system

Abstract

The system according to this embodiment aims to improve the user's communication skills. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives messages from users. The analysis unit analyzes the messages received by the reception unit. The provision unit provides advice based on the results of the analysis performed by the analysis unit.

Description

Technical Field

[0004] ,

[0006]

[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

[0007] The system according to this embodiment allows users to improve their communication skills. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) A training system according to an embodiment of the present invention is a system for improving communication skills using an AI avatar. This training system allows users to speak to the AI ​​avatar as they would with a regular messaging app. The AI ​​avatar analyzes the user's messages and points out areas where the user is doing well and areas that need improvement. The AI ​​avatar also provides advice on how to initiate conversations. This allows users to improve their communication skills and lower the barrier to initiating conversations. Ultimately, users gain confidence in conversations, experience reduced stress, and improve communication with their partners. For example, a user speaks to the AI ​​avatar. The user can speak as they wish, for instance, discussing work-related problems or everyday events. This information is input into the AI ​​avatar. Next, the AI ​​avatar analyzes the input information. The AI ​​avatar understands the user's messages and points out areas where the user is doing well and areas that need improvement. For example, if a user says, "Lately, things haven't been going well at work," the AI ​​avatar might offer advice such as, "What you're doing is great, but you need to be a little more strategic in your actions." Furthermore, the AI ​​avatar also provides advice on how to initiate conversations. For example, it provides advice such as, "Giving more specific examples will make it easier for the other person to understand." This allows users to improve their communication skills. This mechanism allows users to improve their communication skills and lower the barrier to initiating conversations. Through conversations with the AI ​​avatar, users can overcome their weaknesses and gain confidence in conversation. It also reduces stress and improves communication with their partner. For example, if a user has trouble in a conversation with their partner, they can refer to the AI ​​avatar's advice to communicate better. In this way, the training system can improve users' communication skills and lower the barrier to initiating conversations.

[0029] The training system according to this embodiment comprises a reception unit, an analysis unit, and a delivery unit. The reception unit receives messages from the user. User messages include, but are not limited to, text messages, voice messages, and image messages. For example, the reception unit receives text messages. The reception unit can also receive voice messages. The reception unit can also receive image messages. For example, the reception unit receives a text message sent by the user and inputs it into the system. Voice messages are recorded via a microphone and input into the system. Image messages are captured via a camera and input into the system. The analysis unit analyzes the messages received by the reception unit. Analysis is performed by, but is not limited to, methods such as natural language processing, sentiment analysis, and keyword extraction. For example, the analysis unit uses natural language processing to analyze the content of the message. The analysis unit can also use sentiment analysis to analyze the sentiment of the message. The analysis unit can also use keyword extraction to extract important points of the message. For example, the analysis unit uses natural language processing to analyze the grammatical structure of the message and understand its meaning. Sentiment analysis analyzes the emotional tone of a message to understand the user's emotional state. Keyword extraction extracts particularly important words and phrases from the message, focusing the analysis. The service provider provides advice based on the results analyzed by the analysis unit. Advice may be provided in various ways, such as text, audio, or real-time. For example, the service provider may provide text-based advice. It may also provide audio advice. It may also provide real-time advice. For example, based on the analysis results, the service provider may send specific advice to the user as a text message. Audio advice is delivered to the user through a speaker. Real-time advice is provided immediately after the user sends a message.As a result, the training system according to this embodiment can improve communication skills by receiving, analyzing, and providing advice to the user's messages.

[0030] The reception unit receives user messages. User messages include, but are not limited to, text messages, voice messages, and image messages. For example, the reception unit receives text messages. Specifically, it receives text messages entered by the user using a keyboard or touchscreen and inputs them into the system. Voice messages are recorded via a microphone and input into the system. Speech recognition technology is used to receive voice messages and convert the user's speech into text. Image messages are captured via a camera and input into the system. Image recognition technology is used to receive image messages and analyze the content of the images. For example, it receives text messages sent by users and inputs them into the system. Voice messages are recorded via a microphone and input into the system. Image messages are captured via a camera and input into the system. This allows the reception unit to receive messages in various formats from users, improving user convenience. Furthermore, the reception unit can centrally manage the received messages and quickly transfer them to the analysis and delivery units. This improves the overall efficiency of the system and allows for a quick response to users.

[0031] The analysis unit analyzes messages received by the reception unit. Analysis is performed using methods such as natural language processing, sentiment analysis, and keyword extraction, but is not limited to these examples. Specifically, natural language processing is used to analyze the content of the message. Natural language processing includes morphological analysis, grammatical analysis, and semantic analysis, which are combined to analyze the grammatical structure of the message and understand its meaning. Sentiment analysis analyzes the emotional tone of the message and grasps the user's emotional state. Sentiment analysis uses methods to classify emotions into categories such as positive, negative, and neutral. Keyword extraction extracts particularly important words and phrases from the message, focusing the analysis. For example, the analysis unit uses natural language processing to analyze the grammatical structure of the message and understand its meaning. Sentiment analysis analyzes the emotional tone of the message and grasps the user's emotional state. Keyword extraction extracts particularly important words and phrases from the message, focusing the analysis. This allows the analysis unit to quickly and accurately analyze received messages and understand the user's intentions and emotions. Furthermore, the analysis unit can perform more accurate analyses by utilizing past message data and user history. This allows the analysis unit to build a foundation for providing appropriate advice tailored to user needs.

[0032] The service provider provides advice based on the results analyzed by the analysis unit. This advice may be provided in various ways, including, but are not limited to, text-based advice, voice-based advice, and real-time advice. Specifically, the service provider offers text-based advice, which sends specific answers or suggestions to the user's message as a text message. Voice-based advice is delivered to the user via a speaker. Voice-based advice utilizes speech synthesis technology, providing the user with a voice message generated based on the analysis results. Real-time advice is provided immediately after the user sends a message. Real-time advice employs an instant response system, quickly generating and delivering advice based on the analysis results. For example, the service provider sends specific advice to the user as a text message based on the analysis results. Voice-based advice is delivered to the user via a speaker. Real-time advice is provided immediately after the user sends a message. This allows the service provider to quickly provide appropriate advice to the user and improve their communication skills. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. This allows the service provider to consistently offer users the best possible advice and improve the overall system performance.

