system

Abstract

The system according to this embodiment aims to communicate automatically while maintaining an appropriate distance. [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 messages from users. The analysis unit analyzes the messages received by the reception unit. The generation unit generates stamps and simple replies based on the results of the analysis by the analysis unit. The provision unit transmits the stamps and replies 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 method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 conventional technology, there is a problem that it is difficult to communicate while maintaining an appropriate sense of distance.

[0005] The system according to the embodiment aims to automatically communicate while maintaining an appropriate sense of distance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a delivery unit. The reception unit receives messages from users. The analysis unit analyzes the messages received by the reception unit. The generation unit generates stamps or simple replies based on the results of the analysis by the analysis unit. The delivery unit transmits the stamps or replies generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can communicate automatically while maintaining an appropriate distance. [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 manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The communication system according to an embodiment of the present invention is a system that provides a "gentle blocking" function to maintain an appropriate distance. When a user turns on the "gentle blocking" function, the system allows the AI ​​to learn the user's messaging style and habits and interpret the other person's message. For example, if the other person sends a message asking "What are you doing now?", the AI ​​automatically sends a simple reply such as "I'm a little busy." Also, if the other person sends a message asking "I'm eating donuts?", the AI ​​sends a stamp or acknowledgment comment such as "Donuts sound good." Furthermore, the AI ​​responds with a reserved attitude depending on the content of the other person's message. For example, if the other person makes a suggestion such as "I'll treat you next time," the AI ​​sends an ambiguous reply such as "Hmm, I wonder." This allows the user to maintain an appropriate distance in communication with the other person. This "gentle blocking" function is useful for users of all ages who struggle with communication. For example, if you want to distance yourself from a friend who is in a difficult mood, but don't want to cut ties completely, you can use this function to respond appropriately. Furthermore, since frequency settings and response levels can be customized for a fee, the business model is expected to be profitable. This allows the communication system to process user messages appropriately and maintain an appropriate distance.

[0029] The communication system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a delivery unit. The reception unit receives messages from users. For example, the reception unit receives text messages sent by users. The reception unit can also receive voice messages and image messages. For example, the reception unit converts voice messages into text using speech recognition technology and sends it to the analysis unit. The analysis unit analyzes the messages received by the reception unit. For example, the analysis unit analyzes the content of messages using natural language processing technology. The analysis unit can also learn the phrasing and mannerisms of users' messages and interpret the other party's messages. For example, the analysis unit analyzes the user's past message history and extracts specific phrases and stylistic features. The generation unit generates stamps and simple replies based on the results analyzed by the analysis unit. The generation unit generates natural replies using, for example, text generation AI. The generation unit can also generate stamps and acknowledgment comments. For example, the generation unit selects and sends an appropriate stamp according to the content of the user's message. The delivery unit sends the stamps and replies generated by the generation unit. The service provider sends stamps and replies, for example, through a messaging application. The service provider also has functions for customizing frequency settings and response levels. For example, the service provider sends messages based on a frequency set by the user. This allows the communication system according to the embodiment to appropriately process user messages and maintain an appropriate distance.

[0030] The reception unit receives messages from users. For example, it receives text messages sent by users. Specifically, it receives text messages sent by users from smartphones and PCs in real time and stores them in a database. The reception unit can also receive voice messages and image messages. For example, in the case of voice messages, it uses speech recognition technology to convert the voice data into text and sends it to the analysis unit. The speech recognition technology uses a deep learning-based voice model to convert voice to text with high accuracy. In the case of image messages, it uses image recognition technology to analyze the content of the image and convert it into text information as needed. For example, it extracts characters in the image using OCR technology and sends them to the analysis unit as text data. This allows the reception unit to centrally receive and appropriately process messages in various formats from users. Furthermore, the reception unit has a function to monitor the message reception status in real time and send reception confirmation notifications to users. This allows users to confirm that their messages have been reliably received. The reception unit also has a spam filtering function that can automatically detect and block inappropriate messages and spam messages. This helps maintain the integrity and security of the system.

[0031] The analysis unit analyzes messages received by the reception unit. For example, the analysis unit uses natural language processing techniques to analyze the message content. Specifically, it performs morphological and grammatical analysis to understand the message's structure and meaning. Furthermore, the analysis unit learns the user's message phrasing and mannerisms, enabling it to interpret the recipient's messages. For instance, it analyzes the user's past message history to extract specific phrases and stylistic features. This allows it to understand the user's individual communication style and generate more natural responses. The analysis unit continuously learns user message patterns using machine learning algorithms to improve accuracy. For example, it can perform context-aware analysis using recurrent neural networks (RNNs) and transformer models. Additionally, the analysis unit can detect the emotional nuances of messages using sentiment analysis techniques. This enables it to generate appropriate responses based on the user's emotional state. For example, if the user is angry, it provides a calm response; if the user is happy, it generates an empathetic response. By combining these techniques, the analysis unit can analyze user messages from multiple angles, enabling more sophisticated communication.

