System

Abstract

An object of a system according to an embodiment is to enable generated AI to be appropriately used only in a dialogue.SOLUTION: A system includes an interaction unit, a collection unit, a generation unit, a presentation unit, and a learning unit. The interaction unit receives an interaction from a user. The collection unit collects information on the basis of the dialogue received by the dialogue unit. The generation unit generates a prompt based on the information collected by the collection unit. The presentation unit presents the dialog to the user on the basis of the prompt generated by the generation unit. The learning unit learns the dialogue history received by the dialogue unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback that prompt input to the generated AI is time-consuming and difficult, making it difficult for users to use it appropriately.

[0005] The system according to the embodiment aims to enable appropriate use of generative AI through dialogue alone. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, a collection unit, a generation unit, a presentation unit, and a learning unit. The dialogue unit accepts dialogue from a user. The collection unit collects information based on the dialogue accepted by the dialogue unit. The generation unit generates a prompt based on the information collected by the collection unit. The presentation unit presents a dialogue to the user based on the prompt generated by the generation unit. The learning unit learns the dialogue history accepted by the dialogue unit. [Effects of the Invention]

[0007] The system according to the embodiment can make it possible to appropriately use the generative AI through dialogue alone. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A generative AI system according to an embodiment of the present invention accepts dialogue from a user, has a generative AI understand the user's intent, collects the necessary information, automatically generates appropriate prompts, and presents them to the user. In this system, the user initiates a dialogue with the generative AI, and the generative AI understands the user's intent and collects the necessary information. The generative AI then automatically generates appropriate prompts based on the collected information and presents them to the user. When the user answers these questions, the generative AI collects more detailed information based on the answers, ultimately generating the necessary information. The generative AI system can also present information visually using icons. For example, in this system, a user initiates a dialogue with the generative AI. The generative AI system understands the user's intent and collects the necessary information. The generative AI system automatically generates appropriate prompts based on the collected information and presents them to the user. When the user answers these questions, the generative AI collects more detailed information based on the answers, ultimately generating the necessary information. The generative AI system can also present information visually using icons. This significantly reduces the barrier to using generative AI, allowing anyone to easily utilize generative AI without relying on prompt engineering skills. This allows the generative AI system to learn the user's dialogue history and generate more appropriate prompts in subsequent dialogues.For example, the generative AI system can learn the user's dialogue history and generate more appropriate prompts in subsequent dialogues.This allows the generative AI system to learn the user's dialogue history and generate more appropriate prompts in subsequent dialogues.

[0029] A generative AI system according to an embodiment includes a dialogue unit, a collection unit, a generation unit, a presentation unit, and a learning unit. The dialogue unit receives a dialogue from a user. The dialogue unit can receive the dialogue in the form of, for example, a voice dialogue, a text dialogue, or a gesture dialogue. The dialogue unit can use natural language processing technology or an intention recognition algorithm to understand the user's intention. The collection unit collects information based on the dialogue received by the dialogue unit. The collection unit can collect information by, for example, web scraping, data acquisition from an API, or the like. The collection unit can use natural language processing technology or an intention recognition algorithm to understand the user's intention and collect the necessary information. The generation unit generates a prompt based on the information collected by the collection unit. The generation unit can generate the prompt by, for example, a natural language generation technology or a template-based generation method. The generation unit can use a generative AI (e.g., a text generation AI or a multimodal generation AI) to generate an appropriate prompt based on the collected information. The presentation unit presents a dialogue to the user based on the prompt generated by the generation unit. The presentation unit can present the dialogue by, for example, audio output, text display, or the use of visual elements. The presentation unit can also visually present information using icons to present the generated prompt to the user. The learning unit learns the dialogue history received by the dialogue unit. The learning unit can learn the dialogue history using, for example, a machine learning algorithm, and generate more appropriate prompts for subsequent dialogues. The learning unit can also use a data preprocessing method or a machine learning algorithm to clarify the dialogue history learning method and criteria. This enables the generative AI system according to the embodiment to efficiently collect, generate, present, and learn user dialogues.

[0030] The collection unit can understand the user's intent and collect information. The collection unit can understand the user's intent using, for example, natural language processing technology. For example, the collection unit can analyze the user's utterances and identify the intent. The collection unit can also understand the user's intent using an intent recognition algorithm. For example, the collection unit can analyze the context of the user's utterances and identify the intent. The collection unit can also perform web scraping to collect the required information. For example, the collection unit can collect information from a specific website. The collection unit can also acquire data from an API. For example, the collection unit can collect information using a specific API. In this way, the collection unit can understand the user's intent and accurately collect the required information.

[0031] The generation unit can generate a prompt based on the collected information. The generation unit generates the prompt using, for example, natural language generation technology. For example, the generation unit generates an appropriate prompt based on the collected information. The generation unit can also generate a prompt using template-based generation. For example, the generation unit generates a prompt using a template based on the collected information. The generation unit can also generate a prompt using a generation AI (for example, a text generation AI or a multimodal generation AI). For example, the generation unit inputs the collected information into the generation AI, which generates a prompt. In this way, the generation unit generates an appropriate prompt based on the collected information, enabling a dialogue that is in line with the user's intentions.