[0033] The analysis unit may include an identification unit that identifies the user's strengths and areas for improvement. The identification unit may, for example, provide positive feedback. It may also extract success stories. Furthermore, it may provide negative feedback. For example, the identification unit may extract positive elements from the user's message and provide them as feedback. Extracting success stories identifies instances where the user has succeeded in the past and provides them as feedback. Negative feedback extracts areas for improvement from the user's message and provides them as specific advice. This allows for more specific feedback by identifying the user's strengths and areas for improvement. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit may input the user's message into AI, which can then identify positive and negative elements.

[0034] The service provider may include an advice unit that provides advice on how to initiate a conversation. The advice unit may, for example, advise on appropriate language use. It may also advise on topic selection. Furthermore, it may advise on timing. For example, the advice unit may suggest appropriate language to the user. Topic selection advice will provide guidance on how to choose an appropriate topic when initiating a conversation. Timing advice will suggest the optimal time for the user to initiate a conversation. In this way, by providing advice on how to initiate a conversation, the user's communication skills can be improved. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit may input the user's message into AI, which may then suggest appropriate language use and topic selection.

[0035] The reception desk can receive messages from users as they would in a regular messaging app. For example, it can receive messages in a chat format. It can also receive real-time messaging. Furthermore, it can receive messages containing stamps and emojis. For example, the reception desk receives chat-style messages sent by users and inputs them into the system. Real-time messaging is received immediately after the user sends the message. Messages containing stamps and emojis are received when sent by users. This allows for natural communication by enabling users to speak as they would in a regular messaging app. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user messages into an AI, which can then analyze the message content.

[0036] The service provider can provide specific advice to improve users' communication skills. For example, the service provider can provide specific action guidelines. The service provider can also provide improvement measures. The service provider can also introduce success stories. For example, the service provider can propose specific action guidelines to users. Improvement measures would specifically indicate areas where users should improve and suggest methods for improvement. Introducing success stories would provide successful examples that users can refer to. In this way, by providing specific advice, users' communication skills can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's message into AI, and the AI ​​can suggest specific action guidelines or improvement measures.

[0037] The reception unit can analyze the user's past message history and select the optimal reception method. For example, the reception unit can prioritize receiving messages in formats that the user has frequently used in the past. It can also adjust reception to match specific time periods if the user has previously sent messages during those times. Furthermore, the reception unit can prioritize receiving messages related to specific topics based on the user's past message history. For example, the reception unit can prioritize receiving messages in formats that the user has frequently used in the past. It can adjust reception to match specific time periods if the user has previously sent messages during those times. It can prioritize receiving messages related to specific topics based on the user's past message history. This allows the reception unit to select the optimal reception method by analyzing past message history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past message history into AI, which can then select the optimal reception method.

[0038] The reception system can filter messages based on the user's current situation and areas of interest when receiving them. For example, if the user is at work, the reception system will prioritize receiving work-related messages. Similarly, if the user is on vacation, the reception system can prioritize receiving relaxing messages. Furthermore, the reception system can filter and receive relevant messages based on the user's areas of interest. For example, if the user is at work, the reception system will prioritize receiving work-related messages. If the user is on vacation, the reception system will prioritize receiving relaxing messages. It can also filter and receive relevant messages based on the user's areas of interest. This allows the reception system to prioritize receiving highly relevant messages by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the user's current situation and areas of interest into the AI, which can then perform the filtering.

[0039] The reception unit can prioritize receiving messages that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception unit will prioritize receiving messages related to that location. It can also prioritize receiving messages related to the user's travel destination if the user is traveling. Furthermore, it can prioritize receiving messages related to the user's home if the user is at home. This allows the reception unit to prioritize receiving highly relevant messages by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location into the AI, which can then select highly relevant messages.

[0040] The reception unit can analyze the user's social media activity when receiving a message and receive relevant messages. For example, the reception unit can prioritize receiving messages related to topics the user is discussing on social media. It can also prioritize receiving messages related to accounts the user follows on social media. Furthermore, the reception unit can receive messages according to the user's social media activity schedule. For example, the reception unit can prioritize receiving messages related to topics the user is discussing on social media. It can prioritize receiving messages related to accounts the user follows on social media. It can receive messages according to the user's social media activity schedule. This allows the reception unit to prioritize receiving relevant messages by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's social media activity into AI, which can then select relevant messages.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the message during the analysis. For example, the analysis unit can analyze high-importance messages in detail, and low-importance messages in a simplified manner. It can also analyze urgent messages quickly. For example, the analysis unit can analyze high-importance messages in detail and extract all points, analyze low-importance messages in a simplified manner and extract only the main points, and quickly analyze urgent messages and provide results immediately. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the message. 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 importance of the message into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, the analysis unit can apply a business analysis algorithm to work-related messages. It can also apply a casual analysis algorithm to private messages. Furthermore, it can apply a rapid analysis algorithm to urgent messages. For example, the analysis unit can apply a business analysis algorithm to work-related messages, a casual analysis algorithm to private messages, and a rapid analysis algorithm to urgent messages. By applying different analysis algorithms depending on the message category, more appropriate analysis becomes possible. 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 message category into the AI, and the AI ​​can select an appropriate analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on the submission date of messages during analysis. For example, the analysis unit may prioritize the analysis of recently submitted messages. It can also postpone the analysis of older messages. Furthermore, it can prioritize the analysis of urgent messages regardless of their submission date. For example, the analysis unit may prioritize the analysis of recently submitted messages, postpone older messages, and prioritize the analysis of urgent messages regardless of their submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of messages. 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 submission dates of messages into the AI, and the AI ​​can determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of messages during analysis. For example, the analysis unit may prioritize analyzing highly relevant messages. It can also postpone analyzing less relevant messages. Furthermore, it can prioritize analyzing urgent messages regardless of their relevance. For example, the analysis unit may prioritize analyzing highly relevant messages, postpone analyzing less relevant messages, and prioritize analyzing urgent messages regardless of their relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of messages. 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 relevance of messages into the AI, which can then adjust the order of analysis.

[0045] The service provider can provide optimal advice by referring to the user's past behavior history when providing advice. For example, the service provider can provide optimal advice based on advice the user has received in the past. The service provider can also extract and provide effective advice from the user's past behavior history. Furthermore, the service provider can analyze the user's past behavior history and provide the most appropriate advice. For example, the service provider can provide optimal advice based on advice the user has received in the past. It can extract and provide effective advice from the user's past behavior history. It can analyze the user's past behavior history and provide the most appropriate advice. In this way, optimal advice can be provided by referring to the user's past behavior history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past behavior history into AI, and the AI ​​can select the optimal advice.