[0032] The generation unit generates stamps and simple replies based on the results analyzed by the analysis unit. The generation unit generates natural-sounding replies, for example, using text generation AI. Specifically, it uses a generation AI model to generate appropriate responses to the user's message. For example, it can use a large-scale language model to generate contextually natural replies to the user's message. The generation unit can also generate stamps and acknowledgment comments. For example, it can select and send an appropriate stamp depending on the content of the user's message. Image recognition technology and sentiment analysis technology can be used to select the stamp that best suits the user's emotional state and the content of the message. Furthermore, the generation unit can generate more personalized responses by considering the user's past message history and preferences. For example, it can learn stamps and phrases the user has used in the past and generate responses based on that. This allows the generation unit to provide users with more friendly and natural communication. By utilizing these technologies, the generation unit can generate diverse responses to the user's message and improve the quality of communication.

[0033] The service provider sends stamps and replies generated by the generation unit. For example, the service provider sends stamps and replies through messaging applications. Specifically, it sends generated stamps and text messages to the user's messaging application and displays them in real time. The service provider also has customizable frequency settings and response levels. For example, the service provider sends messages based on a frequency set by the user. Users can customize the frequency of message transmission and response speed, allowing them to enjoy communication tailored to their preferences. Furthermore, the service provider supports multiple messaging applications and platforms, enabling seamless message transmission between different applications. For example, it can simultaneously send messages to multiple messaging applications used by the user, providing consistent communication across all applications. This improves user convenience and enables smoother communication. The service provider also monitors the delivery status of sent messages and sends delivery confirmation notifications to the user. This allows users to confirm that their messages have been successfully delivered. Through these functions, the service provider provides users with fast and reliable message delivery, facilitating smoother communication.

[0034] The generation unit can learn the phrasing and mannerisms of the user's messages. For example, the generation unit can learn the phrases and stylistic features that the user frequently uses. For instance, if the user frequently uses "thank you," the generation unit will learn that phrase and use it at the appropriate time. The generation unit can also learn the length and structure of the user's messages. For example, if the generation unit learns the user's preference for short messages, it will learn this tendency and generate short replies. In this way, the generation unit can generate more natural replies by learning the phrasing and mannerisms of the user's messages. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the user's message history into a generation AI and have the generation AI perform the learning of phrasing and mannerisms.

[0035] The analysis unit can interpret the other party's message and respond with a reserved attitude depending on its content. For example, the analysis unit can analyze the content of the other party's message and select an appropriate response. For example, if the other party makes a suggestion such as "I'll treat you next time," the analysis unit will select an ambiguous response such as "Hmm, I wonder." The analysis unit can also analyze the tone and emotion of the other party's message. For example, if the other party is angry, the analysis unit will select a calm response. In this way, the analysis unit can maintain an appropriate distance by responding with a reserved attitude to the other party'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 other party's message into a generating AI and have the generating AI execute a reserved attitude response.

[0036] The service provider can have functions for customizing frequency settings and response levels. For example, the service provider sends messages based on a frequency set by the user. For example, if the user sets the service provider to send messages once a day, the service provider will send messages according to that setting. The service provider can also have a function for customizing response levels. For example, if the user desires a strong response, the service provider will generate an emotionally expressive reply. This allows the service provider to customize frequency settings and response levels, enabling it to respond to user needs. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user setting information into a generating AI and have the generating AI perform the frequency settings and response level customization.

[0037] The service provider can send stamps and acknowledgment comments. For example, the service provider can select and send an appropriate stamp depending on the content of the user's message. For example, if the user sends "I'm eating a donut?", the service provider will send a stamp such as "Donuts sound nice." The service provider can also send acknowledgment comments. For example, if the user sends "What are you doing now?", the service provider will send a simple reply such as "I'm a little busy." In this way, the service provider can maintain appropriate communication by sending stamps and acknowledgment comments. 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 message into a generation AI and have the generation AI generate stamps and acknowledgment comments.

[0038] The reception unit can analyze the user's past message history and select the optimal reception method. For example, the reception unit can prioritize receiving messages from people the user has frequently communicated with in the past. It can also filter messages from people the user has ignored in the past and set their importance to a lower level. Furthermore, if the reception unit receives many messages during a particular time period, it can suggest the most suitable reception method for that time. In this way, the reception unit can select the optimal reception method by analyzing the past message history. 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 message history into a generating AI and have the generating AI select the optimal reception method.

[0039] The reception system can filter messages based on the user's current situation and areas of interest when receiving them. For example, if the user is at work, the reception system will prioritize work-related messages and postpone private messages. The reception system can also filter messages based on the user's hobbies if the user receives many such messages. Furthermore, if the user is participating in a specific event, the reception system can prioritize messages related to that event. This allows the reception system to provide more appropriate responses by filtering messages based on the user's current situation and areas of interest. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The reception unit can prioritize receiving messages that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception unit will prioritize receiving messages related to that location. Furthermore, if the user is traveling, the reception unit can prioritize receiving messages related to their travel destination. Additionally, if the user is at home, the reception unit can prioritize receiving messages related to their home. In this way, the reception unit can prioritize receiving messages that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant messages.

[0041] The reception unit can analyze a user's social media activity when receiving a message and receive relevant messages. For example, if a user is active on a particular social media platform, the reception unit will prioritize receiving messages related to that platform. It can also prioritize receiving messages related to a particular topic if the user frequently posts on that topic. Furthermore, if a user belongs to a particular group, the reception unit can prioritize receiving messages related to that group. In this way, the reception unit can prioritize receiving relevant messages by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant messages.

