system

Abstract

The system according to this embodiment aims to enable users to exchange messages efficiently. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a suggestion unit. The reception unit receives user input. The generation unit generates a prompt based on the information received by the reception unit. The suggestion unit inputs the prompt generated by the generation unit into a generation AI and outputs an appropriate message.

Description

Technical Field

[0006] , , , ,

[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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a lot of time is consumed to consider an optimal message in the message exchange.

[0005] The system according to the embodiment aims to enable a user to efficiently perform message exchange.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a suggestion unit. The reception unit receives user input. The generation unit generates a prompt based on the information received by the reception unit. The suggestion unit inputs the prompt generated by the generation unit into a generation AI and outputs an appropriate message. [Effects of the Invention]

[0007] The system according to this embodiment allows users to exchange messages efficiently. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The message suggestion system according to an embodiment of the present invention is a system that uses a generating AI to suggest messages that help the user achieve goals such as "I want to reach this state in the end" or "I want to build this kind of relationship" in message exchanges. When the user is exchanging chat messages, this system displays boxes for inputting past exchanges, the current exchange, and what the user wants to achieve. For example, the user inputs a goal such as "I want to become friends with this person" or "I want to decline the invitation depending on the content." Next, the system extracts this information and generates prompts to input into the generating AI. Specifically, it generates prompts in the format of "###This is the exchange we are having.\nContent of this exchange\n###From this exchange, I want to achieve the following\nInput of what you want to achieve###Please read the relationship from the following exchange with the other party\nPast exchanges" + instruction "Please output an appropriate message as a message from the user based on the above content." The generated prompts are input into the generating AI, which outputs an appropriate message. For example, specific messages such as "I want to find out about the other person's hobbies in a neutral way" or "I want to invite them to a movie" are suggested. Furthermore, this system provides two business opportunities. First, if service reservations or purchases are included as suggested options, the system provides links to external services. For example, it can suggest a link to reserve a specific service. Second, it visualizes user communication trends, which can be used for future business ideas. For example, based on data such as "people in their 40s often seek advice on making new friends," a business targeting people in their 40s for making friends can be developed. This allows users to send effective messages toward their goals without consuming a lot of time, and also gain business opportunities. In short, the message suggestion system enables users to send effective messages toward their goals.

[0029] The message suggestion system according to the embodiment comprises a reception unit, a generation unit, and a suggestion unit. The reception unit receives user input. User input includes, but is not limited to, information such as past interactions, the current interaction, and what the user wants to achieve. The reception unit displays a box for the user to enter a goal, such as "I want to become friends with this person" or "I want to decline the invitation depending on the content." The generation unit uses a generation AI to generate a prompt based on the information received by the reception unit. The prompt is generated in the format, but is not limited to, "###This is the interaction we are having.\nContent of the current interaction\n###From this interaction, I want the following to happen\nInput of what you want to achieve###Please read the relationship from the following interaction with the other party\nPast interactions" + instruction "Please output an appropriate message as a message from the user based on the above content." The generation unit uses a generation AI to generate the prompt. The suggestion unit inputs the prompt generated by the generation unit into the generation AI and outputs an appropriate message. Appropriate messages may include specific examples such as "I want to politely find out about the other person's hobbies" or "I want to invite them to a movie," but are not limited to such examples. The suggestion unit uses a generation AI to output appropriate messages. As a result, the message suggestion system according to this embodiment can generate appropriate messages based on user input.

[0030] The reception section receives user input. User input includes, but is not limited to, information such as past interactions, current interactions, and the user's desired outcome. Specifically, users can input the content of past messages, details of current interactions, and even the type of relationship or goals they aim for. For example, the system displays boxes for users to enter goals such as "I want to become friends with this person" or "I may decline the invitation depending on the content." This allows users to clearly communicate their intentions and desires to the system. Furthermore, the reception section also plays a role in organizing the information entered by users and passing it to the generation section in an appropriate format. For example, it analyzes the text entered by the user and extracts important keywords and phrases so that the generation section can efficiently generate prompts. The reception section also has a function to save user input for later reference. This allows users to review past input and make corrections or additions as needed. In addition, the reception section can provide appropriate input guides and support based on the user's input. For example, if a user is unsure about what to input, it can provide specific examples and hints to support smooth input. This allows the reception desk to efficiently and effectively receive user input, improving the overall performance of the system.

[0031] The generation unit uses a generation AI to generate prompts based on information received by the reception unit. Prompts are generated in a format such as, for example, "###This is the exchange we are having.\nContent of this exchange\n###From this exchange, I would like to achieve the following\nInput of what you want to achieve###Please interpret the relationship from the following exchange with the other party\nPast exchange" + instruction "Please output an appropriate message as a message from the user based on the above content," but are not limited to this example. Specifically, the generation unit automatically constructs an appropriate prompt based on the information entered by the user. The generation AI utilizes natural language processing technology to accurately understand the user's intentions and desires and generates the optimal prompt based on that. For example, if the user enters "I want to become friends with this person," the generation unit will consider past exchanges and the current situation and generate a prompt that suggests a specific message to deepen the relationship with the other party. In addition, the generation unit appropriately formats the user's input content during the prompt generation process so that the generation AI can process it efficiently. This allows the generation unit to make the most of the user's input content and generate the optimal prompt. Furthermore, the generation unit also has a function to save the generated prompts so that they can be referenced later. This allows users to review previously generated prompts and make corrections or additions as needed. This enables the generation unit to process user input efficiently and effectively, improving overall system performance.

[0032] The suggestion unit inputs prompts generated by the generation unit into the generation AI and outputs an appropriate message. Appropriate messages may include specific examples such as "I want to politely find out about the other person's hobbies" or "I want to invite them to a movie," but are not limited to these examples. Specifically, the suggestion unit inputs prompts generated by the generation unit into the generation AI, which then generates the optimal message based on those prompts. The generation AI utilizes natural language generation technology to automatically create messages that align with the user's intentions and desires. For example, if the user inputs "I want to politely find out about the other person's hobbies," the generation AI considers past interactions and the current situation to generate a message that naturally asks about the other person's hobbies. The suggestion unit also presents the generated message to the user, allowing them to review it and modify or add to it as needed. This helps the suggestion unit easily create the optimal message. Furthermore, the suggestion unit has a function to save generated messages for later reference. This allows users to review previously generated messages and modify or add to them as needed. This allows the suggestion section to process user input efficiently and effectively, improving the overall system performance.