[0046] The service provider can customize the content of advice based on the user's current situation when providing advice. For example, if the user is at work, the service provider will provide work-related advice. If the user is on vacation, the service provider can also provide advice that promotes relaxation. The service provider can also customize and provide the most appropriate advice based on the user's current situation. For example, if the user is at work, the service provider will provide work-related advice. If the user is on vacation, the service provider will provide advice that promotes relaxation. The service provider can customize and provide the most appropriate advice based on the user's current situation. This allows for more appropriate advice by customizing the content of the advice 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 not using AI. For example, the service provider can input the user's current situation into AI, and the AI ​​can customize the content of the advice.

[0047] The service provider can provide optimal advice by considering the user's geographical location. For example, if the user is in a specific location, the service provider can provide advice relevant to that location. If the user is traveling, the service provider can also provide advice relevant to the travel destination. If the user is at home, the service provider can also provide advice relevant to the home. For example, if the service provider is in a specific location, the service provider can provide advice relevant to that location. If the user is traveling, the service provider can provide advice relevant to the travel destination. If the user is at home, the service provider can provide advice relevant to the home. This allows the service provider to provide highly relevant advice by considering the user's geographical location. 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 the user's geographical location into AI, and the AI ​​can select the optimal advice.

[0048] The service provider can analyze the user's social media activity and adjust the content of the advice when providing it. For example, the service provider can provide advice related to topics the user is talking about on social media. It can also provide advice related to accounts the user follows on social media. Furthermore, the service provider can provide advice tailored to the user's social media activity time. For example, the service provider can provide advice related to topics the user is talking about on social media. It can provide advice related to accounts the user follows on social media. It can provide advice tailored to the user's social media activity time. By analyzing the user's social media activity, it can provide highly relevant advice. 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 the user's social media activity into AI, and the AI ​​can adjust the content of the advice.

[0049] The identification unit can improve the accuracy of identification by referring to the user's past behavior history at the time of identification. For example, the identification unit can improve the accuracy of identification based on feedback the user has received in the past. The identification unit can also extract and provide effective feedback from the user's past behavior history. The identification unit can also analyze the user's past behavior history and provide the most appropriate feedback. For example, the identification unit can improve the accuracy of identification based on feedback the user has received in the past. It can extract and provide effective feedback from the user's past behavior history. It can analyze the user's past behavior history and provide the most appropriate feedback. In this way, the accuracy of identification can be improved by referring to the user's past behavior history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's past behavior history into AI, and the AI ​​can improve the accuracy of identification.

[0050] The identification unit can improve the accuracy of identification by considering the user's geographical location information at the time of identification. For example, if the user is in a specific location, the identification unit provides feedback related to that location. The identification unit can also provide feedback related to the travel destination if the user is traveling. The identification unit can also provide feedback related to the home if the user is at home. For example, if the identification unit is in a specific location, it provides feedback related to that location. If the user is traveling, it provides feedback related to the travel destination. If the user is at home, it provides feedback related to the home. This improves the accuracy of identification by considering the user's geographical location information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's geographical location information into AI, which can then improve the accuracy of identification.

[0051] The advice unit can provide optimal advice by referring to the user's past behavior history when providing advice. For example, the advice unit can provide optimal advice based on advice the user has received in the past. The advice unit can also extract and provide effective advice from the user's past behavior history. Furthermore, the advice unit can analyze the user's past behavior history and provide the most appropriate advice. For example, the advice unit can provide optimal advice based on advice the user has received in the past. It can extract and provide effective advice from the user's past behavior history. It can analyze the user's past behavior history and provide the most appropriate advice. In this way, optimal advice can be provided by referring to the user's past behavior history. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's past behavior history into AI, and the AI ​​can select the optimal advice.

[0052] The advice unit can provide optimal advice by considering the user's geographical location when providing advice. For example, if the user is in a specific location, the advice unit can provide advice relevant to that location. If the user is traveling, the advice unit can also provide advice relevant to the travel destination. If the user is at home, the advice unit can also provide advice relevant to the home. For example, if the advice unit is in a specific location, it can provide advice relevant to that location. If the user is traveling, it can provide advice relevant to the travel destination. If the user is at home, it can provide advice relevant to the home. This allows the advice unit to provide highly relevant advice by considering the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's geographical location information into AI, and the AI ​​can select the optimal advice.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The analysis unit can improve the accuracy of its analysis by referring to the user's past message history when analyzing a user's message. For example, the analysis unit can learn patterns from messages the user has sent in the past and incorporate them into the analysis of the current message. It can also extract specific keywords and phrases from the user's past messages and use them to identify important points in the current message. Furthermore, by analyzing the user's past message history and understanding the user's communication style and preferences, the analysis unit can provide more appropriate analysis results. This allows for improved analysis accuracy and more appropriate advice by utilizing the user's past message history.

[0055] The reception unit can adjust how messages are received, taking into account the user's geographical location. For example, if a user is in a specific location, messages related to that location can be prioritized. Similarly, if a user is traveling, messages related to their travel destination can be prioritized. Furthermore, if a user is at home, messages related to their home can be prioritized. This allows for the prioritization of highly relevant messages by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not.

[0056] The service provider can customize the content of advice by referring to the user's past behavior history. For example, it can provide advice that is best suited to the current situation based on advice the user has received in the past. It can also extract and provide effective advice from the user's past behavior history. Furthermore, it can analyze the user's past behavior history and provide the most appropriate advice. In this way, more appropriate advice can be provided by referring to the user's past behavior history. Some or all of the above processing in the service provider may be performed using AI or not.

[0057] The analysis unit can apply different analysis algorithms depending on the message category. For example, a business analysis algorithm can be applied to work-related messages, a casual analysis algorithm to private messages, and a rapid analysis algorithm to urgent messages. By applying different analysis algorithms depending on the message category, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not.

