system

Abstract

The system according to this embodiment aims to generate an appropriate reply message for the user in response to the content of the chat. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives the content of the message from the user. The analysis unit analyzes the content of the message received by the reception unit. The generation unit generates an optimal reply message based on the information analyzed by the analysis unit. The provision unit provides the reply message generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for a user to generate an appropriate reply message for the conversation content.

[0005] The system according to the embodiment aims to enable a user to generate an appropriate reply message for the conversation content.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives the content of a message from the user. The analysis unit analyzes the content of the message received by the reception unit. The generation unit generates an optimal reply message based on the information analyzed by the analysis unit. The provision unit provides the reply message generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows the user to generate an appropriate reply message in response to the content of a chat. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The reply message recommendation system according to an embodiment of the present invention is a system in which a generating AI reads the content of a conversation with the push of a button and recommends the most suitable reply message. When the user presses a button, the generating AI reads the content of the conversation. Next, the generating AI analyzes the content of past conversations and generates the most suitable reply message, taking into account information such as the relationship, the characteristics of the user's messages, and gender. This allows the user to build smooth communication without having trouble figuring out what to reply to. For example, if a user receives a message such as "How are you doing lately?" in a messaging app, the generating AI analyzes the message and generates the most suitable reply message, taking into account the content of past conversations and the relationship. For example, it might recommend a reply message such as "I've been busy lately, but I'm fine. How about you?" The generating AI analyzes the content of past conversations and also takes into account information such as the characteristics of the user's messages and gender. For example, if the user usually uses polite language, the generating AI will generate a reply message that reflects that characteristic. This system allows the user to build smooth communication without having trouble figuring out what to reply to. For example, it can also provide an appropriate reply to a message from someone the user dislikes, thus preventing the relationship from deteriorating. Furthermore, it helps avoid the risk of online backlash on social media, providing a safe and secure environment for using social media. This allows the reply message recommendation system to efficiently analyze user conversations and generate and provide optimal reply messages.

[0029] The reply message recommendation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives the content of a message from the user. For example, the reception unit receives the content of a message when the user presses a button. The reception unit can also support multiple input methods, such as voice input and text input. For example, the reception unit can use speech recognition technology to convert the user's voice into text and receive it as the content of the message. The analysis unit analyzes the content of the message received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the content of the message and extract important keywords and phrases. The analysis unit can also analyze the content of past conversations and consider information such as the characteristics of the user's messages and gender. For example, the analysis unit can obtain past conversation history from a database and compare it with the content of the message to identify the characteristics of the user's messages. The generation unit generates the optimal reply message based on the information analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate a reply message that corresponds to the content of the message. The generation AI can use text generation AI (e.g., LLM) to generate reply messages that take into account the characteristics and relationships of the user's messages. The generation unit can also use the generation AI to generate reply messages that take into account the user's gender and relationship. For example, if the generation unit usually uses polite language, it will generate a reply message that reflects that characteristic. The delivery unit provides the reply messages generated by the generation unit. The delivery unit can, for example, display the generated reply messages on the user's device. The delivery unit can also play the generated reply messages as audio. For example, the delivery unit can play the generated reply messages as audio using speech synthesis technology. As a result, the reply message recommendation system according to this embodiment can efficiently analyze the content of the user's conversation and generate and provide the optimal reply message.

[0030] The reception desk receives the content of conversations from users. For example, the reception desk can receive conversation content when the user presses a button. The reception desk can also support multiple input methods, such as voice input and text input. For example, the reception desk can use speech recognition technology to convert the user's voice into text and receive it as conversation content. Specifically, the speech recognition technology uses a deep learning-based speech model to transcribe the user's utterances into text with high accuracy. This allows users to easily input conversation content by voice without using their hands. In the case of text input, users can enter messages using a keyboard or touchscreen. Furthermore, the reception desk can provide appropriate feedback depending on the user's input method. For example, when using voice input, the results of speech recognition are displayed in real time so that the user can correct misrecognitions. When using text input, an input completion function is provided to help users enter messages quickly. In this way, the reception desk can respond to the diverse input needs of users and realize smooth reception of conversation content.

[0031] The analysis unit analyzes the content of conversations received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the conversation content and extract important keywords and phrases. Specifically, it performs morphological analysis on the conversation content to identify parts of speech such as nouns, verbs, and adjectives. Furthermore, it can use topic modeling technology to identify the subject of the conversation content. The analysis unit can also analyze the content of past conversations and consider information such as the characteristics of the user's messages and gender. For example, the analysis unit can retrieve past conversation history from a database and compare it with the conversation content to identify the characteristics of the user's messages. This allows it to understand the tone and style in which the user has sent messages in the past, enabling more appropriate analysis. Furthermore, the analysis unit can use sentiment analysis technology to analyze the emotions in the conversation content. This allows it to understand the user's current emotional state and provide information for generating appropriate reply messages. By combining these technologies, the analysis unit can analyze the conversation content from multiple angles and accurately grasp the user's intentions and emotions.