[0042] The analysis unit can adjust the level of detail in its analysis based on the importance of the message. For example, it can analyze high-importance messages in detail to understand all nuances. It can also analyze low-importance messages concisely, extracting only the main points. Furthermore, it can quickly analyze urgent messages, prioritizing the extraction of important information. This allows the analysis unit to perform more appropriate analysis by adjusting the level of detail based on the importance of the message. 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 message importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, the analysis unit can apply a business-specific analysis algorithm to business-related messages. It can also apply a private-specific analysis algorithm to private messages. Furthermore, it can apply a social media-specific analysis algorithm to social media-related messages. This allows the analysis unit to perform more appropriate analysis by applying different analysis algorithms depending on the message category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input message category data into a generating AI and have the generating AI perform the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the submission date of messages during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted messages. It can also postpone the analysis of older messages. Furthermore, the analysis unit may prioritize the analysis of urgent messages regardless of their submission date. This allows the analysis unit to perform more appropriate analysis by determining the priority of analysis based on the submission date of messages. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input message submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of messages during the analysis process. For example, the analysis unit may prioritize analyzing messages related to the user's current situation. It can also prioritize analyzing messages related to the user's areas of interest. Furthermore, it can prioritize analyzing messages related to the user's past message history. This allows the analysis unit to perform more appropriate analysis by adjusting the order of analysis based on the relevance of messages. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input message relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The generation unit can adjust the level of detail in the generated response based on the importance of the message. For example, the generation unit can generate a detailed response for a high-importance message, and a concise response for a low-importance message. Furthermore, the generation unit can generate a rapid response for an urgent message. In this way, the generation unit can generate a more appropriate response by adjusting the level of detail based on the importance of the message. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input message importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the generated response.

[0047] The generation unit can apply different generation algorithms depending on the message category during generation. For example, the generation unit can apply a business-specific generation algorithm to business-related messages. It can also apply a private-specific generation algorithm to private messages. Furthermore, it can apply a social media-specific generation algorithm to social media-related messages. This allows the generation unit to generate more appropriate responses by applying different generation algorithms depending on the message category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input message category data into a generation AI and have the generation AI perform the application of the generation algorithm.

[0048] The generation unit can determine the generation priority based on when the messages were submitted. For example, the generation unit can prioritize generating replies to recently submitted messages. It can also postpone older messages. Furthermore, the generation unit can quickly generate replies to urgent messages. In this way, the generation unit generates more appropriate replies by determining the generation priority based on when the messages were submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input message submission data into a generation AI and have the generation AI determine the generation priority.

[0049] The generation unit can adjust the order of generation based on the relevance of messages during generation. For example, the generation unit can prioritize generating replies to messages related to the user's current situation. It can also prioritize generating replies to messages related to the user's areas of interest. Furthermore, it can prioritize generating replies to messages related to the user's past message history. In this way, the generation unit can generate more appropriate replies by adjusting the order of generation based on the relevance of messages. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input message relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0050] The service provider can select the optimal service delivery method by referring to the user's past message history at the time of delivery. For example, the service provider can prioritize providing stamps and replies that the user has frequently used in the past. Furthermore, the service provider can provide concise replies to messages that the user has previously ignored. In addition, the service provider can provide the most appropriate stamps and replies for specific recipients based on the user's past message history. Thus, the service provider can select the optimal service delivery method by referring to the user's past message 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 message history data into a generating AI and have the generating AI select the optimal service delivery method.

[0051] The service provider can customize the means of delivery based on the user's current situation at the time of delivery. For example, if the user is at work, the service provider can provide stamps and replies appropriate for business. If the user is enjoying private time, the service provider can provide stamps and replies that promote relaxation. Furthermore, if the user is traveling, the service provider can provide stamps and replies related to travel. In this way, the service provider can provide more appropriate replies by customizing the means of delivery based on the user's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's current situation data into a generating AI and have the generating AI perform the customization of the means of delivery.

[0052] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific location, the service provider can provide stamps or replies related to that location. Furthermore, if the user is traveling, the service provider can provide stamps or replies related to the travel destination. Additionally, if the user is at home, the service provider can provide stamps or replies related to home. This allows the service provider to select the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0053] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, if the user is active on a particular social media platform, the service provider can provide stamps and replies related to that platform. Also, if the user posts frequently on a particular topic, the service provider can provide stamps and replies related to that topic. Furthermore, if the user belongs to a particular group, the service provider can provide stamps and replies related to that group. In this way, the service provider can propose the optimal means of delivery by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI propose a means of delivery.

[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 unit can analyze the user's past message history and select the optimal reception method. For example, it can prioritize receiving messages from people the user has frequently communicated with in the past. It can also filter messages from people the user has ignored in the past and set their importance to a lower level. Furthermore, if a user receives many messages during a specific time period, it can suggest the most suitable reception method for that time. In this way, the reception unit can select the optimal reception method by analyzing the past message history. 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 message history into a generating AI and have the generating AI select the optimal reception method.

[0056] The reception unit can filter messages based on the user's current situation and areas of interest when receiving them. For example, if a user is at work, work-related messages can be prioritized, and private messages can be delayed. Similarly, if a user receives many messages related to their hobbies, messages can be filtered based on those interests. Furthermore, if a user is participating in a specific event, messages related to that event can be prioritized. This allows the reception unit to provide more appropriate responses by filtering messages based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0057] The delivery unit can select the optimal delivery method by referring to the user's past message history when providing content. For example, it can prioritize providing stamps and replies that the user has frequently used in the past. It can also provide concise replies to messages that the user has ignored in the past. Furthermore, it can provide the most appropriate stamps and replies for specific recipients based on the user's past message history. In this way, the delivery unit can select the optimal delivery method by referring to the user's past message history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's message history data into a generating AI and have the generating AI select the optimal delivery method.