[0058] The reception desk can analyze a user's social media activity and prioritize receiving relevant messages. For example, it can prioritize receiving messages related to topics the user is discussing on social media. It can also prioritize receiving messages related to accounts the user follows on social media. Furthermore, it can prioritize receiving messages according to the user's social media activity schedule. In this way, by analyzing a user's social media activity, it is possible to prioritize receiving relevant messages. Some or all of the above processing in the reception desk may be performed using AI or not.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives user messages. User messages include text messages, voice messages, and image messages. For example, the reception desk receives text messages sent by users and inputs them into the system. Voice messages are recorded via a microphone and input into the system. Image messages are captured via a camera and input into the system. Step 2: The analysis unit analyzes the messages received by the reception unit. The analysis is performed using methods such as natural language processing, sentiment analysis, and keyword extraction. For example, the analysis unit uses natural language processing to analyze the content of the message, sentiment analysis to analyze the sentiment of the message, and keyword extraction to extract the important points of the message. Step 3: The service provider provides advice based on the results analyzed by the analysis unit. The advice is provided in various formats, such as text, audio, or real-time. For example, the service provider sends specific advice to the user as a text message based on the analysis results, audio advice is delivered to the user through a speaker, and real-time advice is provided immediately after the user sends a message.

[0061] (Example of form 2) A training system according to an embodiment of the present invention is a system for improving communication skills using an AI avatar. This training system allows users to speak to the AI ​​avatar as they would with a regular messaging app. The AI ​​avatar analyzes the user's messages and points out areas where the user is doing well and areas that need improvement. The AI ​​avatar also provides advice on how to initiate conversations. This allows users to improve their communication skills and lower the barrier to initiating conversations. Ultimately, users gain confidence in conversations, experience reduced stress, and improve communication with their partners. For example, a user speaks to the AI ​​avatar. The user can speak as they wish, for instance, discussing work-related problems or everyday events. This information is input into the AI ​​avatar. Next, the AI ​​avatar analyzes the input information. The AI ​​avatar understands the user's messages and points out areas where the user is doing well and areas that need improvement. For example, if a user says, "Lately, things haven't been going well at work," the AI ​​avatar might offer advice such as, "What you're doing is great, but you need to be a little more strategic in your actions." Furthermore, the AI ​​avatar also provides advice on how to initiate conversations. For example, it provides advice such as, "Giving more specific examples will make it easier for the other person to understand." This allows users to improve their communication skills. This mechanism allows users to improve their communication skills and lower the barrier to initiating conversations. Through conversations with the AI ​​avatar, users can overcome their weaknesses and gain confidence in conversation. It also reduces stress and improves communication with their partner. For example, if a user has trouble in a conversation with their partner, they can refer to the AI ​​avatar's advice to communicate better. In this way, the training system can improve users' communication skills and lower the barrier to initiating conversations.

[0062] The training system according to this embodiment comprises a reception unit, an analysis unit, and a delivery unit. The reception unit receives messages from the user. User messages include, but are not limited to, text messages, voice messages, and image messages. For example, the reception unit receives text messages. The reception unit can also receive voice messages. The reception unit can also receive image messages. For example, the reception unit receives a text message sent by the user and inputs it into the system. Voice messages are recorded via a microphone and input into the system. Image messages are captured via a camera and input into the system. The analysis unit analyzes the messages received by the reception unit. Analysis is performed by, but is not limited to, methods such as natural language processing, sentiment analysis, and keyword extraction. For example, the analysis unit uses natural language processing to analyze the content of the message. The analysis unit can also use sentiment analysis to analyze the sentiment of the message. The analysis unit can also use keyword extraction to extract important points of the message. For example, the analysis unit uses natural language processing to analyze the grammatical structure of the message and understand its meaning. Sentiment analysis analyzes the emotional tone of a message to understand the user's emotional state. Keyword extraction extracts particularly important words and phrases from the message, focusing the analysis. The service provider provides advice based on the results analyzed by the analysis unit. Advice may be provided in various ways, such as text, audio, or real-time. For example, the service provider may provide text-based advice. It may also provide audio advice. It may also provide real-time advice. For example, based on the analysis results, the service provider may send specific advice to the user as a text message. Audio advice is delivered to the user through a speaker. Real-time advice is provided immediately after the user sends a message.As a result, the training system according to this embodiment can improve communication skills by receiving, analyzing, and providing advice to the user's messages.

[0063] The reception unit receives user messages. User messages include, but are not limited to, text messages, voice messages, and image messages. For example, the reception unit receives text messages. Specifically, it receives text messages entered by the user using a keyboard or touchscreen and inputs them into the system. Voice messages are recorded via a microphone and input into the system. Speech recognition technology is used to receive voice messages and convert the user's speech into text. Image messages are captured via a camera and input into the system. Image recognition technology is used to receive image messages and analyze the content of the images. For example, it receives text messages sent by users and inputs them into the system. Voice messages are recorded via a microphone and input into the system. Image messages are captured via a camera and input into the system. This allows the reception unit to receive messages in various formats from users, improving user convenience. Furthermore, the reception unit can centrally manage the received messages and quickly transfer them to the analysis and delivery units. This improves the overall efficiency of the system and allows for a quick response to users.

[0064] The analysis unit analyzes messages received by the reception unit. Analysis is performed using methods such as natural language processing, sentiment analysis, and keyword extraction, but is not limited to these examples. Specifically, natural language processing is used to analyze the content of the message. Natural language processing includes morphological analysis, grammatical analysis, and semantic analysis, which are combined to analyze the grammatical structure of the message and understand its meaning. Sentiment analysis analyzes the emotional tone of the message and grasps the user's emotional state. Sentiment analysis uses methods to classify emotions into categories such as positive, negative, and neutral. Keyword extraction extracts particularly important words and phrases from the message, focusing the analysis. For example, the analysis unit uses natural language processing to analyze the grammatical structure of the message and understand its meaning. Sentiment analysis analyzes the emotional tone of the message and grasps the user's emotional state. Keyword extraction extracts particularly important words and phrases from the message, focusing the analysis. This allows the analysis unit to quickly and accurately analyze received messages and understand the user's intentions and emotions. Furthermore, the analysis unit can perform more accurate analyses by utilizing past message data and user history. This allows the analysis unit to build a foundation for providing appropriate advice tailored to user needs.

[0065] The service provider provides advice based on the results analyzed by the analysis unit. This advice may be provided in various ways, including, but are not limited to, text-based advice, voice-based advice, and real-time advice. Specifically, the service provider offers text-based advice, which sends specific answers or suggestions to the user's message as a text message. Voice-based advice is delivered to the user via a speaker. Voice-based advice utilizes speech synthesis technology, providing the user with a voice message generated based on the analysis results. Real-time advice is provided immediately after the user sends a message. Real-time advice employs an instant response system, quickly generating and delivering advice based on the analysis results. For example, the service provider sends specific advice to the user as a text message based on the analysis results. Voice-based advice is delivered to the user via a speaker. Real-time advice is provided immediately after the user sends a message. This allows the service provider to quickly provide appropriate advice to the user and improve their communication skills. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. This allows the service provider to consistently offer users the best possible advice and improve the overall system performance.