[0032] The generation unit generates the optimal reply message based on the information analyzed by the analysis unit. The generation unit can, for example, use a generation AI to generate a reply message that corresponds to the content of the conversation. The generation AI can use a text generation AI (e.g., LLM) to generate a reply message that takes into account the characteristics and relationships of the user's messages. Specifically, the generation AI receives the user's conversation content as input and creates a prompt to generate an appropriate reply message. The prompt includes the content of the user's message, past conversation history, and the user's characteristics. Based on this prompt, the generation AI generates a reply message in natural language. For example, if the user asks a question, the generation AI generates an appropriate answer to that question. Also, if the user expresses gratitude, the generation AI generates a response to that. The generation unit can also use the generation AI to generate a reply message that takes into account the user's gender and relationship. For example, if the generation unit usually uses polite language, it will generate a reply message that reflects that characteristic. In this way, the generation unit can generate and provide the user with the optimal reply message that corresponds to the content of the user's conversation.

[0033] The delivery unit provides the reply message generated by the generation unit. The delivery unit displays the generated reply message on the user's device, for example. Specifically, it displays the reply message on the user's smartphone or computer screen so that the user can check it immediately. The delivery unit can also play the generated reply message as audio. For example, the delivery unit uses speech synthesis technology to play the generated reply message as audio. The speech synthesis technology uses a deep learning-based voice model to achieve natural speech. This allows the user to check the reply message not only visually but also aurally. Furthermore, the delivery unit can select the appropriate delivery method depending on the user's device. For example, it displays a short message to users using a smartwatch and plays the message as audio to users using a smart speaker. This allows the delivery unit to provide the reply message in the most optimal way according to the user's device and situation, improving user convenience.

[0034] The analysis unit can analyze the content of past conversations. For example, the analysis unit can retrieve past conversation history from a database and compare it with the talk content to identify the characteristics of the user's messages. The analysis unit needs to clearly define the scope and time period of past conversations. For example, it can analyze conversations from the last month or the last year. By analyzing the content of past conversations, it is possible to generate more appropriate reply 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 past conversation history into AI and have the AI ​​perform the process of identifying the characteristics of the user's messages.

[0035] The analysis unit can analyze the characteristics of a user's message. For example, the analysis unit can analyze the content of the conversation using natural language processing technology and extract important keywords and phrases. By analyzing the characteristics of a user's message, the analysis unit can generate individually optimized reply messages. It is necessary to clarify the specific content of the characteristics and the method of analysis. For example, style, keywords, and emotions can be analyzed. This allows for the generation of individually optimized reply messages by analyzing the characteristics of a user's 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 characteristics of the user's message into AI and have AI perform the analysis of the characteristics.

[0036] The analysis unit can take the user's gender into consideration. For example, the analysis unit can obtain the user's gender from their profile information and reflect it in the analysis of the chat content. By taking the user's gender into consideration, the analysis unit can generate more appropriate reply messages. It is necessary to clarify the specific methods and criteria for considering gender. For example, expressions for men and expressions for women can be considered. This allows for the generation of more appropriate reply messages by taking the user's gender into consideration. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's gender information into AI and have AI perform analysis that takes gender into consideration.

[0037] The generation unit can generate the most appropriate reply message by considering the relationship. For example, the generation unit can use a generation AI to generate a reply message that corresponds to the content of the conversation. The generation AI can use a text generation AI (e.g., LLM) to generate a reply message that takes into account the characteristics and relationship of the user's message. The generation unit needs to clarify the specific methods and criteria for considering the relationship. For example, it can consider closeness, business relationships, etc. This allows for the generation of more appropriate reply messages by considering the relationship. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can have the generation AI perform the generation of a reply message that takes the relationship into consideration.

[0038] The service provider can provide the generated reply message to the user. For example, the service provider can display the generated reply message on the user's device. The service provider can also play the generated reply message as audio. The specific method and means of provision must be clearly defined. For example, text messages, voice messages, etc., can be provided. This enables smooth communication by providing the user with the generated reply message. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated reply message into AI and have the AI ​​perform the processing of providing it to the user.

[0039] The reception desk can analyze the user's past chat history and select the most suitable reception method. For example, the reception desk can prioritize reception methods (voice, text, etc.) for chat content that the user has frequently used in the past. The reception desk can also suggest the most suitable reception method for a specific time of day based on the user's past chat history. Furthermore, the reception desk can analyze the user's past chat history and select the most suitable reception method for a specific topic. In this way, the reception desk can select the most suitable reception method by analyzing the user's past chat history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past chat history into an AI and have the AI ​​select the most suitable reception method.

[0040] The reception desk can filter incoming messages based on the user's current situation and areas of interest. For example, it can prioritize receiving messages that are highly relevant based on the user's current situation (e.g., working, on vacation). It can also filter relevant messages based on the user's areas of interest (e.g., sports, music). Furthermore, it can select appropriate messages based on the user's current activity (e.g., exercising, reading). By filtering messages based on the user's current situation and areas of interest, it can prioritize receiving messages that are highly relevant. 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 data on the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0041] The reception desk can prioritize receiving conversation content that is highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving conversation content related to that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving conversation content related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving conversation content related to their home. This allows the reception desk to prioritize receiving highly relevant conversation content by considering the user's geographical location. 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 the user's geographical location information into the AI ​​and have the AI ​​select highly relevant conversation content.

