithm based on the results. In addition, the provider unit can flexibly change the way information is delivered depending on the user's situation and environment. For example, it may prioritize voice output in noisy environments and display in quiet environments. This allows the service provider to deliver information to users in the most optimal way and facilitate smooth communication.

[0070] The collection unit can collect the content of conversations using a microphone built into the smart glasses. The collection unit can, for example, collect the content of conversations in real time using a microphone built into the smart glasses. The collection unit can also remove ambient noise using noise cancellation technology when collecting the content of conversations. The collection unit can, for example, use noise cancellation technology to collect the content of conversations clearly. The collection unit can also convert the audio data into text data using speech recognition technology when collecting the content of conversations. The collection unit can, for example, use speech recognition technology to convert the content of conversations into text data. This allows the collection unit to collect the content of conversations in real time using a microphone built into the smart glasses. 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 using the microphone built into the smart glasses into a generating AI and have the generating AI perform the conversion from audio data to text data.

[0071] The analysis unit can understand the context of a conversation and the content of the other party's statements using a generation AI. For example, the analysis unit uses a generation AI to understand the context of the conversation and analyze the content of the other party's statements. The generation AI analyzes the content of the conversation using a text generation AI (e.g., LLM). The generation AI understands the context of the conversation and provides information to generate an appropriate response. The analysis unit uses the generation AI to provide information to generate example responses, for example, when the other party asks "How have you been lately?", such as "Talk about recent events" or "Talk about your hobbies." In this way, the analysis unit can accurately understand the context of the conversation and the content of the other party's statements by using the generation AI. Some or all of the above processing in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit can input the content of the conversation collected by the collection unit into the generation AI and have the generation AI perform the analysis of the context of the conversation and the content of the other party's statements.

[0072] The generation unit can generate examples of appropriate responses and topics using a generation AI. For example, the generation unit generates examples of appropriate responses and topics using a generation AI. The generation AI analyzes the content of a conversation using a text generation AI (e.g., LLM) and generates appropriate responses. The generation AI understands the context of the conversation and provides information to generate appropriate responses. Using the generation AI, the generation unit generates examples of responses such as "Talk about recent events" or "Talk about your hobbies" when the other person asks "How have you been lately?". In this way, the generation unit can quickly generate examples of appropriate responses and topics by using a generation AI. Some or all of the above processing in the generation unit may be performed using an AI, for example, or without an AI. For example, the generation unit can input the content of the conversation analyzed by the analysis unit into the generation AI and have the generation AI perform the generation of examples of appropriate responses and topics.

[0073] The provider unit can display the generated responses and topics on the smart glasses' display. For example, the provider unit displays the generated responses and topics on the smart glasses' display. The provider unit makes it easier for the user to confirm the responses and topics by visually displaying them. For example, the provider unit displays "Talk about recent events" on the display. When displaying the generated responses and topics, the provider unit can also improve visibility by adjusting the font size and color. For example, the provider unit adjusts the font size and color to make it easier for the user to confirm the responses and topics. This allows the user to visually confirm the responses and topics by displaying them on the smart glasses' display. Some or all of the above processing in the provider unit may be performed using AI, for example, or without AI. For example, the provider unit can input the responses and topics generated by the generation unit into a generation AI and have the generation AI perform adjustments for visual display.

[0074] The delivery unit can provide the generated responses and topics to the user in voice. For example, the delivery unit can provide the generated responses and topics to the user in voice. By providing them in voice, the delivery unit can ensure that the user receives the responses and topics even in situations where they cannot visually confirm them. For example, the delivery unit can advise the user in voice, "Tell me about something that's happened recently." When providing in voice, the delivery unit can also adjust the volume and sound quality to make it easier to hear. For example, the delivery unit can adjust the volume and sound quality to make the responses and topics easier for the user to hear. This allows the delivery unit to ensure that the user receives the responses and topics even in situations where they cannot visually confirm them. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the responses and topics generated by the generation unit into a generation AI and have the generation AI perform adjustments for providing them in voice.