[0066] The analysis unit may include an identification unit that identifies the user's strengths and areas for improvement. The identification unit may, for example, provide positive feedback. It may also extract success stories. Furthermore, it may provide negative feedback. For example, the identification unit may extract positive elements from the user's message and provide them as feedback. Extracting success stories identifies instances where the user has succeeded in the past and provides them as feedback. Negative feedback extracts areas for improvement from the user's message and provides them as specific advice. This allows for more specific feedback by identifying the user's strengths and areas for improvement. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit may input the user's message into AI, which can then identify positive and negative elements.

[0067] The service provider may include an advice unit that provides advice on how to initiate a conversation. The advice unit may, for example, advise on appropriate language use. It may also advise on topic selection. Furthermore, it may advise on timing. For example, the advice unit may suggest appropriate language to the user. Topic selection advice will provide guidance on how to choose an appropriate topic when initiating a conversation. Timing advice will suggest the optimal time for the user to initiate a conversation. In this way, by providing advice on how to initiate a conversation, the user's communication skills can be improved. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit may input the user's message into AI, which may then suggest appropriate language use and topic selection.

[0068] The reception desk can receive messages from users as they would in a regular messaging app. For example, it can receive messages in a chat format. It can also receive real-time messaging. Furthermore, it can receive messages containing stamps and emojis. For example, the reception desk receives chat-style messages sent by users and inputs them into the system. Real-time messaging is received immediately after the user sends the message. Messages containing stamps and emojis are received when sent by users. This allows for natural communication by enabling users to speak as they would in a regular messaging app. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user messages into an AI, which can then analyze the message content.

[0069] The service provider can provide specific advice to improve users' communication skills. For example, the service provider can provide specific action guidelines. The service provider can also provide improvement measures. The service provider can also introduce success stories. For example, the service provider can propose specific action guidelines to users. Improvement measures would specifically indicate areas where users should improve and suggest methods for improvement. Introducing success stories would provide successful examples that users can refer to. In this way, by providing specific advice, users' communication skills can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's message into AI, and the AI ​​can suggest specific action guidelines or improvement measures.

[0070] The reception unit can estimate the user's emotions and adjust the timing of message reception based on the estimated emotions. For example, if the user is stressed, the reception unit may delay message reception. Conversely, if the user is relaxed, the reception unit may accept the message immediately. Furthermore, if the user is in a hurry, the reception unit may prioritize message reception. For example, if the user is stressed, the AI ​​will delay message reception and wait until the user is relaxed. If the user is relaxed, the AI ​​will accept the message immediately and respond quickly. If the user is in a hurry, the AI ​​will prioritize message reception and begin analysis immediately. This allows for a more appropriate response by adjusting the timing of message reception 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 reception unit may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into an AI, which can then estimate the emotion and adjust the timing of the reception.

[0071] The reception unit can analyze the user's past message history and select the optimal reception method. For example, the reception unit can prioritize receiving messages in formats that the user has frequently used in the past. It can also adjust reception to match specific time periods if the user has previously sent messages during those times. Furthermore, the reception unit can prioritize receiving messages related to specific topics based on the user's past message history. For example, the reception unit can prioritize receiving messages in formats that the user has frequently used in the past. It can adjust reception to match specific time periods if the user has previously sent messages during those times. It can prioritize receiving messages related to specific topics based on the user's past message history. This allows the reception unit to select the optimal reception method by analyzing past message history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past message history into AI, which can then select the optimal reception method.

[0072] The reception system can filter messages based on the user's current situation and areas of interest when receiving them. For example, if the user is at work, the reception system will prioritize receiving work-related messages. Similarly, if the user is on vacation, the reception system can prioritize receiving relaxing messages. Furthermore, the reception system can filter and receive relevant messages based on the user's areas of interest. For example, if the user is at work, the reception system will prioritize receiving work-related messages. If the user is on vacation, the reception system will prioritize receiving relaxing messages. It can also filter and receive relevant messages based on the user's areas of interest. This allows the reception system to prioritize receiving highly relevant messages by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the user's current situation and areas of interest into the AI, which can then perform the filtering.

[0073] The reception desk can estimate the user's emotions and determine the priority of messages to receive based on the estimated emotions. For example, if the user is stressed, the reception desk may postpone less important messages. If the user is relaxed, the reception desk may accept all messages equally. If the user is in a hurry, the reception desk may prioritize urgent messages. For example, if the user is stressed, the reception desk will postpone less important messages. If the user is relaxed, it will accept all messages equally. If the user is in a hurry, it will prioritize urgent messages. This allows for a more appropriate response by prioritizing messages based on 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 reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into the AI, which can then estimate the emotion and determine the priority of messages.

[0074] The reception unit can prioritize receiving messages that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception unit will prioritize receiving messages related to that location. It can also prioritize receiving messages related to the user's travel destination if the user is traveling. Furthermore, it can prioritize receiving messages related to the user's home if the user is at home. This allows the reception unit to prioritize receiving highly relevant messages by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location into the AI, which can then select highly relevant messages.

[0075] The reception unit can analyze the user's social media activity when receiving a message and receive relevant messages. For example, the reception unit can prioritize receiving messages related to topics the user is discussing on social media. It can also prioritize receiving messages related to accounts the user follows on social media. Furthermore, the reception unit can receive messages according to the user's social media activity schedule. For example, the reception unit can prioritize receiving messages related to topics the user is discussing on social media. It can prioritize receiving messages related to accounts the user follows on social media. It can receive messages according to the user's social media activity schedule. This allows the reception unit to prioritize receiving relevant messages by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's social media activity into AI, which can then select relevant messages.