[0042] The reception unit can analyze the user's social media activity when receiving talk content and accept relevant talk content. For example, the reception unit can prioritize accepting talk content related to topics the user has recently shown interest in on social media. The reception unit can also consider the user's social media friendships and accept relevant talk content accordingly. Furthermore, the reception unit can analyze the user's social media posts and accept relevant talk content accordingly. In this way, by analyzing the user's social media activity, it is possible to prioritize accepting relevant talk content. 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 data into AI and have the AI ​​select relevant talk content.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the talk content during the analysis. For example, the analysis unit will perform a detailed analysis on important talk content. The analysis unit can also perform a simplified analysis on less important talk content. Furthermore, the analysis unit can perform a rapid analysis on talk content that is of high urgency. In this way, by adjusting the level of detail of the analysis based on the importance of the talk content, a detailed analysis can be performed on important talk content. 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 talk content importance data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the talk content during analysis. For example, the analysis unit can apply a business-specific analysis algorithm to business-related talk content. It can also apply a private-use analysis algorithm to private-use talk content. Furthermore, it can apply an entertainment-use analysis algorithm to entertainment-related talk content. By applying different analysis algorithms depending on the category of the talk content, more appropriate analysis results can be provided. 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 category data of the talk content into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the talk content during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted talk content. The analysis unit can also prioritize the analysis of talk content with high urgency. Furthermore, the analysis unit can also determine the priority of analysis based on a deadline specified by the user. This allows for the prioritization of analysis of talk content with high urgency by determining the priority of analysis based on the submission date of the talk content. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input talk content submission date data into AI and have the AI ​​perform the determination of analysis priorities.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the talk content during analysis. For example, the analysis unit may prioritize analyzing talk content that is highly relevant. It can also postpone analyzing talk content that is less relevant. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the talk content. This allows for prioritizing the analysis of highly relevant talk content by adjusting the order of analysis based on the relevance of the talk content. 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 talk content relevance data into AI and have AI perform the adjustment of the analysis order.

[0047] The generation unit can adjust the level of detail in reply messages based on the importance of the conversation content during generation. For example, the generation unit generates detailed reply messages for important conversation content. It can also generate simplified reply messages for less important conversation content. Furthermore, it can quickly generate reply messages for urgent conversation content. In this way, by adjusting the level of detail in reply messages based on the importance of the conversation content, it is possible to generate detailed reply messages for important conversation content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input importance data of the conversation content into a generation AI and have the generation AI perform the adjustment of the level of detail in the reply messages.

[0048] The generation unit can apply different generation algorithms depending on the category of the talk content during generation. For example, the generation unit can apply a business-specific generation algorithm to business-related talk content. It can also apply a private-specific generation algorithm to private talk content. Furthermore, it can apply an entertainment-specific generation algorithm to entertainment-related talk content. By applying different generation algorithms depending on the category of the talk content, it is possible to generate more appropriate reply messages. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input talk content category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0049] The generation unit can determine the priority of reply messages based on the submission date of the talk content during generation. For example, the generation unit will quickly generate reply messages for recently submitted talk content. The generation unit can also prioritize the generation of reply messages for urgent talk content. Furthermore, the generation unit can also determine the priority of reply messages based on a deadline specified by the user. This allows for the rapid generation of reply messages for urgent talk content by determining the priority of reply messages based on the submission date of the talk content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input talk content submission date data into the generation AI and have the generation AI determine the priority of reply messages.

[0050] The generation unit can adjust the order of reply messages based on the relevance of the conversation content during generation. For example, the generation unit will prioritize generating reply messages for highly relevant conversation content. It can also postpone generating reply messages for less relevant conversation content. Furthermore, the generation unit can dynamically adjust the order of reply messages based on the relevance of the conversation content. This allows for the priority generation of reply messages for highly relevant conversation content by adjusting the order of reply messages based on the relevance of the conversation content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input conversation content relevance data into a generation AI and have the generation AI perform the adjustment of the order of reply messages.

[0051] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has previously preferred. The service provider can also suggest the optimal display method for a specific time period based on the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and select the optimal display method for a specific topic. This allows the service provider to select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past operation history data into AI and have the AI ​​select the optimal display method.

[0052] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI and have the AI ​​select the optimal display method.

[0053] The service provider can make suggestions based on the user's schedule by referring to the user's calendar information at the time of delivery. For example, the service provider can refer to the schedule registered in the user's calendar and automatically set a reply message. The service provider can also suggest a reply message related to a specific event from the user's calendar information. Furthermore, the service provider can suggest the most appropriate reply message based on the schedule, based on the user's calendar information. In this way, by referring to the user's calendar information, the service provider can make optimal suggestions based on the schedule. 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 calendar information into AI and have the AI ​​execute suggestions based on the schedule.

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

[0055] The reception desk can analyze the user's past chat history and select the most suitable reception method. For example, the reception desk can prioritize reception methods (voice, text, etc.) for chat content that the user has frequently used in the past. The reception desk can also suggest the most suitable reception method for a specific time of day based on the user's past chat history. Furthermore, the reception desk can analyze the user's past chat history and select the most suitable reception method for a specific topic. In this way, the reception desk can select the most suitable reception method by analyzing the user's past chat history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past chat history into an AI and have the AI ​​select the most suitable reception method.