[0033] The generation unit can generate prompts using a generative AI. For example, the generation unit uses the generative AI to generate prompts based on user input. For example, if the user inputs "I want to become friends with this person," the generation unit can generate a prompt based on that goal. Also, if the user inputs "I want to decline the invitation depending on the content," the generation unit can generate a prompt based on that goal. Furthermore, if the user inputs "I want to politely find out about the other person's hobbies," the generation unit can generate a prompt based on that goal. This improves the accuracy of prompt generation by using a generative AI. The generative AI can generate prompts using, for example, a specific algorithm or model. Some or all of the above-described processes in the generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generation unit can input user input into the generative AI and have the generative AI generate prompts.

[0034] The suggestion unit can output appropriate messages using a generative AI. For example, the suggestion unit uses a generative AI to output appropriate messages based on user input. For example, if the user inputs "I want to become friends with this person," the suggestion unit can output an appropriate message based on that goal. Also, if the user inputs "I want to decline the invitation depending on the content," the suggestion unit can output an appropriate message based on that goal. Furthermore, if the user inputs "I want to politely find out about the other person's hobbies," the suggestion unit can output an appropriate message based on that goal. In this way, using a generative AI improves the accuracy of message output. The generative AI can output messages using, for example, a specific algorithm or model. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input a prompt generated by the generation unit into the generative AI and cause the generative AI to output an appropriate message.

[0035] The message suggestion system includes a suggestion section that proposes service reservations and purchases. For example, when a user is exchanging messages, the suggestion section can suggest a link to reserve a specific service. If the user inputs "I want to become friends with this person," the suggestion section can suggest a restaurant reservation link based on that goal. Similarly, if the user inputs "I might decline the invitation depending on the content," the suggestion section can suggest a movie ticket purchase link based on that goal. Furthermore, if the user inputs "I want to politely find out about the other person's hobbies," the suggestion section can suggest a reservation link for an event related to those hobbies based on that goal. This improves user convenience by suggesting service reservations and purchases. Some or all of the above processing in the suggestion section may be performed using AI, or not. For example, the suggestion section can input user input into AI and have the AI ​​execute service reservation and purchase suggestions.

[0036] The message suggestion system includes a visualization unit that visualizes the user's communication trends. The visualization unit can, for example, analyze the frequency and content of the user's message exchanges and display them as graphs and charts. For instance, if a user inputs "I want to become friends with this person," the visualization unit can visualize the frequency and content of past exchanges based on that goal. Similarly, if a user inputs "I want to decline the invitation depending on its content," the visualization unit can visualize the trends of past exchanges based on that goal. Furthermore, if a user inputs "I want to politely find out about the other person's hobbies," the visualization unit can visualize the content of past exchanges based on that goal. This visualization of the user's communication trends enables the utilization of business ideas. Some or all of the above-described processes in the visualization unit may be performed using AI, or not. For example, the visualization unit can input user message exchange data into an AI and have the AI ​​perform the visualization of communication trends.

[0037] The reception desk can analyze the user's past input history and suggest appropriate input methods. For example, the reception desk can automatically display as suggestions content that the user has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest input content to be used during specific time periods based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into AI and have the AI ​​suggest the optimal input method.

[0038] The reception unit can filter input content based on the user's current situation and areas of interest during input. For example, when a user enters their current situation, the reception unit can prioritize displaying input content relevant to that situation. The reception unit can also automatically filter relevant input content based on the user's areas of interest. Furthermore, if the user is in a specific situation, the reception unit can suggest input content that is most suitable for that situation. This allows for more relevant input by filtering input content based on the user's situation and areas of interest. 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 data on the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the filtering of input content.

[0039] The reception unit can prioritize displaying highly relevant input content by considering the user's geographical location during input. For example, if the user is in a specific location, the reception unit can prioritize displaying input content related to that location. The reception unit can also automatically filter relevant input content based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can suggest the most appropriate input content based on their current location. This enables more appropriate input by displaying highly relevant input content based on 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 the AI ​​and have the AI ​​display highly relevant input content.

[0040] The reception unit can analyze the user's social media activity during input and suggest relevant input content. For example, the reception unit can suggest relevant input content based on information the user has shared on social media. It can also analyze the user's areas of interest from their social media activity and suggest the most suitable input content. Furthermore, if the user is active on a particular social media platform, the reception unit can prioritize displaying input content relevant to that platform. This allows the reception unit to suggest relevant input content 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 without AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​suggest relevant input content.

[0041] The generation unit can adjust the level of detail of prompts based on the importance of the input content when generating prompts. For example, the generation unit can generate detailed prompts for important input content. It can also generate concise prompts for general input content. Furthermore, the generation unit can dynamically adjust the level of detail of prompts depending on the specific situation. This allows for the generation of more appropriate prompts by adjusting the level of detail of prompts based on the importance of the input content. 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 data on the importance of the input content into the AI ​​and have the AI ​​perform the adjustment of the level of detail of prompts.

[0042] The generation unit can apply different generation algorithms depending on the category of the input content when generating prompts. For example, the generation unit can apply a business-oriented generation algorithm to business-related input content. It can also apply a private-use generation algorithm to private input content. Furthermore, the generation unit can dynamically select the optimal generation algorithm depending on a specific category. This allows for the generation of more appropriate prompts by applying different generation algorithms depending on the input content 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 the input content category data into the AI ​​and have the AI ​​apply the generation algorithm.

[0043] The generation unit can determine the priority of prompts based on the submission timing of the input content when generating prompts. For example, the generation unit can prioritize prompts for urgent input content. It can also generate prompts with normal priority for general input content. Furthermore, the generation unit can dynamically adjust the prompt priority according to specific submission timings. This allows for the generation of more appropriate prompts by determining prompt priority based on the submission timing of the input content. 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 input content submission timing data into AI and have the AI ​​determine the prompt priority.

[0044] The generation unit can adjust the order of prompts based on the relevance of the input content when generating prompts. For example, the generation unit can prioritize generating prompts for important and relevant input content. It can also generate prompts in the normal order for generally relevant input content. Furthermore, the generation unit can dynamically adjust the order of prompts according to specific relevance. This allows for the generation of more appropriate prompts by adjusting the order of prompts based on the relevance of the input content. 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 relevance data of the input content into the AI ​​and have the AI ​​perform the adjustment of the prompt order.