[0075] The analysis unit can learn the content of user communication and provide feedback. For example, the analysis unit can learn the content of user communication using a generative AI and provide feedback. The generative AI can analyze the content of user communication using a text generation AI (e.g., LLM) and generate feedback. The generative AI learns the patterns of user communication and provides feedback to reduce device dependency. The analysis unit can use the generative AI to learn the content of responses made by the user, for example, and reflect it in advice for future interactions. In this way, the analysis unit can learn the content of user communication and provide feedback to reduce device dependency. 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 the content of user communication into the generative AI and have the generative AI generate feedback.

[0076] The collection unit can estimate the user's emotions and adjust the timing of conversation collection based on the estimated emotions. For example, the collection unit estimates the user's emotions using an emotion engine or generative AI and adjusts the timing of conversation collection based on the estimated emotions. For example, if the user is nervous, the collection unit temporarily delays conversation collection to give them time to relax. If the user is relaxed, the collection unit immediately starts collecting conversation to maintain a natural flow. If the user is excited, the collection unit collects conversation quickly to ensure that important information is not missed. In this way, the collection unit can collect conversations more naturally by adjusting the timing of conversation collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then perform emotion estimation and adjust the timing of data collection.

[0077] The collection unit can analyze the user's past conversation history and select the optimal collection method when collecting conversations. For example, the collection unit can use AI to analyze the user's past conversation history and select the optimal collection method. For example, the collection unit can prioritize collecting conversation topics that the user has preferred to use in the past. The collection unit can exclude topics that the user has avoided in the past from collection. The collection unit can select a collection method suitable for a specific time period from the user's past conversation history. In this way, the collection unit can select the optimal collection method by analyzing the user's past conversation history. 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 user's past conversation history into a generating AI and have the generating AI select the optimal collection method.

[0078] The collection unit can filter conversations based on the user's current situation and areas of interest. For example, the collection unit can use AI to filter based on the user's current situation and areas of interest. For example, the collection unit can prioritize collecting work-related conversations depending on the user's current situation. The collection unit can filter and collect conversations related to hobbies based on the user's areas of interest. The collection unit can collect relaxing conversations to reduce stress depending on the user's current situation. In this way, the collection unit can collect highly relevant conversations by filtering based on the user's current situation and areas of interest. 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 data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0079] The collection unit can estimate the user's emotions and determine the priority of conversations to collect based on the estimated emotions. For example, the collection unit estimates the user's emotions using an emotion engine or generative AI and determines the priority of conversations to collect based on the estimated emotions. For example, if the user is tense, the collection unit will prioritize collecting relaxing topics. If the user is relaxed, the collection unit will prioritize collecting deep topics or discussions. If the user is excited, the collection unit will prioritize collecting interesting topics. In this way, the collection unit can collect more appropriate conversations by determining the priority of conversations to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then perform emotion estimation and determine conversation priorities.

[0080] The collection unit can prioritize collecting conversations that are highly relevant by considering the user's geographical location information when collecting conversations. For example, the collection unit can use AI to prioritize collecting conversations that are highly relevant by considering the user's geographical location information. For example, if the user is in a specific region, the collection unit will prioritize collecting topics related to that region. If the user is traveling, the collection unit will prioritize collecting topics related to the travel destination. If the user is participating in a specific event, the collection unit will prioritize collecting topics related to that event. In this way, the collection unit can prioritize collecting conversations that are highly relevant by considering the user's geographical location information. 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 user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant conversations.

[0081] The collection unit can analyze the user's social media activity and collect relevant conversations when collecting conversations. For example, the collection unit can use AI to analyze the user's social media activity and collect relevant conversations. For example, the collection unit can prioritize collecting topics that the user frequently mentions on social media. The collection unit can collect topics that the user's social media friends are interested in. The collection unit can collect relevant conversations based on the content of the user's social media posts. In this way, the collection unit can collect relevant conversations by analyzing the user's social media activity. 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 data on the user's social media activity into a generating AI and have the generating AI perform the collection of relevant conversations.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions using an emotion engine or generative AI and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is excited, the analysis unit provides a visually appealing analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 emotion estimation and adjustment of the presentation of the analysis.