[0032] The presentation unit can present the generated prompt to the user. The presentation unit, for example, presents the generated prompt to the user using audio output. For example, the presentation unit conveys the generated prompt to the user by voice. The presentation unit can also present the generated prompt to the user using a text display. For example, the presentation unit displays the generated prompt to the user in text. The presentation unit can also present the generated prompt to the user using a visual element. For example, the presentation unit presents information visually using an icon. In this way, the presentation unit presents the generated prompt to the user, allowing the user to obtain appropriate information.

[0033] The learning unit can learn the dialogue history and generate prompts for subsequent dialogues. The learning unit learns the dialogue history using, for example, a machine learning algorithm. For example, the learning unit analyzes the user's dialogue history and generates more appropriate prompts for subsequent dialogues. The learning unit can also learn the dialogue history using a data preprocessing method. For example, the learning unit preprocesses dialogue history data and inputs it into a machine learning algorithm. The learning unit can also use a data preprocessing method or a machine learning algorithm to clarify the dialogue history learning method and criteria. In this way, the learning unit can learn the dialogue history and generate more appropriate prompts for subsequent dialogues.

[0034] The presentation unit can present information using icons. The presentation unit, for example, presents information using visual symbols. For example, the presentation unit visually presents information using icons. The presentation unit can also present information using color coding. For example, the presentation unit visually distinguishes information using different colors. The presentation unit can also present information using animation. For example, the presentation unit visually emphasizes information using animation. In this way, the presentation unit visually presents information using icons, making it easier for the user to visually confirm the information.

[0035] The dialogue unit can analyze the user's past dialogue history and select a dialogue initiation method. The dialogue unit, for example, analyzes the user's past dialogue history and selects the optimal dialogue initiation method. For example, the dialogue unit can start a dialogue in a similar style based on a dialogue style that the user has preferred in the past. The dialogue unit can also start a dialogue on a related topic based on keywords that the user has frequently used in the past. The dialogue unit can also predict the dialogue content that the user will prefer at a specific time period from the user's past dialogue history and start a dialogue on an appropriate topic. In this way, the dialogue unit can select the optimal dialogue initiation method by analyzing the user's past dialogue history.

[0036] The dialogue unit can adjust the content of the dialogue based on the user's current situation and areas of interest when starting a dialogue. For example, the dialogue unit analyzes the user's current situation and adjusts the content of the dialogue. For example, the dialogue unit starts a dialogue on a topic related to a project the user is currently working on. The dialogue unit can also analyze the user's areas of interest and adjust the content of the dialogue. For example, the dialogue unit starts a dialogue based on a topic that the user has recently become interested in. The dialogue unit can also start a dialogue on a related topic based on the user's current geographic location information. For example, the dialogue unit provides information related to the user's current location. This allows the dialogue unit to customize the content of the dialogue based on the user's current situation and areas of interest, thereby enabling a more appropriate dialogue.

[0037] The dialogue unit can select a dialogue means according to the user's input method at the start of a dialogue. For example, the dialogue unit analyzes the user's input method and selects the optimal dialogue means. For example, if the user prefers voice input, the dialogue unit can prioritize voice dialogue. Also, if the user prefers text input, the dialogue unit can prioritize text dialogue. Also, if the user uses gesture input, the dialogue unit can provide a dialogue means corresponding to the gesture. For example, the dialogue unit analyzes the user's gestures, understands their intentions, and proceeds with the dialogue. In this way, the dialogue unit can select the optimal dialogue means according to the user's input method, thereby enabling a more appropriate dialogue.

[0038] The dialogue unit can provide dialogue content preferentially by taking into account the user's geographical location information when starting a dialogue. The dialogue unit, for example, analyzes the user's geographical location information and provides highly relevant dialogue content. For example, if the user is in a specific area, the dialogue unit can provide information related to that area. Furthermore, if the user is traveling, the dialogue unit can provide information related to the travel destination. Furthermore, if the user is participating in a specific event, the dialogue unit can provide information related to the event. For example, the dialogue unit can provide detailed information about the event the user is participating in. In this way, the dialogue unit can provide highly relevant dialogue content by taking into account the user's geographical location information.

[0039] The dialogue unit can analyze the user's social media activity and provide the dialogue content at the start of the dialogue. For example, the dialogue unit can analyze the user's social media activity and provide related dialogue content. For example, the dialogue unit can start the dialogue based on the content the user shared on social media. The dialogue unit can also customize the dialogue content by referring to the activity of the user's friends on social media. The dialogue unit can also analyze the content posted by the user on social media and start a dialogue on a related topic. In this way, the dialogue unit can provide related dialogue content by analyzing the user's social media activity.

[0040] The dialogue unit can adjust the dialogue method by reflecting the user's past feedback when starting a dialogue. The dialogue unit, for example, analyzes the user's past feedback and adjusts the dialogue method. For example, the dialogue unit starts a dialogue in a similar style based on a dialogue style that the user has preferred in the past. The dialogue unit can also adjust the dialogue content based on feedback provided by the user in the past. The dialogue unit can also analyze the user's past feedback and suggest an optimal dialogue method. In this way, the dialogue unit can customize the dialogue method by reflecting the user's past feedback.