[0045] The suggestion unit can adjust the level of detail in a message based on the importance of the input content when generating a message. For example, the suggestion unit can generate a detailed message for important input content. It can also generate a concise message for general input content. Furthermore, the suggestion unit can dynamically adjust the level of detail in a message depending on the specific situation. This allows for the generation of more appropriate messages by adjusting the level of detail based on the importance of the input content. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input importance data of the input content into AI and have the AI ​​perform the adjustment of the level of detail in the message.

[0046] The suggestion unit can apply different generation algorithms depending on the category of the input content when generating messages. For example, the suggestion unit can apply a business-oriented generation algorithm to business-related input content. It can also apply a private-use generation algorithm to private input content. Furthermore, the suggestion unit can dynamically select the optimal generation algorithm depending on a specific category. This allows for the generation of more appropriate messages by applying different generation algorithms depending on the input content category. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the input content category data into an AI and have the AI ​​apply the generation algorithm.

[0047] The suggestion unit can determine message priority based on the submission timing of the input content when generating messages. For example, the suggestion unit can prioritize messages for urgent input content. It can also generate messages with normal priority for general input content. Furthermore, the suggestion unit can dynamically adjust message priority according to specific submission timings. This allows for the generation of more appropriate messages by determining message priority based on the submission timing of the input content. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input input content submission timing data into AI and have the AI ​​determine message priority.

[0048] The suggestion unit can adjust the order of messages based on the relevance of the input content when generating messages. For example, the suggestion unit can prioritize generating messages for important and relevant input content. It can also generate messages in the normal order for generally relevant input content. Furthermore, the suggestion unit can dynamically adjust the order of messages according to specific relevance. This allows for the generation of more appropriate messages by adjusting the order of messages based on the relevance of the input content. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input relevance data of the input content into AI and have the AI ​​perform the message order adjustment.

[0049] The suggestion unit can analyze the user's past purchase history to make appropriate suggestions. For example, it can suggest related products based on items the user has purchased in the past. It can also prioritize suggesting products of specific brands or categories based on the user's purchase history. Furthermore, it can analyze the user's past purchase patterns to make the most suitable suggestions. This enables optimal suggestions by analyzing the user's past purchase history. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past purchase history data into AI and have the AI ​​perform the execution of appropriate suggestions.

[0050] The suggestion unit can customize the suggested content based on the user's current living situation. For example, when a user inputs their current living situation, the suggestion unit can prioritize displaying suggestions relevant to that situation. The suggestion unit can also automatically filter relevant suggestions based on the user's living situation. Furthermore, if the user is in a specific living situation, the suggestion unit can provide suggestions that are best suited to that situation. This allows for more appropriate suggestions by customizing the suggestions based on the user's current living situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's current living situation data into AI and have the AI ​​customize the suggested content.

[0051] The suggestion unit can prioritize displaying highly relevant suggestions by considering the user's geographical location when making suggestions. For example, if the user is in a specific location, the suggestion unit can prioritize displaying suggestions related to that location. The suggestion unit can also automatically filter relevant suggestions based on the user's geographical location. Furthermore, if the user is on the move, the suggestion unit can provide optimal suggestions based on their current location. This enables more appropriate suggestions by displaying highly relevant suggestions based on the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's geographical location data into AI and have the AI ​​display highly relevant suggestions.

[0052] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can provide relevant suggestions based on information the user has shared on social media. It can also analyze the user's areas of interest from their social media activity and provide optimal suggestions. Furthermore, if the user is active on a particular social media platform, the suggestion unit can prioritize displaying suggestions related to that platform. This makes it possible to make relevant suggestions by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into an AI and have the AI ​​display relevant suggestions.

[0053] The visualization unit can analyze the user's past communication history and select the optimal visualization method during visualization. For example, the visualization unit can suggest the optimal visualization method based on the visualization methods the user has used in the past. It can also select the most effective visualization method from the user's past communication history. Furthermore, the visualization unit can analyze the user's past history and provide a visualization method tailored to specific situations. This allows for the selection of the optimal visualization method by analyzing the user's past communication history. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's past communication history data into AI and have the AI ​​select the optimal visualization method.

[0054] The visualization unit can select the optimal visualization method based on the user's current situation and areas of interest during visualization. For example, when the user inputs their current situation, the visualization unit can prioritize displaying visualization methods related to that situation. The visualization unit can also automatically filter related visualization methods based on the user's areas of interest. Furthermore, if the user is in a specific situation, the visualization unit can provide the most suitable visualization method for that situation. This enables more appropriate visualization by selecting the optimal visualization method based on the user's current situation and areas of interest. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data on the user's current situation and areas of interest into the AI ​​and have the AI ​​select the optimal visualization method.

[0055] The visualization unit can select the optimal visualization method when visualizing data, taking into account the user's geographical location. For example, if the user is in a specific location, the visualization unit can prioritize displaying visualization methods related to that location. The visualization unit can also automatically filter relevant visualization methods based on the user's geographical location. Furthermore, if the user is on the move, the visualization unit can provide the optimal visualization method based on their current location. This allows for more appropriate visualization by selecting the optimal visualization method based on the user's geographical location. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's geographical location data into AI and have the AI ​​select the optimal visualization method.

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

[0057] The reception desk can analyze the user's past message history when receiving user input and provide advice based on past successes and failures. For example, if a user previously entered "I want to become friends with this person" and succeeded, the reception desk can provide advice based on that success. Also, if a user previously entered "I want to decline the invitation depending on the content" and failed, the reception desk can suggest improvements based on that failure. Furthermore, if a user enters "I want to find out about the other person's hobbies in a neutral way," the reception desk can provide specific question examples based on similar past interactions. This allows users to send more effective messages by leveraging their past experiences. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past message history data into an AI and have the AI ​​provide advice.

[0058] The suggestion section, when outputting appropriate messages using generating AI, can analyze the user's past message history and provide advice based on past successes and failures. For example, if a user previously entered "I want to become friends with this person" and succeeded, the suggestion section can suggest a message based on that success. Also, if a user previously entered "I want to decline the invitation depending on the content" and failed, the suggestion section can suggest improvements based on that failure. Furthermore, if a user entered "I want to find out about the other person's hobbies in a neutral way," the suggestion section can provide specific question examples based on similar past interactions. This allows users to send more effective messages by leveraging their past experiences. Some or all of the above processing in the suggestion section may be performed using AI, or not. For example, the suggestion section can input the user's past message history data into the AI ​​and have the AI ​​provide advice.