[0076] The analysis unit can estimate the user's emotions and adjust the message analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can simplify the message analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. If the user is in a hurry, the analysis unit can perform a rapid analysis. For example, if the user is stressed, the AI ​​simplifies the message analysis and extracts only the important points. If the user is relaxed, the AI ​​performs a detailed analysis and extracts all points. If the user is in a hurry, the AI ​​performs a rapid analysis and provides results immediately. This allows for more appropriate analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, which can then estimate the emotion and adjust the analysis method.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the message during the analysis. For example, the analysis unit can analyze high-importance messages in detail, and low-importance messages in a simplified manner. It can also analyze urgent messages quickly. For example, the analysis unit can analyze high-importance messages in detail and extract all points, analyze low-importance messages in a simplified manner and extract only the main points, and quickly analyze urgent messages and provide results immediately. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the message. 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 importance of the message into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, the analysis unit can apply a business analysis algorithm to work-related messages. It can also apply a casual analysis algorithm to private messages. Furthermore, it can apply a rapid analysis algorithm to urgent messages. For example, the analysis unit can apply a business analysis algorithm to work-related messages, a casual analysis algorithm to private messages, and a rapid analysis algorithm to urgent messages. By applying different analysis algorithms depending on the message category, more appropriate analysis becomes possible. 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 message category into the AI, and the AI ​​can select an appropriate analysis algorithm.

[0079] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing messages of high importance. If the user is relaxed, the analysis unit can also analyze all messages equally. If the user is in a hurry, the analysis unit can also prioritize analyzing messages of high urgency. For example, if the user is stressed, the analysis unit will prioritize analyzing messages of high importance. If the user is relaxed, it will analyze all messages equally. If the user is in a hurry, it will prioritize analyzing messages of high urgency. This allows for more appropriate analysis by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, which can then estimate emotions and determine the priority of the analysis.

[0080] The analysis unit can determine the priority of analysis based on the submission date of messages during analysis. For example, the analysis unit may prioritize the analysis of recently submitted messages. It can also postpone the analysis of older messages. Furthermore, it can prioritize the analysis of urgent messages regardless of their submission date. For example, the analysis unit may prioritize the analysis of recently submitted messages, postpone older messages, and prioritize the analysis of urgent messages regardless of their submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of messages. 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 submission dates of messages into the AI, and the AI ​​can determine the priority of analysis.

[0081] The analysis unit can adjust the order of analysis based on the relevance of messages during analysis. For example, the analysis unit may prioritize analyzing highly relevant messages. It can also postpone analyzing less relevant messages. Furthermore, it can prioritize analyzing urgent messages regardless of their relevance. For example, the analysis unit may prioritize analyzing highly relevant messages, postpone analyzing less relevant messages, and prioritize analyzing urgent messages regardless of their relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of messages. 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 relevance of messages into the AI, which can then adjust the order of analysis.

[0082] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is stressed, the service provider will provide advice in gentle language. If the user is relaxed, the service provider can also provide detailed advice. If the user is in a hurry, the service provider can also provide concise and quick advice. For example, if the user is stressed, the service provider will provide advice in gentle language. If the user is relaxed, it will provide detailed advice. If the user is in a hurry, it will provide concise and quick advice. This allows for more appropriate advice by adjusting the way advice is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI, which can then estimate emotions and adjust the way advice is expressed.

[0083] The service provider can provide optimal advice by referring to the user's past behavior history when providing advice. For example, the service provider can provide optimal advice based on advice the user has received in the past. The service provider can also extract and provide effective advice from the user's past behavior history. Furthermore, the service provider can analyze the user's past behavior history and provide the most appropriate advice. For example, the service provider can provide optimal advice based on advice the user has received in the past. It can extract and provide effective advice from the user's past behavior history. It can analyze the user's past behavior history and provide the most appropriate advice. In this way, optimal advice can be provided by referring to the user's past behavior history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past behavior history into AI, and the AI ​​can select the optimal advice.

[0084] The service provider can customize the content of advice based on the user's current situation when providing advice. For example, if the user is at work, the service provider will provide work-related advice. If the user is on vacation, the service provider can also provide advice that promotes relaxation. The service provider can also customize and provide the most appropriate advice based on the user's current situation. For example, if the user is at work, the service provider will provide work-related advice. If the user is on vacation, the service provider will provide advice that promotes relaxation. The service provider can customize and provide the most appropriate advice based on the user's current situation. This allows for more appropriate advice by customizing the content of the advice 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 not using AI. For example, the service provider can input the user's current situation into AI, and the AI ​​can customize the content of the advice.

[0085] The service provider can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, the service provider will prioritize providing high-importance advice. If the user is relaxed, the service provider can also provide all advice equally. If the user is in a hurry, the service provider can also prioritize providing urgent advice. For example, if the user is stressed, the service provider will prioritize providing high-importance advice. If the user is relaxed, all advice will be provided equally. If the user is in a hurry, urgent advice will be prioritized. This allows for more appropriate advice by prioritizing advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into the AI, which can then estimate the emotion and determine the priority of advice.

[0086] The service provider can provide optimal advice by considering the user's geographical location. For example, if the user is in a specific location, the service provider can provide advice relevant to that location. If the user is traveling, the service provider can also provide advice relevant to the travel destination. If the user is at home, the service provider can also provide advice relevant to the home. For example, if the service provider is in a specific location, the service provider can provide advice relevant to that location. If the user is traveling, the service provider can provide advice relevant to the travel destination. If the user is at home, the service provider can provide advice relevant to the home. This allows the service provider to provide highly relevant advice by considering the user's geographical location. 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 the user's geographical location into AI, and the AI ​​can select the optimal advice.

[0087] The service provider can analyze the user's social media activity and adjust the content of the advice when providing it. For example, the service provider can provide advice related to topics the user is talking about on social media. It can also provide advice related to accounts the user follows on social media. Furthermore, the service provider can provide advice tailored to the user's social media activity time. For example, the service provider can provide advice related to topics the user is talking about on social media. It can provide advice related to accounts the user follows on social media. It can provide advice tailored to the user's social media activity time. By analyzing the user's social media activity, it can provide highly relevant advice. 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 the user's social media activity into AI, and the AI ​​can adjust the content of the advice.

[0088] The identification unit can estimate the user's emotions and adjust the method of identifying strengths and areas for improvement based on the estimated emotions. For example, if the user is stressed, the identification unit will point out strengths and areas for improvement in gentle terms. The identification unit can also provide detailed feedback if the user is relaxed. The identification unit can also provide concise and quick feedback if the user is in a hurry. For example, if the identification unit is stressed, it will point out strengths and areas for improvement in gentle terms. If the user is relaxed, it will provide detailed feedback. If the user is in a hurry, it will provide concise and quick feedback. By adjusting the identification method based on the user's emotions, more appropriate feedback becomes possible. 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 identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input user emotion data into the AI, which can then adjust the method for estimating and identifying emotions.