[0041] The collection unit can analyze the user's past dialogue history and select an information collection method. The collection unit, for example, analyzes the user's past dialogue history and selects the optimal information collection method. For example, the collection unit collects information using a similar method based on the information collection method that the user has preferred in the past. The collection unit can also prioritize a specific information collection method from the user's past dialogue history. The collection unit can also analyze the user's past dialogue history and suggest the most efficient information collection method. In this way, the collection unit can select the optimal information collection method by analyzing the user's past dialogue history.

[0042] When collecting information, the collection unit can select information based on the user's current project or areas of interest. For example, the collection unit analyzes the user's current project and collects related information. For example, the collection unit prioritizes collecting information related to the project the user is currently working on. The collection unit can also analyze the user's areas of interest and filter information. For example, the collection unit filters information based on topics that the user has recently become interested in. The collection unit can also collect related information based on the user's current geographic location information. For example, the collection unit provides information related to the user's current location. This allows the collection unit to filter information based on the user's current project or areas of interest, thereby enabling more appropriate information collection.

[0043] When collecting information, the collection unit can select a collection means according to the user's input method. The collection unit, for example, analyzes the user's input method and selects the optimal collection means. For example, if the user prefers voice input, the collection unit can prioritize voice information collection. Also, if the user prefers text input, the collection unit can also prioritize text information collection. Also, if the user uses image input, the collection unit can provide an information collection means corresponding to the image. For example, the collection unit analyzes the user's image and collects information by understanding the user's intention. This allows the collection unit to select the optimal collection means according to the user's input method, enabling more appropriate information collection.

[0044] When collecting information, the collection unit can prioritize collecting information by taking into account the user's geographical location information. The collection unit, for example, analyzes the user's geographical location information and collects highly relevant information. For example, if the user is in a specific area, the collection unit can prioritize collecting information related to that area. Furthermore, if the user is traveling, the collection unit can also prioritize collecting information related to the travel destination. Furthermore, if the user is participating in a specific event, the collection unit can prioritize collecting information related to the event. For example, the collection unit provides detailed information about the event the user is participating in. This allows the collection unit to prioritize collecting highly relevant information by taking into account the user's geographical location information.

[0045] When collecting information, the collection unit can analyze the user's social media activities and collect the information. For example, the collection unit analyzes the user's social media activities and collects related information. For example, the collection unit collects information based on the content the user has shared on social media. The collection unit can also collect information by referring to the activities of the user's friends on social media. The collection unit can also analyze the content posted by the user on social media and collect related information. In this way, the collection unit can collect related information by analyzing the user's social media activities.

[0046] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting information. The collection unit, for example, analyzes the user's past feedback and adjusts the collection method. For example, the collection unit collects information in a similar manner based on the information collection method that the user has previously preferred. The collection unit can also adjust the information collection method based on feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and suggest an optimal information collection method. In this way, the collection unit can customize the collection method by reflecting the user's past feedback.

[0047] When generating a prompt, the generation unit can determine the level of detail of the prompt based on the importance of the collected information. For example, the generation unit analyzes the importance of the collected information and determines the level of detail of the prompt. For example, the generation unit generates a detailed prompt based on information with high importance. The generation unit can also generate a concise prompt based on information with low importance. The generation unit can also analyze the importance of the collected information and generate a prompt with an optimal level of detail. In this way, the generation unit can generate a more appropriate prompt by adjusting the level of detail of the prompt based on the importance of the collected information.

[0048] The generation unit can apply a generation algorithm depending on the category of information when generating a prompt. For example, the generation unit analyzes the category of information and applies an optimal generation algorithm. For example, the generation unit applies a specialized generation algorithm to technical information. The generation unit can also apply a simple generation algorithm to general information. The generation unit can also analyze the category of information and select an optimal generation algorithm. In this way, the generation unit can generate a more appropriate prompt by applying different generation algorithms depending on the category of information.

[0049] When generating a prompt, the generation unit can improve the accuracy of generation by referring to the user's past prompt results. The generation unit, for example, analyzes the user's past prompt results to improve the accuracy of generation. For example, the generation unit generates a similar prompt based on a prompt result that the user previously preferred. The generation unit can also analyze the user's past prompt results to generate an optimal prompt. The generation unit can also adjust the generation algorithm by referring to the user's past prompt results to improve accuracy. In this way, the generation unit can improve the accuracy of generation by referring to the user's past prompt results.

[0050] When generating prompts, the generation unit can determine the order of prompts based on the time of submission of information. The generation unit, for example, analyzes the time of submission of information and determines the order of prompts. For example, the generation unit generates prompts with priority based on urgent information. The generation unit can also set a high priority based on information whose submission deadline is approaching. The generation unit can also analyze the submission time and generate prompts with an optimal priority. In this way, the generation unit can generate more appropriate prompts by determining the priority of prompts based on the time of submission of information.

[0051] When generating prompts, the generation unit can determine the order of prompts based on the relevance of information. The generation unit, for example, analyzes the relevance of information and determines the order of prompts. For example, the generation unit prioritizes the order of prompts based on highly relevant information. The generation unit can also postpone the order of prompts based on less relevant information. The generation unit can also analyze the relevance of information and generate prompts in an optimal order. In this way, the generation unit can generate more appropriate prompts by adjusting the order of prompts based on the relevance of information.