[0059] The visualization unit can analyze a user's past message history to visualize their communication trends and provide advice based on past successes and failures. For example, if a user previously entered "I want to become friends with this person" and succeeded, the visualization unit can provide a visualization based on that success. Similarly, if a user previously entered "I want to decline the invitation depending on the content" and failed, the visualization unit can visualize areas for improvement based on that failure. Furthermore, if a user entered "I want to find out about the other person's hobbies in a neutral way," the visualization unit can visualize specific question examples based on similar past interactions. This allows users to leverage past experiences to communicate more effectively. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not. For example, the visualization unit can input the user's past message history data into AI and have the AI ​​provide advice.

[0060] The reception desk can analyze the user's past input history and suggest appropriate input methods. For example, it can automatically display content that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest input content that the user will use at specific times based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into AI and have the AI ​​suggest the optimal input method.

[0061] The reception unit can filter input content based on the user's current situation and areas of interest during input. For example, when a user enters their current situation, the reception unit can prioritize displaying input content related to that situation. The reception unit can also automatically filter relevant input content based on the user's areas of interest. Furthermore, if the user is in a specific situation, the reception unit can suggest input content that is most suitable for that situation. This allows for more relevant input by filtering input content based on the user's situation and areas of interest. 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 data on the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the filtering of input content.

[0062] The reception unit can prioritize displaying highly relevant input content by considering the user's geographical location during input. For example, if the user is in a specific location, it can prioritize displaying input content related to that location. The reception unit can also automatically filter relevant input content based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can suggest the most appropriate input content based on their current location. This enables more appropriate input by displaying highly relevant input content based on 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 the AI ​​and have the AI ​​display highly relevant input content.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives user input. User input includes information such as past interactions, the current interaction, and what the user wants to achieve. The reception desk displays boxes for the user to enter their goals, such as "I want to become friends with this person" or "I may decline the invitation depending on the content." Step 2: The generation unit generates a prompt based on the information received by the reception unit. The prompt is generated in the following format, for example: "###This is the exchange we are having.\nContent of this exchange\n###From this exchange, I would like to achieve the following\nInput of what you want to achieve###Please interpret the relationship from the following exchange with the other party\nPast exchange" + instruction "Please output an appropriate message as a user message based on the above content." Step 3: The suggestion unit inputs the prompt generated by the generation unit into the generation AI and outputs an appropriate message. Appropriate messages may include specific messages such as, "I want to find out about the other person's hobbies in a neutral way," or "I want to invite them to a movie."

[0065] (Example of form 2) The message suggestion system according to an embodiment of the present invention is a system that uses a generating AI to suggest messages that help the user achieve goals such as "I want to reach this state in the end" or "I want to build this kind of relationship" in message exchanges. When the user is exchanging chat messages, this system displays boxes for inputting past exchanges, the current exchange, and what the user wants to achieve. For example, the user inputs a goal such as "I want to become friends with this person" or "I want to decline the invitation depending on the content." Next, the system extracts this information and generates prompts to input into the generating AI. Specifically, it generates prompts in the format of "###This is the exchange we are having.\nContent of this exchange\n###From this exchange, I want to achieve the following\nInput of what you want to achieve###Please read the relationship from the following exchange with the other party\nPast exchanges" + instruction "Please output an appropriate message as a message from the user based on the above content." The generated prompts are input into the generating AI, which outputs an appropriate message. For example, specific messages such as "I want to find out about the other person's hobbies in a neutral way" or "I want to invite them to a movie" are suggested. Furthermore, this system provides two business opportunities. First, if service reservations or purchases are included as suggested options, the system provides links to external services. For example, it can suggest a link to reserve a specific service. Second, it visualizes user communication trends, which can be used for future business ideas. For example, based on data such as "people in their 40s often seek advice on making new friends," a business targeting people in their 40s for making friends can be developed. This allows users to send effective messages toward their goals without consuming a lot of time, and also gain business opportunities. In short, the message suggestion system enables users to send effective messages toward their goals.

[0066] The message suggestion system according to the embodiment comprises a reception unit, a generation unit, and a suggestion unit. The reception unit receives user input. User input includes, but is not limited to, information such as past interactions, the current interaction, and what the user wants to achieve. The reception unit displays a box for the user to enter a goal, such as "I want to become friends with this person" or "I want to decline the invitation depending on the content." The generation unit uses a generation AI to generate a prompt based on the information received by the reception unit. The prompt is generated in the format, but is not limited to, "###This is the interaction we are having.\nContent of the current interaction\n###From this interaction, I want the following to happen\nInput of what you want to achieve###Please read the relationship from the following interaction with the other party\nPast interactions" + instruction "Please output an appropriate message as a message from the user based on the above content." The generation unit uses a generation AI to generate the prompt. The suggestion unit inputs the prompt generated by the generation unit into the generation AI and outputs an appropriate message. Appropriate messages may include specific examples such as "I want to politely find out about the other person's hobbies" or "I want to invite them to a movie," but are not limited to such examples. The suggestion unit uses a generation AI to output appropriate messages. As a result, the message suggestion system according to this embodiment can generate appropriate messages based on user input.

[0067] The reception section receives user input. User input includes, but is not limited to, information such as past interactions, current interactions, and the user's desired outcome. Specifically, users can input the content of past messages, details of current interactions, and even the type of relationship or goals they aim for. For example, the system displays boxes for users to enter goals such as "I want to become friends with this person" or "I may decline the invitation depending on the content." This allows users to clearly communicate their intentions and desires to the system. Furthermore, the reception section also plays a role in organizing the information entered by users and passing it to the generation section in an appropriate format. For example, it analyzes the text entered by the user and extracts important keywords and phrases so that the generation section can efficiently generate prompts. The reception section also has a function to save user input for later reference. This allows users to review past input and make corrections or additions as needed. In addition, the reception section can provide appropriate input guides and support based on the user's input. For example, if a user is unsure about what to input, it can provide specific examples and hints to support smooth input. This allows the reception desk to efficiently and effectively receive user input, improving the overall performance of the system.