[0056] The reception desk can filter incoming messages based on the user's current situation and areas of interest. For example, it can prioritize receiving messages that are highly relevant based on the user's current situation (e.g., working, on vacation). It can also filter relevant messages based on the user's areas of interest (e.g., sports, music). Furthermore, it can select appropriate messages based on the user's current activity (e.g., exercising, reading). By filtering messages based on the user's current situation and areas of interest, it can prioritize receiving messages that are highly relevant. 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 data on the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0057] The reception desk can prioritize receiving conversation content that is highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving conversation content related to that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving conversation content related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving conversation content related to their home. This allows the reception desk to prioritize receiving highly relevant conversation content by considering the user's geographical location. 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 the user's geographical location information into the AI ​​and have the AI ​​select highly relevant conversation content.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the talk content during the analysis. For example, the analysis unit will perform a detailed analysis on important talk content. The analysis unit can also perform a simplified analysis on less important talk content. Furthermore, the analysis unit can perform a rapid analysis on talk content that is of high urgency. In this way, by adjusting the level of detail of the analysis based on the importance of the talk content, a detailed analysis can be performed on important talk content. 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 talk content importance data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0059] The analysis unit can apply different analysis algorithms depending on the category of the talk content during analysis. For example, the analysis unit can apply a business-specific analysis algorithm to business-related talk content. It can also apply a private-use analysis algorithm to private-use talk content. Furthermore, it can apply an entertainment-use analysis algorithm to entertainment-related talk content. By applying different analysis algorithms depending on the category of the talk content, more appropriate analysis results can be provided. 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 category data of the talk content into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0060] The analysis unit can determine the priority of analysis based on the submission date of the talk content during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted talk content. The analysis unit can also prioritize the analysis of talk content with high urgency. Furthermore, the analysis unit can also determine the priority of analysis based on a deadline specified by the user. This allows for the prioritization of analysis of talk content with high urgency by determining the priority of analysis based on the submission date of the talk content. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input talk content submission date data into AI and have the AI ​​perform the determination of analysis priorities.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives the message from the user. The reception desk accepts the message when the user presses a button, and also supports multiple input methods such as voice input and text input. For example, it can use speech recognition technology to convert the user's voice into text and accept it as the message. Step 2: The analysis unit analyzes the content of the conversation received by the reception unit. The analysis unit uses natural language processing technology to analyze the conversation content and extract important keywords and phrases. It can also analyze the content of past conversations and take into account information such as the characteristics of the user's messages and gender. For example, it can retrieve past conversation history from a database and compare it with the conversation content to identify the characteristics of the user's messages. Step 3: The generation unit generates the optimal reply message based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate a reply message that corresponds to the content of the conversation. The generation AI can use a text generation AI (e.g., LLM) to generate a reply message that takes into account the characteristics and relationships of the user's messages. It can also generate a reply message that takes into account the user's gender and relationship. For example, if the user usually uses polite language, the generation unit will generate a reply message that reflects that characteristic. Step 4: The providing unit provides the reply message generated by the generating unit. The providing unit can display the generated reply message on the user's device and can also play it back as audio. For example, it can use speech synthesis technology to play back the generated reply message as audio.

[0063] (Example of form 2) The reply message recommendation system according to an embodiment of the present invention is a system in which a generating AI reads the content of a conversation with the push of a button and recommends the most suitable reply message. When the user presses a button, the generating AI reads the content of the conversation. Next, the generating AI analyzes the content of past conversations and generates the most suitable reply message, taking into account information such as the relationship, the characteristics of the user's messages, and gender. This allows the user to build smooth communication without having trouble figuring out what to reply to. For example, if a user receives a message such as "How are you doing lately?" in a messaging app, the generating AI analyzes the message and generates the most suitable reply message, taking into account the content of past conversations and the relationship. For example, it might recommend a reply message such as "I've been busy lately, but I'm fine. How about you?" The generating AI analyzes the content of past conversations and also takes into account information such as the characteristics of the user's messages and gender. For example, if the user usually uses polite language, the generating AI will generate a reply message that reflects that characteristic. This system allows the user to build smooth communication without having trouble figuring out what to reply to. For example, it can also provide an appropriate reply to a message from someone the user dislikes, thus preventing the relationship from deteriorating. Furthermore, it helps avoid the risk of online backlash on social media, providing a safe and secure environment for using social media. This allows the reply message recommendation system to efficiently analyze user conversations and generate and provide optimal reply messages.

[0064] The reply message recommendation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives the content of a message from the user. For example, the reception unit receives the content of a message when the user presses a button. The reception unit can also support multiple input methods, such as voice input and text input. For example, the reception unit can use speech recognition technology to convert the user's voice into text and receive it as the content of the message. The analysis unit analyzes the content of the message received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the content of the message and extract important keywords and phrases. The analysis unit can also analyze the content of past conversations and consider information such as the characteristics of the user's messages and gender. For example, the analysis unit can obtain past conversation history from a database and compare it with the content of the message to identify the characteristics of the user's messages. The generation unit generates the optimal reply message based on the information analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate a reply message that corresponds to the content of the message. The generation AI can use text generation AI (e.g., LLM) to generate reply messages that take into account the characteristics and relationships of the user's messages. The generation unit can also use the generation AI to generate reply messages that take into account the user's gender and relationship. For example, if the generation unit usually uses polite language, it will generate a reply message that reflects that characteristic. The delivery unit provides the reply messages generated by the generation unit. The delivery unit can, for example, display the generated reply messages on the user's device. The delivery unit can also play the generated reply messages as audio. For example, the delivery unit can play the generated reply messages as audio using speech synthesis technology. As a result, the reply message recommendation system according to this embodiment can efficiently analyze the content of the user's conversation and generate and provide the optimal reply message.