[0083] The analysis unit can adjust the level of detail of its analysis based on the importance of the conversation during analysis. For example, the analysis unit can use AI to adjust the level of detail of its analysis based on the importance of the conversation. For example, in the case of an important conversation, the analysis unit performs a detailed analysis and generates a specific response. In the case of a general conversation, the analysis unit performs a concise analysis and generates a simple response. In the case of a short conversation, the analysis unit performs a rapid analysis and generates a response immediately. In this way, the analysis unit can provide appropriate analysis results by adjusting the level of detail of its analysis based on the importance of the conversation. 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 conversation importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of its analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of the conversation during analysis. For example, the analysis unit can use AI to apply different analysis algorithms depending on the category of the conversation. For example, in the case of business conversations, the analysis unit can apply an analysis algorithm that takes technical terms into account. In the case of casual conversations, the analysis unit can apply an analysis algorithm that takes everyday language into account. In the case of technical conversations, the analysis unit can apply an analysis algorithm that takes technical terms into account. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of the conversation. 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 conversation category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions using an emotion engine or generative AI and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit performs a short, concise analysis. If the user is relaxed, the analysis unit performs a detailed analysis. If the user is excited, the analysis unit performs a visually appealing analysis. This allows the analysis unit to provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the analysis length.

[0086] The analysis unit can determine the priority of analysis based on the timing of conversation submission during analysis. For example, the analysis unit can use AI to determine the priority of analysis based on the timing of conversation submission. For example, the analysis unit can prioritize analyzing recent conversations and generate responses immediately. The analysis unit can postpone the analysis of older conversations. The analysis unit can prioritize analyzing conversations that took place during a specific time period. In this way, the analysis unit can provide analysis results at the appropriate time by determining the priority of analysis based on the timing of conversation submission. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the timing of conversation submission into a generating AI and have the generating AI perform the determination of the analysis priority.

[0087] The analysis unit can adjust the order of analysis based on the relevance of conversations during analysis. For example, the analysis unit can use AI to adjust the order of analysis based on the relevance of conversations. For example, the analysis unit can prioritize the analysis of important conversations. The analysis unit can postpone the analysis of less relevant conversations. The analysis unit can prioritize the analysis of conversations related to a specific topic. In this way, the analysis unit can prioritize the analysis of important conversations by adjusting the order of analysis based on the relevance of conversations. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input conversation relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0088] The generation unit can estimate the user's emotions and adjust the expression of the response it generates based on the estimated emotions. For example, the generation unit estimates the user's emotions using an emotion engine or a generation AI and adjusts the expression of the response it generates based on the estimated emotions. For example, if the user is nervous, the generation unit generates a simple and easy-to-understand response. If the user is relaxed, the generation unit generates a detailed response. If the user is excited, the generation unit generates a visually appealing response. In this way, the generation unit can provide a more appropriate response by adjusting the expression of the response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation and adjustment of the response expression.

[0089] The generation unit can adjust the level of detail of the responses it generates based on the importance of the conversation during generation. For example, the generation unit can use AI to adjust the level of detail of the responses it generates based on the importance of the conversation. For example, the generation unit generates detailed responses for important conversations. For general conversations, the generation unit generates concise responses. For short conversations, the generation unit generates responses quickly. In this way, the generation unit can provide appropriate responses by adjusting the level of detail of the responses based on the importance of the conversation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input conversation importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the responses.

[0090] The generation unit can apply different generation algorithms depending on the category of the conversation during generation. For example, the generation unit can use AI to apply different generation algorithms depending on the category of the conversation. For example, in the case of a business conversation, the generation unit can apply a generation algorithm that takes technical terms into account. In the case of a casual conversation, the generation unit can apply a generation algorithm that takes everyday language into account. In the case of a technical conversation, the generation unit can apply a generation algorithm that takes technical terms into account. In this way, the generation unit can provide more appropriate responses by applying different generation algorithms depending on the category of the conversation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input conversation category data into a generation AI and have the generation AI execute the application of different generation algorithms.