[0068] The generation unit uses a generation AI to generate prompts based on information received by the reception unit. Prompts are generated in a format such as, for example, "###This is the exchange we are having.\nContent of this exchange\n###From this exchange, I would like to achieve the following\nInput of what you want to achieve###Please interpret the relationship from the following exchange with the other party\nPast exchange" + instruction "Please output an appropriate message as a message from the user based on the above content," but are not limited to this example. Specifically, the generation unit automatically constructs an appropriate prompt based on the information entered by the user. The generation AI utilizes natural language processing technology to accurately understand the user's intentions and desires and generates the optimal prompt based on that. For example, if the user enters "I want to become friends with this person," the generation unit will consider past exchanges and the current situation and generate a prompt that suggests a specific message to deepen the relationship with the other party. In addition, the generation unit appropriately formats the user's input content during the prompt generation process so that the generation AI can process it efficiently. This allows the generation unit to make the most of the user's input content and generate the optimal prompt. Furthermore, the generation unit also has a function to save the generated prompts so that they can be referenced later. This allows users to review previously generated prompts and make corrections or additions as needed. This enables the generation unit to process user input efficiently and effectively, improving overall system performance.

[0069] The suggestion unit inputs prompts generated by the generation unit into the generation AI and outputs an appropriate message. Appropriate messages may include specific examples such as "I want to politely find out about the other person's hobbies" or "I want to invite them to a movie," but are not limited to these examples. Specifically, the suggestion unit inputs prompts generated by the generation unit into the generation AI, which then generates the optimal message based on those prompts. The generation AI utilizes natural language generation technology to automatically create messages that align with the user's intentions and desires. For example, if the user inputs "I want to politely find out about the other person's hobbies," the generation AI considers past interactions and the current situation to generate a message that naturally asks about the other person's hobbies. The suggestion unit also presents the generated message to the user, allowing them to review it and modify or add to it as needed. This helps the suggestion unit easily create the optimal message. Furthermore, the suggestion unit has a function to save generated messages for later reference. This allows users to review previously generated messages and modify or add to them as needed. This allows the suggestion section to process user input efficiently and effectively, improving the overall system performance.

[0070] The generation unit can generate prompts using a generative AI. For example, the generation unit uses the generative AI to generate prompts based on user input. For example, if the user inputs "I want to become friends with this person," the generation unit can generate a prompt based on that goal. Also, if the user inputs "I want to decline the invitation depending on the content," the generation unit can generate a prompt based on that goal. Furthermore, if the user inputs "I want to politely find out about the other person's hobbies," the generation unit can generate a prompt based on that goal. This improves the accuracy of prompt generation by using a generative AI. The generative AI can generate prompts using, for example, a specific algorithm or model. Some or all of the above-described processes in the generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generation unit can input user input into the generative AI and have the generative AI generate prompts.

[0071] The suggestion unit can output appropriate messages using a generative AI. For example, the suggestion unit uses a generative AI to output appropriate messages based on user input. For example, if the user inputs "I want to become friends with this person," the suggestion unit can output an appropriate message based on that goal. Also, if the user inputs "I want to decline the invitation depending on the content," the suggestion unit can output an appropriate message based on that goal. Furthermore, if the user inputs "I want to politely find out about the other person's hobbies," the suggestion unit can output an appropriate message based on that goal. In this way, using a generative AI improves the accuracy of message output. The generative AI can output messages using, for example, a specific algorithm or model. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input a prompt generated by the generation unit into the generative AI and cause the generative AI to output an appropriate message.

[0072] The message suggestion system includes a suggestion section that proposes service reservations and purchases. For example, when a user is exchanging messages, the suggestion section can suggest a link to reserve a specific service. If the user inputs "I want to become friends with this person," the suggestion section can suggest a restaurant reservation link based on that goal. Similarly, if the user inputs "I might decline the invitation depending on the content," the suggestion section can suggest a movie ticket purchase link based on that goal. Furthermore, if the user inputs "I want to politely find out about the other person's hobbies," the suggestion section can suggest a reservation link for an event related to those hobbies based on that goal. This improves user convenience by suggesting service reservations and purchases. Some or all of the above processing in the suggestion section may be performed using AI, or not. For example, the suggestion section can input user input into AI and have the AI ​​execute service reservation and purchase suggestions.

[0073] The message suggestion system includes a visualization unit that visualizes the user's communication trends. The visualization unit can, for example, analyze the frequency and content of the user's message exchanges and display them as graphs and charts. For instance, if a user inputs "I want to become friends with this person," the visualization unit can visualize the frequency and content of past exchanges based on that goal. Similarly, if a user inputs "I want to decline the invitation depending on its content," the visualization unit can visualize the trends of past exchanges based on that goal. Furthermore, if a user inputs "I want to politely find out about the other person's hobbies," the visualization unit can visualize the content of past exchanges based on that goal. This visualization of the user's communication trends enables the utilization of business ideas. Some or all of the above-described processes in the visualization unit may be performed using AI, or not. For example, the visualization unit can input user message exchange data into an AI and have the AI ​​perform the visualization of communication trends.

[0074] The reception desk can estimate the user's emotions and adjust the priority of input content based on the estimated emotions. For example, if the user is nervous, the reception desk can display important input items first and simpler items later. If the user is relaxed, the reception desk can also prioritize displaying detailed input items and provide a customizable input method. Furthermore, if the user is in a hurry, the reception desk can display only the most important input items to allow for quick completion. This allows for more appropriate input by adjusting the priority of input content 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 desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception desk can analyze the user's past input history and suggest appropriate input methods. For example, the reception desk can automatically display as suggestions content that the user has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest input content to be used during specific time periods based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into AI and have the AI ​​suggest the optimal input method.

[0076] The reception unit can filter input content based on the user's current situation and areas of interest during input. For example, when a user enters their current situation, the reception unit can prioritize displaying input content relevant to that situation. The reception unit can also automatically filter relevant input content based on the user's areas of interest. Furthermore, if the user is in a specific situation, the reception unit can suggest input content that is most suitable for that situation. This allows for more relevant input by filtering input content based on the user's situation and areas of interest. 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 data on the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the filtering of input content.

[0077] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. By adjusting the display method of the input interface according to the user's emotions, a more comfortable input experience is possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The reception unit can prioritize displaying highly relevant input content by considering the user's geographical location during input. For example, if the user is in a specific location, the reception unit can prioritize displaying input content related to that location. The reception unit can also automatically filter relevant input content based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can suggest the most appropriate input content based on their current location. This enables more appropriate input by displaying highly relevant input content based on 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 the AI ​​and have the AI ​​display highly relevant input content.

[0079] The reception unit can analyze the user's social media activity during input and suggest relevant input content. For example, the reception unit can suggest relevant input content based on information the user has shared on social media. It can also analyze the user's areas of interest from their social media activity and suggest the most suitable input content. Furthermore, if the user is active on a particular social media platform, the reception unit can prioritize displaying input content relevant to that platform. This allows the reception unit to suggest relevant input content 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 without AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​suggest relevant input content.