[0058] The service provider can customize the means of delivery based on the user's current situation at the time of delivery. For example, if the user is at work, it can provide stamps and replies that are appropriate for business. If the user is enjoying private time, it can provide stamps and replies that promote relaxation. Furthermore, if the user is traveling, it can provide stamps and replies related to travel. In this way, the service provider can provide more appropriate replies by customizing the means of delivery based on the user's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's current situation into a generating AI and have the generating AI perform the customization of the means of delivery.

[0059] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific location, it can provide stamps or replies related to that location. If the user is traveling, it can also provide stamps or replies related to the travel destination. Furthermore, if the user is at home, it can provide stamps or replies related to home. In this way, the service provider can select the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception unit receives messages from users. For example, the reception unit receives text messages sent by users. The reception unit can also receive voice messages and image messages. For example, the reception unit uses speech recognition technology to convert voice messages into text and sends it to the analysis unit. Step 2: The analysis unit analyzes the message received by the reception unit. The analysis unit analyzes the message content using, for example, natural language processing technology. The analysis unit can also learn the user's message phrasing and mannerisms to understand the other party's message. For example, the analysis unit analyzes the user's past message history and extracts specific phrases and stylistic features. Step 3: The generation unit generates stamps or simple replies based on the results analyzed by the analysis unit. The generation unit generates natural-sounding replies, for example, using text generation AI. The generation unit can also generate stamps and acknowledgment comments. For example, the generation unit selects and sends an appropriate stamp according to the content of the user's message. Step 4: The provider sends the stamps and replies generated by the generator. The provider sends the stamps and replies, for example, through a messaging application. The provider also has functions to customize the frequency and level of response. For example, the provider sends messages based on the frequency set by the user.

[0062] (Example of form 2) The communication system according to an embodiment of the present invention is a system that provides a "gentle blocking" function to maintain an appropriate distance. When a user turns on the "gentle blocking" function, the system allows the AI ​​to learn the user's messaging style and habits and interpret the other person's message. For example, if the other person sends a message asking "What are you doing now?", the AI ​​automatically sends a simple reply such as "I'm a little busy." Also, if the other person sends a message asking "I'm eating donuts?", the AI ​​sends a stamp or acknowledgment comment such as "Donuts sound good." Furthermore, the AI ​​responds with a reserved attitude depending on the content of the other person's message. For example, if the other person makes a suggestion such as "I'll treat you next time," the AI ​​sends an ambiguous reply such as "Hmm, I wonder." This allows the user to maintain an appropriate distance in communication with the other person. This "gentle blocking" function is useful for users of all ages who struggle with communication. For example, if you want to distance yourself from a friend who is in a difficult mood, but don't want to cut ties completely, you can use this function to respond appropriately. Furthermore, since frequency settings and response levels can be customized for a fee, the business model is expected to be profitable. This allows the communication system to process user messages appropriately and maintain an appropriate distance.

[0063] The communication system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a delivery unit. The reception unit receives messages from users. For example, the reception unit receives text messages sent by users. The reception unit can also receive voice messages and image messages. For example, the reception unit converts voice messages into text using speech recognition technology and sends it to the analysis unit. The analysis unit analyzes the messages received by the reception unit. For example, the analysis unit analyzes the content of messages using natural language processing technology. The analysis unit can also learn the phrasing and mannerisms of users' messages and interpret the other party's messages. For example, the analysis unit analyzes the user's past message history and extracts specific phrases and stylistic features. The generation unit generates stamps and simple replies based on the results analyzed by the analysis unit. The generation unit generates natural replies using, for example, text generation AI. The generation unit can also generate stamps and acknowledgment comments. For example, the generation unit selects and sends an appropriate stamp according to the content of the user's message. The delivery unit sends the stamps and replies generated by the generation unit. The service provider sends stamps and replies, for example, through a messaging application. The service provider also has functions for customizing frequency settings and response levels. For example, the service provider sends messages based on a frequency set by the user. This allows the communication system according to the embodiment to appropriately process user messages and maintain an appropriate distance.

[0064] The reception unit receives messages from users. For example, it receives text messages sent by users. Specifically, it receives text messages sent by users from smartphones and PCs in real time and stores them in a database. The reception unit can also receive voice messages and image messages. For example, in the case of voice messages, it uses speech recognition technology to convert the voice data into text and sends it to the analysis unit. The speech recognition technology uses a deep learning-based voice model to convert voice to text with high accuracy. In the case of image messages, it uses image recognition technology to analyze the content of the image and convert it into text information as needed. For example, it extracts characters in the image using OCR technology and sends them to the analysis unit as text data. This allows the reception unit to centrally receive and appropriately process messages in various formats from users. Furthermore, the reception unit has a function to monitor the message reception status in real time and send reception confirmation notifications to users. This allows users to confirm that their messages have been reliably received. The reception unit also has a spam filtering function that can automatically detect and block inappropriate messages and spam messages. This helps maintain the integrity and security of the system.