[0091] The generation unit can estimate the user's emotions and adjust the length of the response it generates based on the estimated emotions. For example, the generation unit estimates the user's emotions using an emotion engine or a generation AI and adjusts the length of the response it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit generates a short, to-the-point response. If the user is relaxed, the generation unit generates a detailed response. If the user is excited, the generation unit generates a visually appealing response. In this way, the generation unit can provide a more appropriate response by adjusting the length of the response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation and response length adjustment.

[0092] The generation unit can determine the priority of responses to generate based on the timing of conversation submissions during the generation process. For example, the generation unit may use AI to determine the priority of responses based on the timing of conversation submissions. For example, the generation unit may prioritize generating responses to recent conversations. The generation unit may postpone generating responses to past conversations. The generation unit may prioritize generating responses to conversations that took place during a specific time period. This allows the generation unit to provide responses at the appropriate time by prioritizing responses based on the timing of conversation submissions. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may input conversation submission timing data into a generation AI and have the generation AI perform the task of determining the priority of responses.

[0093] The generation unit can adjust the order of responses it generates based on the relevance of the conversation during generation. For example, the generation unit can use AI to adjust the order of responses based on the relevance of the conversation. For example, the generation unit can prioritize generating responses to important conversations. The generation unit can postpone generating responses to less relevant conversations. The generation unit can prioritize generating responses to conversations related to specific topics. In this way, the generation unit can prioritize responses to important conversations by adjusting the order of responses based on the relevance of the conversations. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input conversation relevance data into a generation AI and have the generation AI perform the adjustment of the response order.

[0094] The service provider can estimate the user's emotions and adjust the display method of the response based on the estimated emotions. For example, the service provider can estimate the user's emotions using an emotion engine or generative AI and adjust the display method of the response based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. If the user is excited, the service provider can provide a visually appealing display method. In this way, the service provider can provide a more appropriate display by adjusting the display method of the response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display method.

[0095] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. The service provider can, for example, use AI to refer to the user's past operation history and select the optimal display method. The service provider can, for example, prioritize providing display methods that the user has preferred to use in the past. The service provider selects the optimal display method from the user's past operation history. The service provider analyzes the user's past operation history and provides a display method with high visibility. In this way, the service provider can select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data of the user's past operation history into a generating AI and have the generating AI perform the selection of the optimal display method.

[0096] The service provider can customize the means of providing responses based on the user's current situation at the time of delivery. For example, the service provider can use AI to customize the means of providing responses based on the user's current situation. For example, if the user is in a meeting, the service provider can prioritize providing text responses. If the user is driving, the service provider can provide voice responses. If the user is in a public place, the service provider can notify the user with vibration and provide visual responses. This allows the service provider to provide responses in a more appropriate manner by customizing the means of response based on the user's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's current situation into a generating AI and have the generating AI perform the customization of the means of response.

[0097] The service provider can estimate the user's emotions and determine the priority of responses to provide based on the estimated emotions. For example, the service provider can estimate the user's emotions using an emotion engine or generative AI and determine the priority of responses to provide based on the estimated emotions. For example, if the user is tense, the service provider will prioritize providing relaxing responses. If the user is relaxed, the service provider will prioritize providing detailed responses. If the user is excited, the service provider will prioritize providing interesting responses. In this way, the service provider can provide more appropriate responses by determining the priority of responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and response priority determination.

[0098] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can use AI to select the optimal delivery method by considering the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize providing information related to that region. If the user is traveling, the service provider can prioritize providing information related to the travel destination. If the user is participating in a specific event, the service provider can prioritize providing information related to that event. In this way, the service provider can select the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal delivery method.