[0080] The generation unit can estimate the user's emotions and adjust the prompt generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate detailed prompts and provide customizable messages. If the user is in a hurry, the generation unit can also generate concise and to-the-point prompts. Furthermore, if the user is excited, the generation unit can generate visually stimulating prompts. By adjusting the prompt generation method according to the user's emotions, more appropriate prompts are generated. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI perform prompt generation.

[0081] The generation unit can adjust the level of detail of prompts based on the importance of the input content when generating prompts. For example, the generation unit can generate detailed prompts for important input content. It can also generate concise prompts for general input content. Furthermore, the generation unit can dynamically adjust the level of detail of prompts depending on the specific situation. This allows for the generation of more appropriate prompts by adjusting the level of detail of prompts based on the importance of the input content. 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 data on the importance of the input content into the AI ​​and have the AI ​​perform the adjustment of the level of detail of prompts.

[0082] The generation unit can apply different generation algorithms depending on the category of the input content when generating prompts. For example, the generation unit can apply a business-oriented generation algorithm to business-related input content. It can also apply a private-use generation algorithm to private input content. Furthermore, the generation unit can dynamically select the optimal generation algorithm depending on a specific category. This allows for the generation of more appropriate prompts by applying different generation algorithms depending on the input content 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 the input content category data into the AI ​​and have the AI ​​apply the generation algorithm.

[0083] The generation unit can estimate the user's emotions and adjust the length of the prompt based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise prompt. If the user is relaxed, the generation unit can generate a longer prompt with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a prompt with visually stimulating effects. By adjusting the length of the prompt according to the user's emotions, more appropriate prompts are generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the prompt.

[0084] The generation unit can determine the priority of prompts based on the submission timing of the input content when generating prompts. For example, the generation unit can prioritize prompts for urgent input content. It can also generate prompts with normal priority for general input content. Furthermore, the generation unit can dynamically adjust the prompt priority according to specific submission timings. This allows for the generation of more appropriate prompts by determining prompt priority based on the submission timing of the input content. 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 input content submission timing data into AI and have the AI ​​determine the prompt priority.

[0085] The generation unit can adjust the order of prompts based on the relevance of the input content when generating prompts. For example, the generation unit can prioritize generating prompts for important and relevant input content. It can also generate prompts in the normal order for generally relevant input content. Furthermore, the generation unit can dynamically adjust the order of prompts according to specific relevance. This allows for the generation of more appropriate prompts by adjusting the order of prompts based on the relevance of the input content. 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 relevance data of the input content into the AI ​​and have the AI ​​perform the adjustment of the prompt order.

[0086] The suggestion unit can estimate the user's emotions and adjust the message's expression based on those emotions. For example, if the user is relaxed, the suggestion unit can provide a friendly expression. If the user is tense, it can provide a formal expression. Furthermore, if the user is excited, it can provide an energetic expression. By adjusting the message's expression according to the user's emotions, a more appropriate message is generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the message's expression.

[0087] The suggestion unit can adjust the level of detail in a message based on the importance of the input content when generating a message. For example, the suggestion unit can generate a detailed message for important input content. It can also generate a concise message for general input content. Furthermore, the suggestion unit can dynamically adjust the level of detail in a message depending on the specific situation. This allows for the generation of more appropriate messages by adjusting the level of detail based on the importance of the input content. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input importance data of the input content into AI and have the AI ​​perform the adjustment of the level of detail in the message.

[0088] The suggestion unit can apply different generation algorithms depending on the category of the input content when generating messages. For example, the suggestion unit can apply a business-oriented generation algorithm to business-related input content. It can also apply a private-use generation algorithm to private input content. Furthermore, the suggestion unit can dynamically select the optimal generation algorithm depending on a specific category. This allows for the generation of more appropriate messages by applying different generation algorithms depending on the input content category. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the input content category data into an AI and have the AI ​​apply the generation algorithm.

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

[0090] The suggestion unit can determine message priority based on the submission timing of the input content when generating messages. For example, the suggestion unit can prioritize messages for urgent input content. It can also generate messages with normal priority for general input content. Furthermore, the suggestion unit can dynamically adjust message priority according to specific submission timings. This allows for the generation of more appropriate messages by determining message priority based on the submission timing of the input content. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input input content submission timing data into AI and have the AI ​​determine message priority.

[0091] The suggestion unit can adjust the order of messages based on the relevance of the input content when generating messages. For example, the suggestion unit can prioritize generating messages for important and relevant input content. It can also generate messages in the normal order for generally relevant input content. Furthermore, the suggestion unit can dynamically adjust the order of messages according to specific relevance. This allows for the generation of more appropriate messages by adjusting the order of messages based on the relevance of the input content. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input relevance data of the input content into AI and have the AI ​​perform the message order adjustment.

[0092] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can prioritize displaying detailed suggestions. If the user is in a hurry, it can display only the most important suggestions. Furthermore, if the user is excited, it can prioritize displaying visually stimulating suggestions. This allows for more appropriate suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0093] The suggestion unit can analyze the user's past purchase history to make appropriate suggestions. For example, it can suggest related products based on items the user has purchased in the past. It can also prioritize suggesting products of specific brands or categories based on the user's purchase history. Furthermore, it can analyze the user's past purchase patterns to make the most suitable suggestions. This enables optimal suggestions by analyzing the user's past purchase history. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past purchase history data into AI and have the AI ​​perform the execution of appropriate suggestions.

[0094] The suggestion unit can customize the suggested content based on the user's current living situation. For example, when a user inputs their current living situation, the suggestion unit can prioritize displaying suggestions relevant to that situation. The suggestion unit can also automatically filter relevant suggestions based on the user's living situation. Furthermore, if the user is in a specific living situation, the suggestion unit can provide suggestions that are best suited to that situation. This allows for more appropriate suggestions by customizing the suggestions based on the user's current living situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's current living situation data into AI and have the AI ​​customize the suggested content.

[0095] The suggestion unit can estimate the user's emotions and adjust how suggestions are displayed based on those emotions. For example, if the user is feeling stressed, the suggestion unit can display suggestions using a calming color scheme. If the user is enjoying themselves, the suggestion unit can display suggestions using a bright color scheme. Furthermore, if the user is tired, the suggestion unit can display suggestions using a simple and highly visible interface. By adjusting how suggestions are displayed according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust how suggestions are displayed.