[0065] The analysis unit analyzes messages received by the reception unit. For example, the analysis unit uses natural language processing techniques to analyze the message content. Specifically, it performs morphological and grammatical analysis to understand the message's structure and meaning. Furthermore, the analysis unit learns the user's message phrasing and mannerisms, enabling it to interpret the recipient's messages. For instance, it analyzes the user's past message history to extract specific phrases and stylistic features. This allows it to understand the user's individual communication style and generate more natural responses. The analysis unit continuously learns user message patterns using machine learning algorithms to improve accuracy. For example, it can perform context-aware analysis using recurrent neural networks (RNNs) and transformer models. Additionally, the analysis unit can detect the emotional nuances of messages using sentiment analysis techniques. This enables it to generate appropriate responses based on the user's emotional state. For example, if the user is angry, it provides a calm response; if the user is happy, it generates an empathetic response. By combining these techniques, the analysis unit can analyze user messages from multiple angles, enabling more sophisticated communication.

[0066] The generation unit generates stamps and simple replies based on the results analyzed by the analysis unit. The generation unit generates natural-sounding replies, for example, using text generation AI. Specifically, it uses a generation AI model to generate appropriate responses to the user's message. For example, it can use a large-scale language model to generate contextually natural replies to the user's message. The generation unit can also generate stamps and acknowledgment comments. For example, it can select and send an appropriate stamp depending on the content of the user's message. Image recognition technology and sentiment analysis technology can be used to select the stamp that best suits the user's emotional state and the content of the message. Furthermore, the generation unit can generate more personalized responses by considering the user's past message history and preferences. For example, it can learn stamps and phrases the user has used in the past and generate responses based on that. This allows the generation unit to provide users with more friendly and natural communication. By utilizing these technologies, the generation unit can generate diverse responses to the user's message and improve the quality of communication.

[0067] The service provider sends stamps and replies generated by the generation unit. For example, the service provider sends stamps and replies through messaging applications. Specifically, it sends generated stamps and text messages to the user's messaging application and displays them in real time. The service provider also has customizable frequency settings and response levels. For example, the service provider sends messages based on a frequency set by the user. Users can customize the frequency of message transmission and response speed, allowing them to enjoy communication tailored to their preferences. Furthermore, the service provider supports multiple messaging applications and platforms, enabling seamless message transmission between different applications. For example, it can simultaneously send messages to multiple messaging applications used by the user, providing consistent communication across all applications. This improves user convenience and enables smoother communication. The service provider also monitors the delivery status of sent messages and sends delivery confirmation notifications to the user. This allows users to confirm that their messages have been successfully delivered. Through these functions, the service provider provides users with fast and reliable message delivery, facilitating smoother communication.

[0068] The generation unit can learn the phrasing and mannerisms of the user's messages. For example, the generation unit can learn the phrases and stylistic features that the user frequently uses. For instance, if the user frequently uses "thank you," the generation unit will learn that phrase and use it at the appropriate time. The generation unit can also learn the length and structure of the user's messages. For example, if the generation unit learns the user's preference for short messages, it will learn this tendency and generate short replies. In this way, the generation unit can generate more natural replies by learning the phrasing and mannerisms of the user's messages. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the user's message history into a generation AI and have the generation AI perform the learning of phrasing and mannerisms.

[0069] The analysis unit can interpret the other party's message and respond with a reserved attitude depending on its content. For example, the analysis unit can analyze the content of the other party's message and select an appropriate response. For example, if the other party makes a suggestion such as "I'll treat you next time," the analysis unit will select an ambiguous response such as "Hmm, I wonder." The analysis unit can also analyze the tone and emotion of the other party's message. For example, if the other party is angry, the analysis unit will select a calm response. In this way, the analysis unit can maintain an appropriate distance by responding with a reserved attitude to the other party'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 other party's message into a generating AI and have the generating AI execute a reserved attitude response.

[0070] The service provider can have functions for customizing frequency settings and response levels. For example, the service provider sends messages based on a frequency set by the user. For example, if the user sets the service provider to send messages once a day, the service provider will send messages according to that setting. The service provider can also have a function for customizing response levels. For example, if the user desires a strong response, the service provider will generate an emotionally expressive reply. This allows the service provider to customize frequency settings and response levels, enabling it to respond to user needs. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user setting information into a generating AI and have the generating AI perform the frequency settings and response level customization.

[0071] The service provider can send stamps and acknowledgment comments. For example, the service provider can select and send an appropriate stamp depending on the content of the user's message. For example, if the user sends "I'm eating a donut?", the service provider will send a stamp such as "Donuts sound nice." The service provider can also send acknowledgment comments. For example, if the user sends "What are you doing now?", the service provider will send a simple reply such as "I'm a little busy." In this way, the service provider can maintain appropriate communication by sending stamps and acknowledgment comments. 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 message into a generation AI and have the generation AI generate stamps and acknowledgment comments.

[0072] The reception unit can estimate the user's emotions and adjust the timing of message reception based on the estimated emotions. For example, if the user is stressed, the reception unit can delay message reception and wait until the user is relaxed. If the user is relaxed, the reception unit can immediately receive the message and respond quickly. Furthermore, if the user is in a hurry, the reception unit can prioritize important messages and process them quickly. This allows the reception unit to provide a more appropriate response by adjusting the timing of message reception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception unit can analyze the user's past message history and select the optimal reception method. For example, the reception unit can prioritize receiving messages from people the user has frequently communicated with in the past. It can also filter messages from people the user has ignored in the past and set their importance to a lower level. Furthermore, if the reception unit receives many messages during a particular time period, it can suggest the most suitable reception method for that time. In this way, the reception unit can select the optimal reception method by analyzing the past message history. 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 message history into a generating AI and have the generating AI select the optimal reception method.