[0052] When generating a prompt, the generation unit can determine the use of technical terminology in the prompt depending on the user's level of expertise. The generation unit, for example, analyzes the user's level of expertise and determines the optimal use of technical terminology. For example, if the user has specialized knowledge, the generation unit generates a prompt that uses a lot of technical terminology. Alternatively, if the user has only general knowledge, the generation unit can generate a prompt using simple language. The generation unit can also analyze the user's level of expertise and adjust the optimal use of technical terminology. In this way, the generation unit can generate a more appropriate prompt by adjusting the use of technical terminology in the prompt depending on the user's level of expertise.

[0053] The presentation unit can select a display method by referring to the user's past operation history when presenting information. The presentation unit, for example, analyzes the user's past operation history and selects the optimal display method. For example, the presentation unit presents information in a similar manner based on a display method that the user has preferred in the past. The presentation unit can also prioritize a specific display method based on the user's past operation history. The presentation unit can also analyze the user's past operation history and suggest the most efficient display method. In this way, the presentation unit can select the optimal display method by referring to the user's past operation history.

[0054] The presentation unit can adjust the display content according to the user's current task when presenting the information. For example, the presentation unit analyzes the user's current task and adjusts the display content. For example, the presentation unit prioritizes displaying information related to a project the user is currently working on. The presentation unit can also customize the display content based on topics that the user has recently been interested in. The presentation unit can also display related information based on the user's current geographic location information. For example, the presentation unit provides information related to the user's current location. This allows the presentation unit to customize the display content according to the user's current task, thereby presenting more appropriate information.

[0055] The presentation unit can select a display means according to the user's input method when presenting information. The presentation unit, for example, analyzes the user's input method and selects the optimal display means. For example, if the user prefers voice input, the presentation unit can prioritize voice information presentation. Also, if the user prefers text input, the presentation unit can prioritize text information presentation. Also, if the user uses gesture input, the presentation unit can provide an information presentation means corresponding to the gesture. For example, the presentation unit analyzes the user's gesture, understands the intention, and presents information. This allows the presentation unit to select the optimal display means according to the user's input method, thereby enabling more appropriate information presentation.

[0056] When presenting information, the presentation unit can prioritize displaying highly relevant information by taking into account the user's geographical location information. The presentation unit, for example, analyzes the user's geographical location information and displays highly relevant information. For example, if the user is in a specific area, the presentation unit can prioritize displaying information related to that area. Furthermore, if the user is traveling, the presentation unit can also prioritize displaying information related to the travel destination. Furthermore, if the user is participating in a specific event, the presentation unit can also prioritize displaying information related to the event. For example, the presentation unit provides detailed information about the event the user is participating in. In this way, the presentation unit can prioritize displaying highly relevant information by taking into account the user's geographical location information.

[0057] At the time of presentation, the presentation unit can analyze the user's social media activity and display the information. The presentation unit, for example, analyzes the user's social media activity and displays related information. For example, the presentation unit displays information based on the content the user shared on social media. The presentation unit can also display information with reference to the activity of the user's friends on social media. The presentation unit can also analyze the content posted by the user on social media and display related information. In this way, the presentation unit can display related information by analyzing the user's social media activity.

[0058] The presentation unit can adjust the display method by reflecting the user's past feedback when presenting information. The presentation unit, for example, analyzes the user's past feedback and adjusts the display method. For example, the presentation unit presents information in a similar manner based on a display method that the user has previously preferred. The presentation unit can also adjust the display method based on feedback provided by the user in the past. The presentation unit can also analyze the user's past feedback and suggest an optimal display method. In this way, the presentation unit can customize the display method by reflecting the user's past feedback.

[0059] During learning, the learning unit can adjust the learning algorithm by referring to past learning data. The learning unit, for example, analyzes past learning data and optimizes the learning algorithm. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also analyze past learning data and adjust the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. In this way, the learning unit can optimize the learning algorithm by referring to past learning data.

[0060] During learning, the learning unit can analyze fluctuations in the user's dialogue history and determine the update frequency of the learning data. The learning unit, for example, analyzes fluctuations in the user's dialogue history and adjusts the update frequency of the learning data. For example, the learning unit can increase the update frequency of the learning data if the user's dialogue history changes frequently. The learning unit can also decrease the update frequency of the learning data if the user's dialogue history is stable. The learning unit can also analyze fluctuations in the user's dialogue history and set an optimal update frequency. In this way, the learning unit can adjust the update frequency of the learning data by analyzing fluctuations in the user's dialogue history.

[0061] During learning, the learning unit can revise the learning data by reflecting user feedback. The learning unit, for example, analyzes user feedback and updates the learning data. For example, the learning unit updates the learning data based on feedback provided by the user. The learning unit can also analyze user feedback and adjust the learning data. The learning unit can also improve the accuracy of the learning data by reflecting user feedback. In this way, the learning unit can update the learning data by reflecting user feedback.

[0062] During learning, the learning unit can weight the learning data based on the time when the dialogue history was submitted. The learning unit, for example, analyzes the time when the dialogue history was submitted and weights the learning data. For example, the learning unit weights the learning data based on recent dialogue history. The learning unit can also adjust the weighting of the learning data based on older dialogue history. The learning unit can also analyze the time when the dialogue history was submitted and set optimal weighting. In this way, the learning unit weights the learning data based on the time when the dialogue history was submitted, enabling more appropriate learning.