[0065] The reception desk receives the content of conversations from users. For example, the reception desk can receive conversation content when the user presses a button. The reception desk can also support multiple input methods, such as voice input and text input. For example, the reception desk can use speech recognition technology to convert the user's voice into text and receive it as conversation content. Specifically, the speech recognition technology uses a deep learning-based speech model to transcribe the user's utterances into text with high accuracy. This allows users to easily input conversation content by voice without using their hands. In the case of text input, users can enter messages using a keyboard or touchscreen. Furthermore, the reception desk can provide appropriate feedback depending on the user's input method. For example, when using voice input, the results of speech recognition are displayed in real time so that the user can correct misrecognitions. When using text input, an input completion function is provided to help users enter messages quickly. In this way, the reception desk can respond to the diverse input needs of users and realize smooth reception of conversation content.

[0066] The analysis unit analyzes the content of conversations received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the conversation content and extract important keywords and phrases. Specifically, it performs morphological analysis on the conversation content to identify parts of speech such as nouns, verbs, and adjectives. Furthermore, it can use topic modeling technology to identify the subject of the conversation content. The analysis unit can also analyze the content of past conversations and consider information such as the characteristics of the user's messages and gender. For example, the analysis unit can retrieve past conversation history from a database and compare it with the conversation content to identify the characteristics of the user's messages. This allows it to understand the tone and style in which the user has sent messages in the past, enabling more appropriate analysis. Furthermore, the analysis unit can use sentiment analysis technology to analyze the emotions in the conversation content. This allows it to understand the user's current emotional state and provide information for generating appropriate reply messages. By combining these technologies, the analysis unit can analyze the conversation content from multiple angles and accurately grasp the user's intentions and emotions.

[0067] The generation unit generates the optimal reply message based on the information analyzed by the analysis unit. The generation unit can, for example, use a generation AI to generate a reply message that corresponds to the content of the conversation. The generation AI can use a text generation AI (e.g., LLM) to generate a reply message that takes into account the characteristics and relationships of the user's messages. Specifically, the generation AI receives the user's conversation content as input and creates a prompt to generate an appropriate reply message. The prompt includes the content of the user's message, past conversation history, and the user's characteristics. Based on this prompt, the generation AI generates a reply message in natural language. For example, if the user asks a question, the generation AI generates an appropriate answer to that question. Also, if the user expresses gratitude, the generation AI generates a response to that. The generation unit can also use the generation AI to generate a reply message that takes into account the user's gender and relationship. For example, if the generation unit usually uses polite language, it will generate a reply message that reflects that characteristic. In this way, the generation unit can generate and provide the user with the optimal reply message that corresponds to the content of the user's conversation.

[0068] The delivery unit provides the reply message generated by the generation unit. The delivery unit displays the generated reply message on the user's device, for example. Specifically, it displays the reply message on the user's smartphone or computer screen so that the user can check it immediately. The delivery unit can also play the generated reply message as audio. For example, the delivery unit uses speech synthesis technology to play the generated reply message as audio. The speech synthesis technology uses a deep learning-based voice model to achieve natural speech. This allows the user to check the reply message not only visually but also aurally. Furthermore, the delivery unit can select the appropriate delivery method depending on the user's device. For example, it displays a short message to users using a smartwatch and plays the message as audio to users using a smart speaker. This allows the delivery unit to provide the reply message in the most optimal way according to the user's device and situation, improving user convenience.

[0069] The analysis unit can analyze the content of past conversations. For example, the analysis unit can retrieve past conversation history from a database and compare it with the talk content to identify the characteristics of the user's messages. The analysis unit needs to clearly define the scope and time period of past conversations. For example, it can analyze conversations from the last month or the last year. By analyzing the content of past conversations, it is possible to generate more appropriate reply 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 past conversation history into AI and have the AI ​​perform the process of identifying the characteristics of the user's messages.

[0070] The analysis unit can analyze the characteristics of a user's message. For example, the analysis unit can analyze the content of the conversation using natural language processing technology and extract important keywords and phrases. By analyzing the characteristics of a user's message, the analysis unit can generate individually optimized reply messages. It is necessary to clarify the specific content of the characteristics and the method of analysis. For example, style, keywords, and emotions can be analyzed. This allows for the generation of individually optimized reply messages by analyzing the characteristics of a user's 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 characteristics of the user's message into AI and have AI perform the analysis of the characteristics.

[0071] The analysis unit can take the user's gender into consideration. For example, the analysis unit can obtain the user's gender from their profile information and reflect it in the analysis of the chat content. By taking the user's gender into consideration, the analysis unit can generate more appropriate reply messages. It is necessary to clarify the specific methods and criteria for considering gender. For example, expressions for men and expressions for women can be considered. This allows for the generation of more appropriate reply messages by taking the user's gender into consideration. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's gender information into AI and have AI perform analysis that takes gender into consideration.

[0072] The generation unit can generate the most appropriate reply message by considering the relationship. For example, the generation unit can use a generation AI to generate a reply message that corresponds to the content of the conversation. The generation AI can use a text generation AI (e.g., LLM) to generate a reply message that takes into account the characteristics and relationship of the user's message. The generation unit needs to clarify the specific methods and criteria for considering the relationship. For example, it can consider closeness, business relationships, etc. This allows for the generation of more appropriate reply messages by considering the relationship. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can have the generation AI perform the generation of a reply message that takes the relationship into consideration.