[0074] The reception system can filter messages based on the user's current situation and areas of interest when receiving them. For example, if the user is at work, the reception system will prioritize work-related messages and postpone private messages. The reception system can also filter messages based on the user's hobbies if the user receives many such messages. Furthermore, if the user is participating in a specific event, the reception system can prioritize messages related to that event. This allows the reception system to provide more appropriate responses by filtering messages based on the user's current situation and areas of interest. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0075] The reception unit can estimate the user's emotions and determine the priority of messages to receive based on the estimated emotions. For example, if the user is stressed, the reception unit will postpone less important messages and prioritize only important ones. If the user is relaxed, the reception unit can receive all messages equally. Furthermore, if the user is in a hurry, the reception unit can prioritize urgent messages. This allows the reception unit to provide a more appropriate response by prioritizing messages according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception unit can prioritize receiving messages that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception unit will prioritize receiving messages related to that location. Furthermore, if the user is traveling, the reception unit can prioritize receiving messages related to their travel destination. Additionally, if the user is at home, the reception unit can prioritize receiving messages related to their home. In this way, the reception unit can prioritize receiving messages that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant messages.

[0077] The reception unit can analyze a user's social media activity when receiving a message and receive relevant messages. For example, if a user is active on a particular social media platform, the reception unit will prioritize receiving messages related to that platform. It can also prioritize receiving messages related to a particular topic if the user frequently posts on that topic. Furthermore, if a user belongs to a particular group, the reception unit can prioritize receiving messages related to that group. In this way, the reception unit can prioritize receiving relevant messages by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant messages.

[0078] The analysis unit can estimate the user's emotions and adjust the message analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit will perform a concise and to-the-point analysis. If the user is relaxed, the analysis unit can perform a detailed analysis and gain a deeper understanding of the message's nuances. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis and prioritize the extraction of important information. In this way, the analysis unit can perform a more appropriate analysis by adjusting the message analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 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.

[0079] The analysis unit can adjust the level of detail in its analysis based on the importance of the message. For example, it can analyze high-importance messages in detail to understand all nuances. It can also analyze low-importance messages concisely, extracting only the main points. Furthermore, it can quickly analyze urgent messages, prioritizing the extraction of important information. This allows the analysis unit to perform more appropriate analysis by adjusting the level of detail based on the importance of the message. 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 message importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, the analysis unit can apply a business-specific analysis algorithm to business-related messages. It can also apply a private-specific analysis algorithm to private messages. Furthermore, it can apply a social media-specific analysis algorithm to social media-related messages. This allows the analysis unit to perform more appropriate analysis by applying different analysis algorithms depending on the message category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input message category data into a generating AI and have the generating AI perform the application of the analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing messages of high importance. If the user is relaxed, the analysis unit can analyze all messages equally. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing messages of high urgency. In this way, the analysis unit can perform more appropriate analysis by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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.

[0082] The analysis unit can determine the priority of analysis based on the submission date of messages during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted messages. It can also postpone the analysis of older messages. Furthermore, the analysis unit may prioritize the analysis of urgent messages regardless of their submission date. This allows the analysis unit to perform more appropriate analysis by determining the priority of analysis based on the submission date of messages. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input message submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of messages during the analysis process. For example, the analysis unit may prioritize analyzing messages related to the user's current situation. It can also prioritize analyzing messages related to the user's areas of interest. Furthermore, it can prioritize analyzing messages related to the user's past message history. This allows the analysis unit to perform more appropriate analysis by adjusting the order of analysis based on the relevance of messages. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input message relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0084] The generation unit can estimate the user's emotions and adjust the method of generating stamps and replies based on the estimated emotions. For example, if the user is stressed, the generation unit can generate simple, relaxing stamps and replies. If the user is relaxed, the generation unit can generate more detailed stamps and replies. Furthermore, if the user is in a hurry, the generation unit can generate quick replies. In this way, the generation unit generates more appropriate replies by adjusting the method of generating stamps and replies according to the user's emotions. 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 AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0085] The generation unit can adjust the level of detail in the generated response based on the importance of the message. For example, the generation unit can generate a detailed response for a high-importance message, and a concise response for a low-importance message. Furthermore, the generation unit can generate a rapid response for an urgent message. In this way, the generation unit can generate a more appropriate response by adjusting the level of detail based on the importance of the message. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input message importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the generated response.

[0086] The generation unit can apply different generation algorithms depending on the message category during generation. For example, the generation unit can apply a business-specific generation algorithm to business-related messages. It can also apply a private-specific generation algorithm to private messages. Furthermore, it can apply a social media-specific generation algorithm to social media-related messages. This allows the generation unit to generate more appropriate responses by applying different generation algorithms depending on the message category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input message category data into a generation AI and have the generation AI perform the application of the generation algorithm.