[0099] The service provider can analyze the user's social media activity at the time of service provision and propose a means of providing a response. For example, the service provider can use AI to analyze the user's social media activity and propose a means of providing a response. For example, the service provider can prioritize providing the platforms that the user frequently uses on social media. The service provider can provide topics that the user's social media friends are interested in. The service provider can provide relevant responses based on the content of the user's social media posts. In this way, the service provider can propose a more appropriate means of response by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's social media activity into a generating AI and have the generating AI propose a means of response.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] A communication support system can estimate a user's emotions and select conversation topics based on those emotions. For example, if the user is nervous, it can select relaxing topics. If the user is relaxed, it can select deeper topics or discussions. If the user is excited, it can select topics that pique their interest. By selecting appropriate conversation topics according to the user's emotions, it can support more natural communication.

[0102] The data collection unit can analyze the user's past conversation history and select the optimal collection method. For example, it can prioritize collecting conversation topics that the user has preferred to use in the past, and exclude topics that the user has avoided in the past. It can also select a collection method suitable for a specific time period based on the user's past conversation history. In this way, the data collection unit can select the optimal collection method by analyzing the user's past conversation history.

[0103] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, it provides simple and easy-to-understand analysis results. If the user is relaxed, it provides detailed analysis results. If the user is excited, it provides visually appealing analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions.

[0104] The generation unit can apply different generation algorithms depending on the category of the conversation during generation. For example, in the case of business conversations, a generation algorithm that takes technical terms into account is applied. In the case of casual conversations, a generation algorithm that takes everyday language into account is applied. In the case of technical conversations, a generation algorithm that takes technical terms into account is applied. In this way, the generation unit can provide more appropriate responses by applying different generation algorithms depending on the category of the conversation.

[0105] The service provider can estimate the user's emotions and adjust the way the response is displayed based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. If the user is excited, it can provide a visually appealing display. In this way, the service provider can provide a more appropriate display by adjusting the way the response is displayed according to the user's emotions.

[0106] The data collection unit can prioritize collecting conversations that are highly relevant by considering the user's geographical location. For example, if the user is in a specific region, it will prioritize collecting topics related to that region. If the user is traveling, it will prioritize collecting topics related to their travel destination. If the user is participating in a specific event, it will prioritize collecting topics related to that event. In this way, the data collection unit can prioritize collecting conversations that are highly relevant by considering the user's geographical location.

[0107] The analysis unit can adjust the level of detail in its analysis based on the importance of the conversation. For example, in the case of an important conversation, it performs a detailed analysis and generates a specific response. In the case of a general conversation, it performs a concise analysis and generates a simple response. In the case of a short conversation, it performs a rapid analysis and generates a response immediately. In this way, the analysis unit can provide appropriate analysis results by adjusting the level of detail in its analysis based on the importance of the conversation.

[0108] The generation unit can estimate the user's emotions and adjust the length of the response it generates based on those emotions. For example, if the user is in a hurry, it will generate a short, to-the-point response. If the user is relaxed, it will generate a detailed response. If the user is excited, it will generate a visually appealing response. In this way, the generation unit can provide more appropriate responses by adjusting the length of the response according to the user's emotions.

[0109] The service provider can select the optimal display method by referring to the user's past operation history at the time of delivery. For example, it can prioritize providing the display method that the user has previously preferred. It selects the optimal display method from the user's past operation history. It analyzes the user's past operation history and provides a display method with high visibility. In this way, the service provider can select the optimal display method by referring to the user's past operation history.