[0063] During learning, the learning unit can integrate information from different data sources to increase the training data. For example, the learning unit analyzes information from different data sources to expand the training data. For example, the learning unit integrates information from different data sources to increase the training data. The learning unit can also analyze different data sources, select optimal information, and add it to the training data. The learning unit can also integrate information from different data sources to improve the accuracy of the training data. In this way, the learning unit can expand the training data by integrating information from different data sources.

[0064] During learning, the learning unit can revise the learning algorithm by reflecting user feedback. The learning unit, for example, analyzes user feedback and adjusts the learning algorithm. For example, the learning unit adjusts the learning algorithm based on feedback provided by the user. The learning unit can also analyze user feedback and optimize the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm by reflecting user feedback. In this way, the learning unit can adjust the learning algorithm by reflecting user feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The collection unit can also analyze the user's past search history and prioritize collection of highly relevant information. For example, information can be collected based on keywords that the user has frequently searched for in the past. Information can also be collected by referring to the content of websites that the user has visited in the past. Furthermore, information can be collected based on bookmarks that the user has saved in the past. This allows the collection unit to efficiently collect more relevant information based on the user's past behavior.

[0067] The dialogue unit can also analyze the user's past dialogue history and customize the way the dialogue proceeds. For example, the dialogue unit can proceed in a similar style based on the dialogue style that the user has preferred in the past. Also, the dialogue unit can use related phrases based on phrases that the user has frequently used in the past. Furthermore, the dialogue unit can adjust the way the dialogue proceeds based on topics that the user has avoided in the past. This allows the dialogue unit to provide a more personalized dialogue based on the user's past dialogue history.

[0068] The collection unit may also prioritize collection of region-specific information by taking into account the user's current geographic location information. For example, if the user is in a particular city, news and event information related to that city may be collected. Also, if the user is traveling, tourist information and transportation information for the travel destination may be collected. Furthermore, if the user is attending a business meeting in a particular region, business-related information for that region may be collected. This allows the collection unit to provide more relevant information based on the user's current geographic location information.

[0069] The generator can also adjust the content of the prompt according to the user's level of expertise. For example, if the user has specialized knowledge, the generator can generate a prompt that uses a lot of technical terminology. If the user only has general knowledge, the generator can generate a prompt in simple language. Furthermore, if the user is knowledgeable in a particular field, the generator can generate a detailed prompt related to that field. This allows the generator to generate a prompt with appropriate content according to the user's level of expertise.

[0070] The presentation unit can also adjust the display order of information according to the user's current task. For example, it can prioritize displaying information related to a project the user is currently working on. It can also display information based on topics that the user has recently been interested in. Furthermore, if the user is pressed for a specific deadline, it can prioritize displaying information related to that deadline. This allows the presentation unit to display information appropriate to the user's current task.

[0071] The learning unit can also integrate information from different data sources to increase the training data. For example, the learning unit can collect and integrate information from different data sources, such as websites, social media, and academic papers. It can also analyze information from different data sources, select the most appropriate information, and add it to the training data. Furthermore, it can integrate information from different data sources to improve the accuracy of the training data. This allows the learning unit to expand the training data by integrating information from different data sources.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The dialogue unit accepts dialogue from the user. The dialogue unit can accept dialogue in the form of voice dialogue, text dialogue, gesture dialogue, etc., and understands the user's intention using natural language processing technology and intention recognition algorithms. Step 2: The collection unit collects information based on the dialogue received by the dialogue unit. The collection unit collects information by methods such as web scraping and data acquisition from APIs, and uses natural language processing technology and intent recognition algorithms to understand the user's intent and collect the necessary information. Step 3: The generator generates prompts based on the information collected by the collector. The generator generates prompts using methods such as natural language generation technology and template-based generation, and generates appropriate prompts using a generation AI. Step 4: The presentation unit presents the dialogue to the user based on the prompts generated by the generation unit. The presentation unit can present the dialogue by using voice output, text display, visual elements, or the like, and can present information visually using icons. Step 5: The learning unit learns the dialogue history received by the dialogue unit. The learning unit uses a machine learning algorithm to learn the dialogue history and generate more appropriate prompts for subsequent dialogues.

[0074] (Example 2) A generative AI system according to an embodiment of the present invention accepts dialogue from a user, has a generative AI understand the user's intent, collects the necessary information, automatically generates appropriate prompts, and presents them to the user. In this system, the user initiates a dialogue with the generative AI, and the generative AI understands the user's intent and collects the necessary information. The generative AI then automatically generates appropriate prompts based on the collected information and presents them to the user. When the user answers these questions, the generative AI collects more detailed information based on the answers, ultimately generating the necessary information. The generative AI system can also present information visually using icons. For example, in this system, a user initiates a dialogue with the generative AI. The generative AI system understands the user's intent and collects the necessary information. The generative AI system automatically generates appropriate prompts based on the collected information and presents them to the user. When the user answers these questions, the generative AI collects more detailed information based on the answers, ultimately generating the necessary information. The generative AI system can also present information visually using icons. This significantly reduces the barrier to using generative AI, allowing anyone to easily utilize generative AI without relying on prompt engineering skills. This allows the generative AI system to learn the user's dialogue history and generate more appropriate prompts in subsequent dialogues.For example, the generative AI system can learn the user's dialogue history and generate more appropriate prompts in subsequent dialogues.This allows the generative AI system to learn the user's dialogue history and generate more appropriate prompts in subsequent dialogues.