[0073] The service provider can provide the generated reply message to the user. For example, the service provider can display the generated reply message on the user's device. The service provider can also play the generated reply message as audio. The specific method and means of provision must be clearly defined. For example, text messages, voice messages, etc., can be provided. This enables smooth communication by providing the user with the generated reply message. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated reply message into AI and have the AI ​​perform the processing of providing it to the user.

[0074] The reception unit can estimate the user's emotions and adjust the timing of receiving the message content based on the estimated emotions. For example, if the user is stressed, the reception unit can delay receiving the message content and wait until the user is relaxed. Conversely, if the user is excited, the reception unit can immediately receive the message content and begin analysis quickly. Furthermore, if the user is tired, the reception unit can temporarily stop receiving the message content to allow the user time to rest. By adjusting the timing of receiving the message content according to the user's emotions, the message content can be received at a more appropriate time. 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 reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception desk can analyze the user's past chat history and select the most suitable reception method. For example, the reception desk can prioritize reception methods (voice, text, etc.) for chat content that the user has frequently used in the past. The reception desk can also suggest the most suitable reception method for a specific time of day based on the user's past chat history. Furthermore, the reception desk can analyze the user's past chat history and select the most suitable reception method for a specific topic. In this way, the reception desk can select the most suitable reception method by analyzing the user's past chat history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past chat history into an AI and have the AI ​​select the most suitable reception method.

[0076] The reception desk can filter incoming messages based on the user's current situation and areas of interest. For example, it can prioritize receiving messages that are highly relevant based on the user's current situation (e.g., working, on vacation). It can also filter relevant messages based on the user's areas of interest (e.g., sports, music). Furthermore, it can select appropriate messages based on the user's current activity (e.g., exercising, reading). By filtering messages based on the user's current situation and areas of interest, it can prioritize receiving messages that are highly relevant. 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 data on the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0077] The reception desk can estimate the user's emotions and determine the priority of the conversation content to be received based on the estimated emotions. For example, if the user is nervous, the reception desk will prioritize receiving important conversation content. Similarly, if the user is relaxed, the reception desk can prioritize receiving lighter conversation content. Furthermore, if the user is in a hurry, the reception desk can prioritize receiving conversation content that requires a quick response. This allows for prioritizing important conversation content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The reception desk can prioritize receiving conversation content that is highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving conversation content related to that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving conversation content related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving conversation content related to their home. This allows the reception desk to prioritize receiving highly relevant conversation content by considering the user's geographical location. 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 the user's geographical location information into the AI ​​and have the AI ​​select highly relevant conversation content.

[0079] The reception unit can analyze the user's social media activity when receiving talk content and accept relevant talk content. For example, the reception unit can prioritize accepting talk content related to topics the user has recently shown interest in on social media. The reception unit can also consider the user's social media friendships and accept relevant talk content accordingly. Furthermore, the reception unit can analyze the user's social media posts and accept relevant talk content accordingly. In this way, by analyzing the user's social media activity, it is possible to prioritize accepting relevant talk content. 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 data into AI and have the AI ​​select relevant talk content.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 a generative AI and have the generative AI perform emotion estimation.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the talk content during the analysis. For example, the analysis unit will perform a detailed analysis on important talk content. The analysis unit can also perform a simplified analysis on less important talk content. Furthermore, the analysis unit can perform a rapid analysis on talk content that is of high urgency. In this way, by adjusting the level of detail of the analysis based on the importance of the talk content, a detailed analysis can be performed on important talk content. 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 talk content importance data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the talk content during analysis. For example, the analysis unit can apply a business-specific analysis algorithm to business-related talk content. It can also apply a private-use analysis algorithm to private-use talk content. Furthermore, it can apply an entertainment-use analysis algorithm to entertainment-related talk content. By applying different analysis algorithms depending on the category of the talk content, more appropriate analysis results can be provided. 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 category data of the talk content into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can determine the priority of analysis based on the submission date of the talk content during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted talk content. The analysis unit can also prioritize the analysis of talk content with high urgency. Furthermore, the analysis unit can also determine the priority of analysis based on a deadline specified by the user. This allows for the prioritization of analysis of talk content with high urgency by determining the priority of analysis based on the submission date of the talk content. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input talk content submission date data into AI and have the AI ​​perform the determination of analysis priorities.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the talk content during analysis. For example, the analysis unit may prioritize analyzing talk content that is highly relevant. It can also postpone analyzing talk content that is less relevant. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the talk content. This allows for prioritizing the analysis of highly relevant talk content by adjusting the order of analysis based on the relevance of the talk content. 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 talk content relevance data into AI and have AI perform the adjustment of the analysis order.

[0086] The generation unit can estimate the user's emotions and adjust the expression of the reply message it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a relaxed reply message. If the user is in a hurry, the generation unit can also generate a concise and quick reply message. Furthermore, if the user is excited, the generation unit can generate a visually stimulating reply message. By adjusting the expression of the reply message according to the user's emotions, a more appropriate reply message can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0087] The generation unit can adjust the level of detail in reply messages based on the importance of the conversation content during generation. For example, the generation unit generates detailed reply messages for important conversation content. It can also generate simplified reply messages for less important conversation content. Furthermore, it can quickly generate reply messages for urgent conversation content. In this way, by adjusting the level of detail in reply messages based on the importance of the conversation content, it is possible to generate detailed reply messages for important conversation content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input importance data of the conversation content into a generation AI and have the generation AI perform the adjustment of the level of detail in the reply messages.