[0087] The generation unit can estimate the user's emotions and determine the priority of stamps and replies to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating relaxing stamps and replies. If the user is relaxed, the generation unit can generate all stamps and replies equally. Furthermore, if the user is in a hurry, the generation unit can generate replies quickly. In this way, the generation unit generates more appropriate replies by determining the priority of stamps and replies according to the user's emotions. 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 AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0088] The generation unit can determine the generation priority based on when the messages were submitted. For example, the generation unit can prioritize generating replies to recently submitted messages. It can also postpone older messages. Furthermore, the generation unit can quickly generate replies to urgent messages. In this way, the generation unit generates more appropriate replies by determining the generation priority based on when the messages were submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input message submission data into a generation AI and have the generation AI determine the generation priority.

[0089] The generation unit can adjust the order of generation based on the relevance of messages during generation. For example, the generation unit can prioritize generating replies to messages related to the user's current situation. It can also prioritize generating replies to messages related to the user's areas of interest. Furthermore, it can prioritize generating replies to messages related to the user's past message history. In this way, the generation unit can generate more appropriate replies by adjusting the order of generation based on the relevance of messages. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input message relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0090] The service provider can estimate the user's emotions and adjust the way stamps and replies are delivered based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple, relaxing stamps and replies. If the user is relaxed, the service provider can provide more detailed stamps and replies. Furthermore, if the user is in a hurry, the service provider can provide a quick reply. In this way, the service provider can provide more appropriate replies by adjusting the way stamps and replies are delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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.

[0091] The service provider can select the optimal service delivery method by referring to the user's past message history at the time of delivery. For example, the service provider can prioritize providing stamps and replies that the user has frequently used in the past. Furthermore, the service provider can provide concise replies to messages that the user has previously ignored. In addition, the service provider can provide the most appropriate stamps and replies for specific recipients based on the user's past message history. Thus, the service provider can select the optimal service delivery method by referring to the user's past message 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 message history data into a generating AI and have the generating AI select the optimal service delivery method.

[0092] The service provider can customize the means of delivery based on the user's current situation at the time of delivery. For example, if the user is at work, the service provider can provide stamps and replies appropriate for business. If the user is enjoying private time, the service provider can provide stamps and replies that promote relaxation. Furthermore, if the user is traveling, the service provider can provide stamps and replies related to travel. In this way, the service provider can provide more appropriate replies by customizing the means of delivery based on the user's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's current situation data into a generating AI and have the generating AI perform the customization of the means of delivery.

[0093] The service provider can estimate the user's emotions and determine the priority of stamps and replies to provide based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing relaxing stamps and replies. If the user is relaxed, the service provider can provide all stamps and replies equally. Furthermore, if the user is in a hurry, the service provider can provide a quick reply. In this way, the service provider can provide more appropriate replies by determining the priority of stamps and replies according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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.

[0094] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific location, the service provider can provide stamps or replies related to that location. Furthermore, if the user is traveling, the service provider can provide stamps or replies related to the travel destination. Additionally, if the user is at home, the service provider can provide stamps or replies related to home. This allows the service provider to select the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0095] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, if the user is active on a particular social media platform, the service provider can provide stamps and replies related to that platform. Also, if the user posts frequently on a particular topic, the service provider can provide stamps and replies related to that topic. Furthermore, if the user belongs to a particular group, the service provider can provide stamps and replies related to that group. In this way, the service provider can propose the optimal means of delivery by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI propose a means of delivery.

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

[0097] The analysis unit can estimate the user's emotions and adjust the message analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a concise and to-the-point analysis. If the user is relaxed, the analysis unit can perform a detailed analysis and gain a deeper understanding of the message's nuances. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis and prioritize the extraction of important information. In this way, the analysis unit can perform a more appropriate analysis by adjusting the message analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The service provider can estimate the user's emotions and adjust the way stamps and replies are delivered based on the estimated emotions. For example, if the user is stressed, the service provider can deliver simple, relaxing stamps and replies. If the user is relaxed, the service provider can deliver more detailed stamps and replies. Furthermore, if the user is in a hurry, the service provider can deliver a quick reply. In this way, the service provider can deliver more appropriate replies by adjusting the way stamps and replies are delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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.

[0099] The reception unit can estimate the user's emotions and adjust the timing of message reception based on the estimated emotions. For example, if the user is stressed, the reception unit can delay message reception and wait until the user is relaxed. If the user is relaxed, the reception unit can immediately receive the message and respond quickly. Furthermore, if the user is in a hurry, the reception unit can prioritize important messages and process them quickly. This allows the reception unit to provide a more appropriate response by adjusting the timing of message reception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The generation unit can estimate the user's emotions and adjust the method of generating stamps and replies based on the estimated emotions. For example, if the user is stressed, the generation unit can generate simple, relaxing stamps and replies. If the user is relaxed, the generation unit can generate more detailed stamps and replies. Furthermore, if the user is in a hurry, the generation unit can generate replies quickly. In this way, the generation unit generates more appropriate replies by adjusting the method of generating stamps and replies according to the user's emotions. 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 AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0101] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize the analysis of high-priority messages. If the user is relaxed, the analysis unit can analyze all messages equally. Furthermore, if the user is in a hurry, the analysis unit can prioritize the analysis of urgent messages. In this way, the analysis unit can perform more appropriate analysis by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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.