[0075] A generative AI system according to an embodiment includes a dialogue unit, a collection unit, a generation unit, a presentation unit, and a learning unit. The dialogue unit receives a dialogue from a user. The dialogue unit can receive the dialogue in the form of, for example, a voice dialogue, a text dialogue, or a gesture dialogue. The dialogue unit can use natural language processing technology or an intention recognition algorithm to understand the user's intention. The collection unit collects information based on the dialogue received by the dialogue unit. The collection unit can collect information by, for example, web scraping, data acquisition from an API, or the like. The collection unit can use natural language processing technology or an intention recognition algorithm to understand the user's intention and collect the necessary information. The generation unit generates a prompt based on the information collected by the collection unit. The generation unit can generate the prompt by, for example, a natural language generation technology or a template-based generation method. The generation unit can use a generative AI (e.g., a text generation AI or a multimodal generation AI) to generate an appropriate prompt based on the collected information. The presentation unit presents a dialogue to the user based on the prompt generated by the generation unit. The presentation unit can present the dialogue by, for example, audio output, text display, or the use of visual elements. The presentation unit can also visually present information using icons to present the generated prompt to the user. The learning unit learns the dialogue history received by the dialogue unit. The learning unit can learn the dialogue history using, for example, a machine learning algorithm, and generate more appropriate prompts for subsequent dialogues. The learning unit can also use a data preprocessing method or a machine learning algorithm to clarify the dialogue history learning method and criteria. This enables the generative AI system according to the embodiment to efficiently collect, generate, present, and learn user dialogues.

[0076] The collection unit can understand the user's intent and collect information. The collection unit can understand the user's intent using, for example, natural language processing technology. For example, the collection unit can analyze the user's utterances and identify the intent. The collection unit can also understand the user's intent using an intent recognition algorithm. For example, the collection unit can analyze the context of the user's utterances and identify the intent. The collection unit can also perform web scraping to collect the required information. For example, the collection unit can collect information from a specific website. The collection unit can also acquire data from an API. For example, the collection unit can collect information using a specific API. In this way, the collection unit can understand the user's intent and accurately collect the required information.

[0077] The generation unit can generate a prompt based on the collected information. The generation unit generates the prompt using, for example, natural language generation technology. For example, the generation unit generates an appropriate prompt based on the collected information. The generation unit can also generate a prompt using template-based generation. For example, the generation unit generates a prompt using a template based on the collected information. The generation unit can also generate a prompt using a generation AI (for example, a text generation AI or a multimodal generation AI). For example, the generation unit inputs the collected information into the generation AI, which generates a prompt. In this way, the generation unit generates an appropriate prompt based on the collected information, enabling a dialogue that is in line with the user's intentions.

[0078] The presentation unit can present the generated prompt to the user. The presentation unit, for example, presents the generated prompt to the user using audio output. For example, the presentation unit conveys the generated prompt to the user by voice. The presentation unit can also present the generated prompt to the user using a text display. For example, the presentation unit displays the generated prompt to the user in text. The presentation unit can also present the generated prompt to the user using a visual element. For example, the presentation unit presents information visually using an icon. In this way, the presentation unit presents the generated prompt to the user, allowing the user to obtain appropriate information.

[0079] The learning unit can learn the dialogue history and generate prompts for subsequent dialogues. The learning unit learns the dialogue history using, for example, a machine learning algorithm. For example, the learning unit analyzes the user's dialogue history and generates more appropriate prompts for subsequent dialogues. The learning unit can also learn the dialogue history using a data preprocessing method. For example, the learning unit preprocesses dialogue history data and inputs it into a machine learning algorithm. The learning unit can also use a data preprocessing method or a machine learning algorithm to clarify the dialogue history learning method and criteria. In this way, the learning unit can learn the dialogue history and generate more appropriate prompts for subsequent dialogues.

[0080] The presentation unit can present information using icons. The presentation unit, for example, presents information using visual symbols. For example, the presentation unit visually presents information using icons. The presentation unit can also present information using color coding. For example, the presentation unit visually distinguishes information using different colors. The presentation unit can also present information using animation. For example, the presentation unit visually emphasizes information using animation. In this way, the presentation unit visually presents information using icons, making it easier for the user to visually confirm the information.

[0081] The dialogue unit can estimate the user's emotions and adjust the speed of the dialogue based on the estimated user's emotions. The dialogue unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the dialogue unit analyzes the user's facial expressions to identify the emotions. The dialogue unit can also estimate the user's emotions using voice analysis technology. For example, the dialogue unit analyzes the tone and speed of the user's voice to identify the emotions. The dialogue unit can also estimate the user's emotions using text analysis technology. For example, the dialogue unit analyzes the content of the user's speech to identify the emotions. The dialogue unit adjusts the speed of the dialogue based on the estimated user's emotions. For example, if the user is impatient, the dialogue unit can speed up the dialogue to provide information quickly. If the user is relaxed, the dialogue unit can slow down the dialogue to provide a detailed explanation. If the user is feeling anxious, the dialogue unit can adjust the speed of the dialogue while selecting words that provide a sense of reassurance. In this way, the dialogue unit can adjust the speed of the dialogue according to the user's emotions, enabling a more appropriate dialogue.