[0088] The generation unit can apply different generation algorithms depending on the category of the talk content during generation. For example, the generation unit can apply a business-specific generation algorithm to business-related talk content. It can also apply a private-specific generation algorithm to private talk content. Furthermore, it can apply an entertainment-specific generation algorithm to entertainment-related talk content. By applying different generation algorithms depending on the category of the talk content, it is possible to generate more appropriate reply messages. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input talk content category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0089] The generation unit can estimate the user's emotions and adjust the length of the reply message it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise reply message. If the user is relaxed, the generation unit can also generate a longer reply message with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a reply message with visually stimulating effects. By adjusting the length of the reply message according to the user's emotions, a more appropriate reply message can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0090] The generation unit can determine the priority of reply messages based on the submission date of the talk content during generation. For example, the generation unit will quickly generate reply messages for recently submitted talk content. The generation unit can also prioritize the generation of reply messages for urgent talk content. Furthermore, the generation unit can also determine the priority of reply messages based on a deadline specified by the user. This allows for the rapid generation of reply messages for urgent talk content by determining the priority of reply messages based on the submission date of the talk content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input talk content submission date data into the generation AI and have the generation AI determine the priority of reply messages.

[0091] The generation unit can adjust the order of reply messages based on the relevance of the conversation content during generation. For example, the generation unit will prioritize generating reply messages for highly relevant conversation content. It can also postpone generating reply messages for less relevant conversation content. Furthermore, the generation unit can dynamically adjust the order of reply messages based on the relevance of the conversation content. This allows for the priority generation of reply messages for highly relevant conversation content by adjusting the order of reply messages based on the relevance of the conversation content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input conversation content relevance data into a generation AI and have the generation AI perform the adjustment of the order of reply messages.

[0092] The service provider can estimate the user's emotions and adjust how the reply message is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display. If the user is relaxed, the service provider can also provide a display that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display. By adjusting how the reply message is displayed according to the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has previously preferred. The service provider can also suggest the optimal display method for a specific time period based on the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and select the optimal display method for a specific topic. This allows the service provider to select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past operation history data into AI and have the AI ​​select the optimal display method.

[0094] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI and have the AI ​​select the optimal display method.

[0095] The service provider can estimate the user's emotions and adjust the instructions for the reply message based on the estimated emotions. For example, if the user is nervous, the service provider can provide simple and intuitive instructions. If the user is relaxed, the service provider can also provide detailed instructions. Furthermore, if the user is in a hurry, the service provider can provide instructions that can be operated quickly. By adjusting the instructions for the reply message according to the user's emotions, more appropriate instructions can be provided. 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 or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The service provider can make suggestions based on the user's schedule by referring to the user's calendar information at the time of delivery. For example, the service provider can refer to the schedule registered in the user's calendar and automatically set a reply message. The service provider can also suggest a reply message related to a specific event from the user's calendar information. Furthermore, the service provider can suggest the most appropriate reply message based on the schedule, based on the user's calendar information. In this way, by referring to the user's calendar information, the service provider can make optimal suggestions based on the schedule. 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 calendar information into AI and have the AI ​​execute suggestions based on the schedule.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception unit can estimate the user's emotions and adjust the timing of receiving the message content based on the estimated emotions. For example, if the user is stressed, the reception unit can delay receiving the message content and wait until the user is relaxed. Conversely, if the user is excited, the reception unit can immediately receive the message content and begin analysis quickly. Furthermore, if the user is tired, the reception unit can temporarily stop receiving the message content to allow the user time to rest. By adjusting the timing of receiving the message content according to the user's emotions, the message content can be received at a more appropriate time. 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 reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The reception desk can analyze the user's past chat history and select the most suitable reception method. For example, the reception desk can prioritize reception methods (voice, text, etc.) for chat content that the user has frequently used in the past. The reception desk can also suggest the most suitable reception method for a specific time of day based on the user's past chat history. Furthermore, the reception desk can analyze the user's past chat history and select the most suitable reception method for a specific topic. In this way, the reception desk can select the most suitable reception method by analyzing the user's past chat history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past chat history into an AI and have the AI ​​select the most suitable reception method.

[0100] The reception desk can filter incoming messages based on the user's current situation and areas of interest. For example, it can prioritize receiving messages that are highly relevant based on the user's current situation (e.g., working, on vacation). It can also filter relevant messages based on the user's areas of interest (e.g., sports, music). Furthermore, it can select appropriate messages based on the user's current activity (e.g., exercising, reading). By filtering messages based on the user's current situation and areas of interest, it can prioritize receiving messages that are highly relevant. 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 data on the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0101] The reception desk can estimate the user's emotions and determine the priority of the conversation content to be received based on the estimated emotions. For example, if the user is nervous, the reception desk will prioritize receiving important conversation content. Similarly, if the user is relaxed, the reception desk can prioritize receiving lighter conversation content. Furthermore, if the user is in a hurry, the reception desk can prioritize receiving conversation content that requires a quick response. This allows for prioritizing important conversation content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The reception desk can prioritize receiving conversation content that is highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving conversation content related to that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving conversation content related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving conversation content related to their home. This allows the reception desk to prioritize receiving highly relevant conversation content by considering the user's geographical location. 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 the user's geographical location information into the AI ​​and have the AI ​​select highly relevant conversation content.