[0110] The service provider can estimate the user's emotions and determine the priority of responses based on those emotions. For example, if the user is nervous, it will prioritize providing relaxing responses. If the user is relaxed, it will prioritize providing detailed responses. If the user is excited, it will prioritize providing interesting responses. In this way, the service provider can provide more appropriate responses by prioritizing responses according to the user's emotions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection unit collects the content of the conversation. The collection unit collects the content of the conversation using, for example, a microphone built into smart glasses. The collection unit can collect the content of the conversation in real time. Step 2: The analysis unit analyzes the content of the conversation collected by the collection unit. The analysis unit uses a generation AI to understand the context of the conversation and the content of what the other party said. The generation AI analyzes the content of the conversation using, for example, a text generation AI (e.g., LLM). The generation AI understands the context of the conversation and provides information to generate an appropriate response. Step 3: The generation unit generates an appropriate response based on the analysis performed by the analysis unit. The generation unit uses the generation AI to generate examples of appropriate responses and topics. The generation AI, for example, understands the context of the conversation and what the other person has said, and generates an appropriate response. Using the generation AI, the generation unit generates examples of responses such as "Talk about recent events" or "Talk about your hobbies" when the other person asks, for example, "How have you been lately?" Step 4: The provider unit provides the user with the responses generated by the generator unit. The provider unit displays the generated responses and topics on the smart glasses' display. The provider unit can also provide the generated responses and topics to the user via voice. For example, the provider unit may display "Talk about recent events" on the display or advise "Try talking about recent events" via voice.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the content of a conversation using the microphone of the smart device 14. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the content of the collected conversation. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an appropriate response based on the analyzed content. The provision unit provides the generated response to the user using the display or speaker of the smart device 14. The collection unit estimates the user's emotions using an emotion engine or generative AI and adjusts the timing of conversation collection based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the content of a conversation using the microphone of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the content of the collected conversation. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an appropriate response based on the analyzed content. The provision unit provides the generated response to the user using the display or speaker of the smart glasses 214. The collection unit estimates the user's emotions using an emotion engine or generative AI and adjusts the timing of conversation collection based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the content of a conversation using the microphone of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the content of the collected conversation. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an appropriate response based on the analyzed content. The provision unit provides the generated response to the user using the display or speaker of the headset terminal 314. The collection unit estimates the user's emotions using an emotion engine or generative AI and adjusts the timing of conversation collection based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the content of a conversation using the microphone of the robot 414. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the content of the collected conversation. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an appropriate response based on the analyzed content. The provision unit provides the generated response to the user using the display or speaker of the robot 414. The collection unit estimates the user's emotions using an emotion engine or generative AI and adjusts the timing of conversation collection based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A collection department that collects the content of conversations, An analysis unit analyzes the content of conversations collected by the aforementioned collection unit, A generation unit that generates an appropriate response based on the content analyzed by the analysis unit, The system includes a providing unit that provides the response generated by the generation unit to the user. A system characterized by the following features. (Note 2) The aforementioned collection unit is The conversation is collected using a microphone built into the smart glasses. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Generative AI understands the context of the conversation and what the other person is saying. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The AI ​​generates appropriate responses and example topics. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The generated responses and topics are displayed on the smart glasses' screen. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The generated responses and topics are provided to the user via voice. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, Learn from the content of user communications and provide feedback. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of conversation collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting conversation data, the system analyzes the user's past conversation history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting conversations, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of conversations to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting conversations, the system prioritizes collecting highly relevant conversations by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting conversations, the system analyzes users' social media activity and collects relevant conversations. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the conversation was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, adjust the level of detail in the generated response based on the importance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, different generation algorithms are applied depending on the category of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and adjusts the length of the response generated based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the priority of responses to be generated is determined based on when the conversation was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, adjust the order of responses based on the relevance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts how responses are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing a response, customize the method of response based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of responses to provide based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, we will analyze the user's social media activity and propose a method for providing responses. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 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 department that collects the content of conversations, An analysis unit analyzes the content of conversations collected by the aforementioned collection unit, A generation unit that generates an appropriate response based on the content analyzed by the analysis unit, The system includes a providing unit that provides the response generated by the generation unit to the user. A system characterized by the following features.

2. The aforementioned collection unit is The conversation is collected using a microphone built into the smart glasses. The system according to feature 1.

3. The aforementioned analysis unit, Generative AI understands the context of the conversation and what the other person is saying. The system according to feature 1.

4. The generating unit is The AI ​​generates examples of appropriate responses and topics. The system according to feature 1.

5. The aforementioned supply unit is, The generated responses and topics are displayed on the smart glasses' screen. The system according to feature 1.

6. The aforementioned supply unit is, The generated responses and topics are provided to the user via voice. The system according to feature 1.

7. The aforementioned analysis unit, Learn from the content of user communications and provide feedback. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of conversation collection based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting conversation data, the system analyzes the user's past conversation history to select the most suitable collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting conversations, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

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