[0082] The dialogue unit can analyze the user's past dialogue history and select a dialogue initiation method. The dialogue unit, for example, analyzes the user's past dialogue history and selects the optimal dialogue initiation method. For example, the dialogue unit can start a dialogue in a similar style based on a dialogue style that the user has preferred in the past. The dialogue unit can also start a dialogue on a related topic based on keywords that the user has frequently used in the past. The dialogue unit can also predict the dialogue content that the user will prefer at a specific time period from the user's past dialogue history and start a dialogue on an appropriate topic. In this way, the dialogue unit can select the optimal dialogue initiation method by analyzing the user's past dialogue history.

[0083] The dialogue unit can adjust the content of the dialogue based on the user's current situation and areas of interest when starting a dialogue. For example, the dialogue unit analyzes the user's current situation and adjusts the content of the dialogue. For example, the dialogue unit starts a dialogue on a topic related to a project the user is currently working on. The dialogue unit can also analyze the user's areas of interest and adjust the content of the dialogue. For example, the dialogue unit starts a dialogue based on a topic that the user has recently become interested in. The dialogue unit can also start a dialogue on a related topic based on the user's current geographic location information. For example, the dialogue unit provides information related to the user's current location. This allows the dialogue unit to customize the content of the dialogue based on the user's current situation and areas of interest, thereby enabling a more appropriate dialogue.

[0084] The dialogue unit can select a dialogue means according to the user's input method at the start of a dialogue. For example, the dialogue unit analyzes the user's input method and selects the optimal dialogue means. For example, if the user prefers voice input, the dialogue unit can prioritize voice dialogue. Also, if the user prefers text input, the dialogue unit can prioritize text dialogue. Also, if the user uses gesture input, the dialogue unit can provide a dialogue means corresponding to the gesture. For example, the dialogue unit analyzes the user's gestures, understands their intentions, and proceeds with the dialogue. In this way, the dialogue unit can select the optimal dialogue means according to the user's input method, thereby enabling a more appropriate dialogue.

[0085] The dialogue unit can estimate the user's emotions and determine the order of dialogues based on the estimated user's emotions. The dialogue unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the dialogue unit analyzes the user's facial expressions and identifies the emotion. The dialogue unit can also estimate the user's emotions using voice analysis technology. For example, the dialogue unit analyzes the tone and speed of the user's voice and identifies the emotion. The dialogue unit can also estimate the user's emotions using text analysis technology. For example, the dialogue unit analyzes the content of the user's speech and identifies the emotion. The dialogue unit determines the order of dialogues based on the estimated user's emotions. For example, if the user has an urgent problem, the dialogue unit gives top priority to that problem. Also, if the user is relaxed, the dialogue can start with a general topic. Also, if the user is feeling anxious, the dialogue unit can prioritize a dialogue to relieve the anxiety. In this way, the dialogue unit can determine the priority of dialogues according to the user's emotions, enabling more appropriate dialogues.

[0086] The dialogue unit can provide dialogue content preferentially by taking into account the user's geographical location information when starting a dialogue. The dialogue unit, for example, analyzes the user's geographical location information and provides highly relevant dialogue content. For example, if the user is in a specific area, the dialogue unit can provide information related to that area. Furthermore, if the user is traveling, the dialogue unit can provide information related to the travel destination. Furthermore, if the user is participating in a specific event, the dialogue unit can provide information related to the event. For example, the dialogue unit can provide detailed information about the event the user is participating in. In this way, the dialogue unit can provide highly relevant dialogue content by taking into account the user's geographical location information.

[0087] The dialogue unit can analyze the user's social media activity and provide the dialogue content at the start of the dialogue. For example, the dialogue unit can analyze the user's social media activity and provide related dialogue content. For example, the dialogue unit can start the dialogue based on the content the user shared on social media. The dialogue unit can also customize the dialogue content by referring to the activity of the user's friends on social media. The dialogue unit can also analyze the content posted by the user on social media and start a dialogue on a related topic. In this way, the dialogue unit can provide related dialogue content by analyzing the user's social media activity.

[0088] The dialogue unit can adjust the dialogue method by reflecting the user's past feedback when starting a dialogue. The dialogue unit, for example, analyzes the user's past feedback and adjusts the dialogue method. For example, the dialogue unit starts a dialogue in a similar style based on a dialogue style that the user has preferred in the past. The dialogue unit can also adjust the dialogue content based on feedback provided by the user in the past. The dialogue unit can also analyze the user's past feedback and suggest an optimal dialogue method. In this way, the dialogue unit can customize the dialogue method by reflecting the user's past feedback.

[0089] The collection unit can estimate the user's emotions and determine the timing of information collection based on the estimated user's emotions. The collection unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the collection unit analyzes the user's facial expressions and identifies the emotions. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice and identifies the emotions. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit analyzes the content of the user's speech and identifies the emotions. The collection unit determines the timing of information collection based on the estimated user's emotions. For example, if the user is relaxed, the timing of information collection can be slowed down to collect detailed information. If the user is in a hurry, the timing of information collection can be quickened. If the user is feeling anxious, the timing of information collection can be set to provide a sense of security. This allows the collection unit to adjust the timing of information collection according to the user's emotions, thereby enabling more appropriate information collection.