[0103] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 a generative AI and have the generative AI perform emotion estimation.

[0104] The analysis unit can adjust the level of detail of the analysis based on the importance of the talk content during the analysis. For example, the analysis unit will perform a detailed analysis on important talk content. The analysis unit can also perform a simplified analysis on less important talk content. Furthermore, the analysis unit can perform a rapid analysis on talk content that is of high urgency. In this way, by adjusting the level of detail of the analysis based on the importance of the talk content, a detailed analysis can be performed on important talk content. 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 talk content importance data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0105] The analysis unit can apply different analysis algorithms depending on the category of the talk content during analysis. For example, the analysis unit can apply a business-specific analysis algorithm to business-related talk content. It can also apply a private-use analysis algorithm to private-use talk content. Furthermore, it can apply an entertainment-use analysis algorithm to entertainment-related talk content. By applying different analysis algorithms depending on the category of the talk content, more appropriate analysis results can be provided. 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 category data of the talk content into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0106] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The analysis unit can determine the priority of analysis based on the submission date of the talk content during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted talk content. The analysis unit can also prioritize the analysis of talk content with high urgency. Furthermore, the analysis unit can also determine the priority of analysis based on a deadline specified by the user. This allows for the prioritization of analysis of talk content with high urgency by determining the priority of analysis based on the submission date of the talk content. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input talk content submission date data into AI and have the AI ​​perform the determination of analysis priorities.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk receives the message from the user. The reception desk accepts the message when the user presses a button, and also supports multiple input methods such as voice input and text input. For example, it can use speech recognition technology to convert the user's voice into text and accept it as the message. Step 2: The analysis unit analyzes the content of the conversation received by the reception unit. The analysis unit uses natural language processing technology to analyze the conversation content and extract important keywords and phrases. It can also analyze the content of past conversations and take into account information such as the characteristics of the user's messages and gender. For example, it can retrieve past conversation history from a database and compare it with the conversation content to identify the characteristics of the user's messages. Step 3: The generation unit generates the optimal reply message based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate a reply message that corresponds to the content of the conversation. The generation AI can use a text generation AI (e.g., LLM) to generate a reply message that takes into account the characteristics and relationships of the user's messages. It can also generate a reply message that takes into account the user's gender and relationship. For example, if the user usually uses polite language, the generation unit will generate a reply message that reflects that characteristic. Step 4: The providing unit provides the reply message generated by the generating unit. The providing unit can display the generated reply message on the user's device and can also play it back as audio. For example, it can use speech synthesis technology to play back the generated reply message as audio.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which receives the message content when the user presses a button. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the message content using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates the optimal reply message using generation AI. The provision unit is implemented by the output device 40 of the smart device 14, which displays the generated reply message on the user's device. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and delivery 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 the content of the conversation by converting the user's voice into text. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the conversation using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal reply message using generation AI. The delivery unit is implemented by the speaker 240 of the smart glasses 214 and plays the generated reply message aloud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the content of the conversation by converting the user's voice into text. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the conversation using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal reply message using generation AI. The provision unit is implemented by the display 343 of the headset terminal 314 and displays the generated reply message on the user's device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and delivery unit, is implemented by, for example, 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 the content of the conversation by converting the user's voice into text. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the content of the conversation using natural language processing technology. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal reply message using generation AI. The delivery unit is implemented by, for example, the speaker 240 of the robot 414 and plays the generated reply message aloud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A reception desk that receives messages from users, An analysis unit analyzes the content of the conversation received by the reception unit, A generation unit that generates an optimal reply message based on the information analyzed by the analysis unit, The system includes a providing unit that provides the reply message generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze the content of past conversations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the characteristics of user messages. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Consider the user's gender. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generating the optimal reply message considering the relationship. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the user with the generated reply message. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving messages based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past chat history to select the most suitable reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving chat 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 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the content of the conversation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving chat content, the system prioritizes receiving chat content that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a message, the system analyzes the user's social media activity and accepts relevant messages. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the conversation content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the talk content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the talk content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the talk content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the wording of the reply messages generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail in the reply message is adjusted based on the importance of the conversation content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the category of the talk content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the reply message generated based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, reply messages are prioritized based on when the chat content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the order of reply messages is adjusted based on the relevance of the conversation content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how reply messages are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the steps in the reply message provided based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, it will refer to the user's calendar information to make suggestions based on their schedule. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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 messages from users, An analysis unit analyzes the content of the conversation received by the reception unit, A generation unit that generates an optimal reply message based on the information analyzed by the analysis unit, The system includes a providing unit that provides the reply message generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the content of past conversations. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the characteristics of user messages. The system according to feature 1.

4. The aforementioned analysis unit, Consider the user's gender. The system according to feature 1.

5. The generating unit is Generating the optimal reply message considering the relationship. The system according to feature 1.

6. The aforementioned supply unit is, Provide the user with the generated reply message. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving messages based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past chat history to select the most suitable reception method. The system according to feature 1.

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