[0096] The suggestion unit can prioritize displaying highly relevant suggestions by considering the user's geographical location when making suggestions. For example, if the user is in a specific location, the suggestion unit can prioritize displaying suggestions related to that location. The suggestion unit can also automatically filter relevant suggestions based on the user's geographical location. Furthermore, if the user is on the move, the suggestion unit can provide optimal suggestions based on their current location. This enables more appropriate suggestions by displaying highly relevant suggestions based on the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's geographical location data into AI and have the AI ​​display highly relevant suggestions.

[0097] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can provide relevant suggestions based on information the user has shared on social media. It can also analyze the user's areas of interest from their social media activity and provide optimal suggestions. Furthermore, if the user is active on a particular social media platform, the suggestion unit can prioritize displaying suggestions related to that platform. This makes it possible to make relevant suggestions by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into an AI and have the AI ​​display relevant suggestions.

[0098] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated user emotions. For example, if the user is tense, the visualization unit can display graphs or charts with calm colors. If the user is enjoying themselves, the visualization unit can display graphs or charts with bright colors. Furthermore, if the user is tired, the visualization unit can display simple and highly visible graphs or charts. By adjusting the display method of the visualization according to the user's emotions, more appropriate visualization becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the visualization.

[0099] The visualization unit can analyze the user's past communication history and select the optimal visualization method during visualization. For example, the visualization unit can suggest the optimal visualization method based on the visualization methods the user has used in the past. It can also select the most effective visualization method from the user's past communication history. Furthermore, the visualization unit can analyze the user's past history and provide a visualization method tailored to specific situations. This allows for the selection of the optimal visualization method by analyzing the user's past communication history. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's past communication history data into AI and have the AI ​​select the optimal visualization method.

[0100] The visualization unit can select the optimal visualization method based on the user's current situation and areas of interest during visualization. For example, when the user inputs their current situation, the visualization unit can prioritize displaying visualization methods related to that situation. The visualization unit can also automatically filter related visualization methods based on the user's areas of interest. Furthermore, if the user is in a specific situation, the visualization unit can provide the most suitable visualization method for that situation. This enables more appropriate visualization by selecting the optimal visualization method based on the user's current situation and areas of interest. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data on the user's current situation and areas of interest into the AI ​​and have the AI ​​select the optimal visualization method.

[0101] The visualization unit can estimate the user's emotions and determine the priority of visualizations based on the estimated emotions. For example, if the user is relaxed, the visualization unit can prioritize displaying detailed visualizations. If the user is in a hurry, the visualization unit can also display only the most important visualizations. Furthermore, if the user is excited, the visualization unit can prioritize displaying visually stimulating visualizations. This allows for more appropriate visualizations by determining the priority of visualizations 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 visualization unit may be performed using AI, or not using AI. For example, the visualization unit can input user emotion data into a generative AI and have the generative AI determine the priority of visualizations.

[0102] The visualization unit can select the optimal visualization method when visualizing data, taking into account the user's geographical location. For example, if the user is in a specific location, the visualization unit can prioritize displaying visualization methods related to that location. The visualization unit can also automatically filter relevant visualization methods based on the user's geographical location. Furthermore, if the user is on the move, the visualization unit can provide the optimal visualization method based on their current location. This allows for more appropriate visualization by selecting the optimal visualization method based on the user's geographical location. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's geographical location data into AI and have the AI ​​select the optimal visualization method.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The reception desk can analyze the user's past message history when receiving user input and provide advice based on past successes and failures. For example, if a user previously entered "I want to become friends with this person" and succeeded, the reception desk can provide advice based on that success. Also, if a user previously entered "I want to decline the invitation depending on the content" and failed, the reception desk can suggest improvements based on that failure. Furthermore, if a user enters "I want to find out about the other person's hobbies in a neutral way," the reception desk can provide specific question examples based on similar past interactions. This allows users to send more effective messages by leveraging their past experiences. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past message history data into an AI and have the AI ​​provide advice.

[0105] The generation unit can adjust the content of prompts by considering the user's current emotional state when generating prompts using a generation AI. For example, if the user is tense, the generation unit can generate a prompt with relaxing content. If the user is relaxed, the generation unit can also generate a prompt with detailed information. Furthermore, if the user is excited, the generation unit can generate an energetic prompt. By adjusting the content of prompts according to the user's emotional state, more appropriate prompts are generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation 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 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 adjust the content of prompts.

[0106] The suggestion section, when outputting appropriate messages using generating AI, can analyze the user's past message history and provide advice based on past successes and failures. For example, if a user previously entered "I want to become friends with this person" and succeeded, the suggestion section can suggest a message based on that success. Also, if a user previously entered "I want to decline the invitation depending on the content" and failed, the suggestion section can suggest improvements based on that failure. Furthermore, if a user entered "I want to find out about the other person's hobbies in a neutral way," the suggestion section can provide specific question examples based on similar past interactions. This allows users to send more effective messages by leveraging their past experiences. Some or all of the above processing in the suggestion section may be performed using AI, or not. For example, the suggestion section can input the user's past message history data into the AI ​​and have the AI ​​provide advice.

[0107] The suggestion unit can adjust its suggestions when proposing service reservations or purchases, taking into account the user's current emotional state. For example, if the user is relaxed, the suggestion unit can provide detailed service information. If the user is in a hurry, the suggestion unit can provide only the most important service information. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating service information. By adjusting the suggestions according to the user's emotional state, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's emotional data into a generative AI and have the generative AI adjust the suggestion content.

[0108] The visualization unit can analyze a user's past message history to visualize their communication trends and provide advice based on past successes and failures. For example, if a user previously entered "I want to become friends with this person" and succeeded, the visualization unit can provide a visualization based on that success. Similarly, if a user previously entered "I want to decline the invitation depending on the content" and failed, the visualization unit can visualize areas for improvement based on that failure. Furthermore, if a user entered "I want to find out about the other person's hobbies in a neutral way," the visualization unit can visualize specific question examples based on similar past interactions. This allows users to leverage past experiences to communicate more effectively. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not. For example, the visualization unit can input the user's past message history data into AI and have the AI ​​provide advice.

[0109] The reception desk can estimate the user's emotions and adjust the priority of input content based on the estimated emotions. For example, if the user is nervous, important input items can be displayed first, and simpler items can be displayed later. If the user is relaxed, the reception desk can also prioritize detailed input items and provide a customizable input method. Furthermore, if the user is in a hurry, the reception desk can display only the most important input items, allowing for quick completion. This allows for more appropriate input by adjusting the priority of input content 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 desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The reception desk can analyze the user's past input history and suggest appropriate input methods. For example, it can automatically display content that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest input content that the user will use at specific times based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into AI and have the AI ​​suggest the optimal input method.