[0102] The reception unit can analyze the user's past message history and select the optimal reception method. For example, it can prioritize receiving messages from people the user has frequently communicated with in the past. It can also filter messages from people the user has ignored in the past and set their importance to a lower level. Furthermore, if a user receives many messages during a specific time period, it can suggest the most suitable reception method for that time. In this way, the reception unit can select the optimal reception method by analyzing the past message history. 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 message history into a generating AI and have the generating AI select the optimal reception method.

[0103] The reception unit can filter messages based on the user's current situation and areas of interest when receiving them. For example, if a user is at work, work-related messages can be prioritized, and private messages can be delayed. Similarly, if a user receives many messages related to their hobbies, messages can be filtered based on those interests. Furthermore, if a user is participating in a specific event, messages related to that event can be prioritized. This allows the reception unit to provide more appropriate responses by filtering messages based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0104] The delivery unit can select the optimal delivery method by referring to the user's past message history when providing content. For example, it can prioritize providing stamps and replies that the user has frequently used in the past. It can also provide concise replies to messages that the user has ignored in the past. Furthermore, it can provide the most appropriate stamps and replies for specific recipients based on the user's past message history. In this way, the delivery unit can select the optimal delivery method by referring to the user's past message history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's message history data into a generating AI and have the generating AI select the optimal delivery method.

[0105] The service provider can customize the means of delivery based on the user's current situation at the time of delivery. For example, if the user is at work, it can provide stamps and replies that are appropriate for business. If the user is enjoying private time, it can provide stamps and replies that promote relaxation. Furthermore, if the user is traveling, it can provide stamps and replies related to travel. In this way, the service provider can provide more appropriate replies by customizing the means of delivery based on the user's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's current situation into a generating AI and have the generating AI perform the customization of the means of delivery.

[0106] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific location, it can provide stamps or replies related to that location. If the user is traveling, it can also provide stamps or replies related to the travel destination. Furthermore, if the user is at home, it can provide stamps or replies related to home. In this way, the service provider can select the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

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

[0108] Step 1: The reception unit receives messages from users. For example, the reception unit receives text messages sent by users. The reception unit can also receive voice messages and image messages. For example, the reception unit uses speech recognition technology to convert voice messages into text and sends it to the analysis unit. Step 2: The analysis unit analyzes the message received by the reception unit. The analysis unit analyzes the message content using, for example, natural language processing technology. The analysis unit can also learn the user's message phrasing and mannerisms to understand the other party's message. For example, the analysis unit analyzes the user's past message history and extracts specific phrases and stylistic features. Step 3: The generation unit generates stamps or simple replies based on the results analyzed by the analysis unit. The generation unit generates natural-sounding replies, for example, using text generation AI. The generation unit can also generate stamps and acknowledgment comments. For example, the generation unit selects and sends an appropriate stamp according to the content of the user's message. Step 4: The provider sends the stamps and replies generated by the generator. The provider sends the stamps and replies, for example, through a messaging application. The provider also has functions to customize the frequency and level of response. For example, the provider sends messages based on the frequency set by the user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, 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 and receives messages from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the message. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates stamps or simple replies. The provision unit is implemented by the output device 40 of the smart device 14 and transmits the generated stamps or replies. 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.

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

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and delivery unit, is implemented, for example, 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 messages from the user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the message. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates stamps or simple replies. The delivery unit is implemented, for example, by the speaker 240 of the smart glasses 214 and transmits the generated stamps or replies. 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.

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

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and delivery 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 messages from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the message. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates stamps or simple replies. The delivery unit is implemented by the speaker 240 of the headset terminal 314 and transmits the generated stamps or replies. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and delivery unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives messages from the user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the message. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates stamps or simple replies. The delivery unit is implemented, for example, by the speaker 240 of the robot 414 and transmits the generated stamps or replies. 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.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A reception desk that receives messages from users, An analysis unit that analyzes the message received by the aforementioned reception unit, A generation unit that generates stamps or simple replies based on the results of analysis performed by the aforementioned analysis unit, The system includes a providing unit that transmits stamps and replies generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Learn the user's messaging style and habits. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Interpret the other person's message and respond with a reserved attitude depending on its content. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It has features to customize the frequency setting and response level. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Send stamps or acknowledgment comments. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of message delivery based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past message history and select the optimal method of receiving messages. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving messages, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of messages to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving messages, the system prioritizes receiving messages that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a message, the system analyzes the user's social media activity and selects relevant messages. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the message analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the message category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the messages were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts how stamps and replies are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, adjust the level of detail based on the importance of the message. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, different generation algorithms are applied depending on the message category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and determines the priority of stamps and replies to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the generation priority is determined based on when the message was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the generation order is adjusted based on the relevance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how stamps and replies are provided based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the system will refer to the user's past message history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the delivery method will be customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of stamps and replies to provide based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A reception desk that receives messages from users, An analysis unit that analyzes the message received by the aforementioned reception unit, A generation unit that generates stamps or simple replies based on the results of analysis performed by the aforementioned analysis unit, The system includes a providing unit that transmits stamps and replies generated by the generation unit. A system characterized by the following features.

2. The generating unit is Learn the user's messaging style and habits. The system according to feature 1.

3. The aforementioned analysis unit, Interpret the other person's message and respond with a reserved attitude depending on its content. The system according to feature 1.

4. The aforementioned supply unit is, It has features to customize the frequency setting and response level. The system according to feature 1.

5. The aforementioned supply unit is, Send stamps or acknowledgment comments. The system according to feature 1.

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

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

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

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

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

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