[0089] The identification unit can improve the accuracy of identification by referring to the user's past behavior history at the time of identification. For example, the identification unit can improve the accuracy of identification based on feedback the user has received in the past. The identification unit can also extract and provide effective feedback from the user's past behavior history. The identification unit can also analyze the user's past behavior history and provide the most appropriate feedback. For example, the identification unit can improve the accuracy of identification based on feedback the user has received in the past. It can extract and provide effective feedback from the user's past behavior history. It can analyze the user's past behavior history and provide the most appropriate feedback. In this way, the accuracy of identification can be improved by referring to the user's past behavior history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's past behavior history into AI, and the AI ​​can improve the accuracy of identification.

[0090] The specific unit can estimate the user's emotions and determine specific priorities based on the estimated emotions. For example, if the user is stressed, the specific unit will prioritize providing high-importance feedback. If the user is relaxed, the specific unit can also provide all feedback equally. If the user is in a hurry, the specific unit can also prioritize providing urgent feedback. For example, if the user is stressed, the specific unit will prioritize providing high-importance feedback. If the user is relaxed, all feedback will be provided equally. If the user is in a hurry, urgent feedback will be prioritized. This allows for more appropriate feedback by determining specific priorities based on 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 specific unit may be performed using AI, for example, or without AI. For example, the specific unit can input user emotion data into the AI, which can then estimate the emotion and determine specific priorities.

[0091] The identification unit can improve the accuracy of identification by considering the user's geographical location information at the time of identification. For example, if the user is in a specific location, the identification unit provides feedback related to that location. The identification unit can also provide feedback related to the travel destination if the user is traveling. The identification unit can also provide feedback related to the home if the user is at home. For example, if the identification unit is in a specific location, it provides feedback related to that location. If the user is traveling, it provides feedback related to the travel destination. If the user is at home, it provides feedback related to the home. This improves the accuracy of identification by considering the user's geographical location information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's geographical location information into AI, which can then improve the accuracy of identification.

[0092] The advice unit can estimate the user's emotions and adjust the content of the advice based on the estimated emotions. For example, if the user is stressed, the advice unit will provide advice in gentle language. If the user is relaxed, the advice unit can also provide detailed advice. If the user is in a hurry, the advice unit can also provide concise and quick advice. For example, if the user is stressed, the advice unit will provide advice in gentle language. If the user is relaxed, it will provide detailed advice. If the user is in a hurry, it will provide concise and quick advice. This allows for more appropriate advice by adjusting the content of the advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input user emotion data into an AI, which can then estimate the emotions and adjust the content of the advice.

[0093] The advice unit can provide optimal advice by referring to the user's past behavior history when providing advice. For example, the advice unit can provide optimal advice based on advice the user has received in the past. The advice unit can also extract and provide effective advice from the user's past behavior history. Furthermore, the advice unit can analyze the user's past behavior history and provide the most appropriate advice. For example, the advice unit can provide optimal advice based on advice the user has received in the past. It can extract and provide effective advice from the user's past behavior history. It can analyze the user's past behavior history and provide the most appropriate advice. In this way, optimal advice can be provided by referring to the user's past behavior history. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's past behavior history into AI, and the AI ​​can select the optimal advice.

[0094] The advice unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, the advice unit will prioritize providing high-importance advice. If the user is relaxed, the advice unit can also provide all advice equally. If the user is in a hurry, the advice unit can also prioritize providing urgent advice. For example, if the user is stressed, the advice unit will prioritize providing high-importance advice. If the user is relaxed, all advice will be provided equally. If the user is in a hurry, urgent advice will be prioritized. This allows for more appropriate advice by prioritizing advice based on 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 advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input user emotion data into the AI, which can then estimate the emotion and determine the priority of advice.

[0095] The advice unit can provide optimal advice by considering the user's geographical location when providing advice. For example, if the user is in a specific location, the advice unit can provide advice relevant to that location. If the user is traveling, the advice unit can also provide advice relevant to the travel destination. If the user is at home, the advice unit can also provide advice relevant to the home. For example, if the advice unit is in a specific location, it can provide advice relevant to that location. If the user is traveling, it can provide advice relevant to the travel destination. If the user is at home, it can provide advice relevant to the home. This allows the advice unit to provide highly relevant advice by considering the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's geographical location information into AI, and the AI ​​can select the optimal advice.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit can improve the accuracy of its analysis by referring to the user's past message history when analyzing a user's message. For example, the analysis unit can learn patterns from messages the user has sent in the past and incorporate them into the analysis of the current message. It can also extract specific keywords and phrases from the user's past messages and use them to identify important points in the current message. Furthermore, by analyzing the user's past message history and understanding the user's communication style and preferences, the analysis unit can provide more appropriate analysis results. This allows for improved analysis accuracy and more appropriate advice by utilizing the user's past message history.

[0098] The service provider can estimate the user's emotions and customize the content of the advice based on the estimated emotions. For example, if the user is stressed, the service provider can provide encouraging advice in gentle words. If the user is relaxed, the service provider can also provide detailed advice. Furthermore, if the user is in a hurry, the service provider can provide concise and quick advice. In this way, more appropriate advice can be provided by customizing the content of the advice based on the user's emotions. Emotion estimation can be achieved, for example, using an emotion engine or generative AI. Some or all of the above processing in the service provider may be performed using AI or not.

[0099] The reception unit can adjust how messages are received, taking into account the user's geographical location. For example, if a user is in a specific location, messages related to that location can be prioritized. Similarly, if a user is traveling, messages related to their travel destination can be prioritized. Furthermore, if a user is at home, messages related to their home can be prioritized. This allows for the prioritization of highly relevant messages by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not.

[0100] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, it can prioritize the analysis of high-importance messages. If the user is relaxed, it can analyze all messages equally. Furthermore, if the user is in a hurry, it can prioritize the analysis of urgent messages. By determining the priority of analysis based on the user's emotions, more appropriate analysis becomes possible. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0101] The service provider can customize the content of advice by referring to the user's past behavior history. For example, it can provide advice that is best suited to the current situation based on advice the user has received in the past. It can also extract and provide effective advice from the user's past behavior history. Furthermore, it can analyze the user's past behavior history and provide the most appropriate advice. In this way, more appropriate advice can be provided by referring to the user's past behavior history. Some or all of the above processing in the service provider may be performed using AI or not.