[0111] The reception unit can filter input content based on the user's current situation and areas of interest during input. For example, when a user enters their current situation, the reception unit can prioritize displaying input content related to that situation. The reception unit can also automatically filter relevant input content based on the user's areas of interest. Furthermore, if the user is in a specific situation, the reception unit can suggest input content that is most suitable for that situation. This allows for more relevant input by filtering input content based on the user's situation and areas of interest. 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 data on the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the filtering of input content.

[0112] The reception unit can estimate the user's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the user is tense, it can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. This allows for more comfortable input by adjusting the display of the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception 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.

[0113] The reception unit can prioritize displaying highly relevant input content by considering the user's geographical location during input. For example, if the user is in a specific location, it can prioritize displaying input content related to that location. The reception unit can also automatically filter relevant input content based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can suggest the most appropriate input content based on their current location. This enables more appropriate input by displaying highly relevant input content based on 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 the AI ​​and have the AI ​​display highly relevant input content.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The reception desk receives user input. User input includes information such as past interactions, the current interaction, and what the user wants to achieve. The reception desk displays boxes for the user to enter their goals, such as "I want to become friends with this person" or "I may decline the invitation depending on the content." Step 2: The generation unit generates a prompt based on the information received by the reception unit. The prompt is generated in the following format, for example: "###This is the exchange we are having.\nContent of this exchange\n###From this exchange, I would like to achieve the following\nInput of what you want to achieve###Please interpret the relationship from the following exchange with the other party\nPast exchange" + instruction "Please output an appropriate message as a user message based on the above content." Step 3: The suggestion unit inputs the prompt generated by the generation unit into the generation AI and outputs an appropriate message. Appropriate messages may include specific messages such as, "I want to find out about the other person's hobbies in a neutral way," or "I want to invite them to a movie."

[0116] 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.

[0117] 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.

[0118] 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.

[0119] For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives user input. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates prompts using a generation AI. The suggestion unit is implemented by the control unit 46A of the smart device 14 and inputs the generated prompts into the generation AI and outputs an appropriate message. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes service reservations and purchases. The visualization unit is implemented by the specific processing unit 290 of the data processing device 12 and visualizes the user's communication trends. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives user input. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates prompts using a generation AI. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 and inputs the generated prompts into the generation AI and outputs an appropriate message. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes service reservations and purchases. The visualization unit is implemented by the specific processing unit 290 of the data processing device 12 and visualizes the user's communication trends. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives user input. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates prompts using a generation AI. The suggestion unit is implemented by the control unit 46A of the headset terminal 314 and inputs the generated prompts into the generation AI and outputs an appropriate message. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes service reservations and purchases. The visualization unit is implemented by the specific processing unit 290 of the data processing device 12 and visualizes the user's communication trends. 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.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives user input. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates prompts using a generation AI. The suggestion unit is implemented by the control unit 46A of the robot 414 and inputs the generated prompts into the generation AI and outputs an appropriate message. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes service reservations and purchases. The visualization unit is implemented by the specific processing unit 290 of the data processing device 12 and visualizes the user's communication trends. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Note 1) A reception area that receives user input, A generation unit that generates a prompt based on the information received by the reception unit, The system includes a suggestion unit that inputs the prompt generated by the generation unit to a generation AI and outputs an appropriate message. A system characterized by the following features. (Note 2) The generating unit is Generate prompts using a generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned suggestion section is, The AI ​​generates the appropriate message. The system described in Appendix 1, characterized by the features described herein. (Note 4) The system described in Appendix 1, characterized by having a proposal section that suggests specific service reservations or purchases. (Note 5) The system described in Appendix 1, characterized by having a visualization unit that visualizes specific user communication trends. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the priority of input content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests appropriate input methods. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When inputting information, the system filters the input 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 adjusts how the input interface is displayed 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 users input data, the system prioritizes displaying relevant input content by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During input, the system analyzes the user's social media activity and suggests relevant input content. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts how prompts are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating prompts, adjust the level of detail of the prompts based on the importance of the input. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating prompts, different generation algorithms are applied depending on the category of the input content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the prompt length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating prompts, the priority of prompts is determined based on when the input content is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating prompts, adjust the order of prompts based on the relevance of the input content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned suggestion section is, It estimates the user's emotions and adjusts the way messages are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned suggestion section is, When generating a message, adjust the level of detail based on the importance of the input content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned suggestion section is, When generating messages, different generation algorithms are applied depending on the category of the input content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned suggestion section is, It estimates the user's emotions and adjusts the message length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned suggestion section is, When generating a message, the message priority is determined based on when the input content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned suggestion section is, When generating messages, the order of messages is adjusted based on the relevance of the input content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and prioritizes suggestions based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, we analyze the user's past purchase history to provide appropriate suggestions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, customize the proposal based on the user's current living situation. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and adjusts how suggestions are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making suggestions, the system prioritizes displaying highly relevant suggestions by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned visualization unit, It estimates the user's emotions and adjusts the display method of the visualization based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned visualization unit, During visualization, the system analyzes the user's past communication history to select the most suitable visualization method. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned visualization unit, When visualizing data, the optimal visualization method is selected based on the user's current situation and areas of interest. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned visualization unit, It estimates the user's emotions and determines the visualization priority based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned visualization unit, When visualizing data, the optimal visualization method is selected by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0188] 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 area that receives user input, A generation unit that generates a prompt based on the information received by the reception unit, The system includes a suggestion unit that inputs the prompt generated by the generation unit to the generation AI and outputs an appropriate message. A system characterized by the following features.

2. The generating unit is Generate prompts using AI. The system according to feature 1.

3. The aforementioned suggestion section is, The AI ​​generates the appropriate message. The system according to feature 1.

4. The system according to claim 1, characterized in that it includes a proposal unit that proposes specific service reservations or purchases.

5. The system according to claim 1, characterized by comprising a visualization unit that visualizes specific user communication trends.

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

7. The aforementioned reception unit is It analyzes the user's past input history and suggests appropriate input methods. The system according to feature 1.

8. The aforementioned reception unit is When inputting information, the system filters the input based on the user's current situation and areas of interest. The system according to feature 1.

Images