[0102] The reception unit can estimate the user's emotions and adjust the timing of message reception based on the estimated emotions. For example, if the user is stressed, message reception can be delayed. Conversely, if the user is relaxed, messages can be received immediately. Furthermore, if the user is in a hurry, messages can be given priority. This allows for more appropriate responses by adjusting the timing of message reception according to the user's emotions. Emotion estimation can be achieved, for example, using an emotion engine or generative AI. Some or all of the above processing in the reception unit may be performed using AI or not.

[0103] The analysis unit can apply different analysis algorithms depending on the message category. For example, a business analysis algorithm can be applied to work-related messages, a casual analysis algorithm to private messages, and a rapid analysis algorithm to urgent messages. By applying different analysis algorithms depending on the message category, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not.

[0104] The service provider can estimate the user's emotions and adjust the way advice is expressed based on those emotions. For example, if the user is stressed, the service provider can offer advice in gentle language. If the user is relaxed, the service provider can offer detailed advice. Furthermore, if the user is in a hurry, the service provider can offer concise and quick advice. By adjusting the way advice is expressed based on the user's emotions, more appropriate advice can be provided. Emotion estimation can be achieved, for example, using an emotion engine or generative AI. Some or all of the processing described above in the service provider may be performed using AI or not.

[0105] The reception desk can analyze a user's social media activity and prioritize receiving relevant messages. For example, it can prioritize receiving messages related to topics the user is discussing on social media. It can also prioritize receiving messages related to accounts the user follows on social media. Furthermore, it can prioritize receiving messages according to the user's social media activity schedule. In this way, by analyzing a user's social media activity, it is possible to prioritize receiving relevant messages. Some or all of the above processing in the reception desk may be performed using AI or not.

[0106] The identification unit can estimate the user's emotions and adjust the method of identifying strengths and areas for improvement based on the estimated emotions. For example, if the user is stressed, it can point out strengths and areas for improvement in gentle terms. If the user is relaxed, it can provide detailed feedback. Furthermore, if the user is in a hurry, it can provide concise and quick feedback. By adjusting the identification method based on the user's emotions, more appropriate feedback becomes possible. Emotion estimation can be achieved, for example, using an emotion engine or generative AI. Some or all of the processing described above in the identification unit may be performed using AI or not.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives user messages. User messages include text messages, voice messages, and image messages. For example, the reception desk receives text messages sent by users and inputs them into the system. Voice messages are recorded via a microphone and input into the system. Image messages are captured via a camera and input into the system. Step 2: The analysis unit analyzes the messages received by the reception unit. The analysis is performed using methods such as natural language processing, sentiment analysis, and keyword extraction. For example, the analysis unit uses natural language processing to analyze the content of the message, sentiment analysis to analyze the sentiment of the message, and keyword extraction to extract the important points of the message. Step 3: The service provider provides advice based on the results analyzed by the analysis unit. The advice is provided in various formats, such as text, audio, or real-time. For example, the service provider sends specific advice to the user as a text message based on the analysis results, audio advice is delivered to the user through a speaker, and real-time advice is provided immediately after the user sends a message.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives text messages, voice messages, and image messages from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the message using natural language processing techniques and sentiment analysis. The provision unit is implemented by the output device 40 of the smart device 14 and provides advice based on the analysis results. The reception unit, analysis unit, and provision unit may also be implemented by the specific processing unit 290 of the data processing unit 12, for example. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives text messages, voice messages, and image messages from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the message using natural language processing techniques and sentiment analysis. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides advice based on the analysis results. The reception unit, analysis unit, and provision unit may also be implemented by the specific processing unit 290 of the data processing unit 12. 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.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives text messages, voice messages, and image messages from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes messages using natural language processing techniques and sentiment analysis. The provision unit is implemented by the speaker 240 of the headset terminal 314 and provides advice based on the analysis results. The reception unit, analysis unit, and provision unit may also be implemented by the specific processing unit 290 of the data processing unit 12. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives text messages, voice messages, and image messages from the user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the message using natural language processing techniques and sentiment analysis. The provision unit is implemented, for example, by the speaker 240 of the robot 414 and provides advice based on the analysis results. The reception unit, analysis unit, and provision unit may also be implemented, for example, by the specific processing unit 290 of the data processing unit 12. 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A reception desk that receives user messages, An analysis unit that analyzes the message received by the aforementioned reception unit, The system includes a provisioning unit that provides advice based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, It includes a special unit that identifies the user's strengths and weaknesses and areas that need improvement. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, It includes an advice section that provides guidance on how to initiate conversations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It accepts messages from users, just like a regular messaging app. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide specific advice to improve users' communication skills. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of message delivery based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past message history and select the optimal method of receiving messages. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving messages, 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 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of messages to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving messages, the system prioritizes receiving messages that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a message, the system analyzes the user's social media activity and selects relevant messages. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the message analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the message category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the messages were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, we refer to the user's past behavioral history to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, customize the content of the advice based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, we analyze the user's social media activity and adjust the content of the advice accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 24) The specified part is, We estimate user emotions and adjust the method of identifying strengths and weaknesses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The specified part is, At specific times, referencing the user's past behavioral history improves the accuracy of certain actions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The specified part is, It estimates the user's emotions and determines specific priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The specified part is, At specific times, the system improves accuracy by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, It estimates the user's emotions and adjusts the content of the advice based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, When providing advice, we refer to the user's past behavioral history to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned advice section, When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives user messages, An analysis unit that analyzes the message received by the aforementioned reception unit, The system includes a provisioning unit that provides advice based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, It includes a special unit that identifies the user's strengths and weaknesses and areas that need improvement. The system according to feature 1.

3. The aforementioned supply unit is, It includes an advice section that provides guidance on how to initiate conversations. The system according to feature 1.

4. The aforementioned reception unit is It accepts messages from users, just like a regular messaging app. The system according to feature 1.

5. The aforementioned supply unit is, Provide specific advice to improve users' communication skills. The system according to feature 1.

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

7. The aforementioned reception unit is Analyze the user's past message history and select the optimal method of receiving messages. The system according to feature 1.

8. The aforementioned reception unit is When receiving messages, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

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

10. The aforementioned reception unit is When receiving messages, the system prioritizes receiving messages that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

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