[0090] The collection unit can analyze the user's past dialogue history and select an information collection method. The collection unit, for example, analyzes the user's past dialogue history and selects the optimal information collection method. For example, the collection unit collects information using a similar method based on the information collection method that the user has preferred in the past. The collection unit can also prioritize a specific information collection method from the user's past dialogue history. The collection unit can also analyze the user's past dialogue history and suggest the most efficient information collection method. In this way, the collection unit can select the optimal information collection method by analyzing the user's past dialogue history.

[0091] When collecting information, the collection unit can select information based on the user's current project or areas of interest. For example, the collection unit analyzes the user's current project and collects related information. For example, the collection unit prioritizes collecting information related to the project the user is currently working on. The collection unit can also analyze the user's areas of interest and filter information. For example, the collection unit filters information based on topics that the user has recently become interested in. The collection unit can also collect related information based on the user's current geographic location information. For example, the collection unit provides information related to the user's current location. This allows the collection unit to filter information based on the user's current project or areas of interest, thereby enabling more appropriate information collection.

[0092] When collecting information, the collection unit can select a collection means according to the user's input method. The collection unit, for example, analyzes the user's input method and selects the optimal collection means. For example, if the user prefers voice input, the collection unit can prioritize voice information collection. Also, if the user prefers text input, the collection unit can also prioritize text information collection. Also, if the user uses image input, the collection unit can provide an information collection means corresponding to the image. For example, the collection unit analyzes the user's image and collects information by understanding the user's intention. This allows the collection unit to select the optimal collection means according to the user's input method, enabling more appropriate information collection.

[0093] The collection unit can estimate the user's emotions and determine the order of information to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the collection unit analyzes the user's facial expressions and identifies the emotions. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice and identifies the emotions. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit analyzes the content of the user's speech and identifies the emotions. The collection unit determines the order of information to be collected based on the estimated user's emotions. For example, if the user has an urgent problem, information related to the problem can be collected with the highest priority. Also, if the user is relaxed, general information can be collected with the highest priority. Also, if the user is feeling anxious, information to relieve the anxiety can be collected with the highest priority. In this way, the collection unit can determine the priority of information to be collected according to the user's emotions, enabling more appropriate information collection.

[0094] When collecting information, the collection unit can prioritize collecting information by taking into account the user's geographical location information. The collection unit, for example, analyzes the user's geographical location information and collects highly relevant information. For example, if the user is in a specific area, the collection unit can prioritize collecting information related to that area. Furthermore, if the user is traveling, the collection unit can also prioritize collecting information related to the travel destination. Furthermore, if the user is participating in a specific event, the collection unit can prioritize collecting information related to the event. For example, the collection unit provides detailed information about the event the user is participating in. This allows the collection unit to prioritize collecting highly relevant information by taking into account the user's geographical location information.

[0095] When collecting information, the collection unit can analyze the user's social media activities and collect the information. For example, the collection unit analyzes the user's social media activities and collects related information. For example, the collection unit collects information based on the content the user has shared on social media. The collection unit can also collect information by referring to the activities of the user's friends on social media. The collection unit can also analyze the content posted by the user on social media and collect related information. In this way, the collection unit can collect related information by analyzing the user's social media activities.

[0096] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting information. The collection unit, for example, analyzes the user's past feedback and adjusts the collection method. For example, the collection unit collects information in a similar manner based on the information collection method that the user has previously preferred. The collection unit can also adjust the information collection method based on feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and suggest an optimal information collection method. In this way, the collection unit can customize the collection method by reflecting the user's past feedback.

[0097] The generation unit can estimate the user's emotion and determine a method of expressing the prompt to be generated based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the generation unit analyzes the user's facial expression to identify the emotion. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to identify the emotion. The generation unit can also estimate the user's emotion using text analysis technology. For example, the generation unit analyzes the content of the user's speech to identify the emotion. The generation unit determines a method of expressing the prompt to be generated based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate a prompt using gentle expression. If the user is in a hurry, the generation unit can generate a prompt using concise and clear expression. If the user is anxious, the generation unit can generate a prompt using expression that gives a sense of security. In this way, the generation unit can generate a more appropriate prompt by adjusting the method of expressing the prompt according to the user's emotion.

[0098] When generating a prompt, the generation unit can determine the level of detail of the prompt based on the importance of the collected information. For example, the generation unit analyzes the importance of the collected information and determines the level of detail of the prompt. For example, the generation unit generates a detailed prompt based on information with high importance. The generation unit can also generate a concise prompt based on information with low importance. The generation unit can also analyze the importance of the collected information and generate a prompt with an optimal level of detail. In this way, the generation unit can generate a more appropriate prompt by adjusting the level of detail of the prompt based on the importance of the collected information.

[0099] The generation unit can apply a generation algorithm depending on the category of information when generating a prompt. For example, the generation unit analyzes the category of information and applies an optimal generation algorithm. For example, the generation unit applies a specialized generation algorithm to technical information. The generation unit can also apply a simple generation algorithm to general information. The generation unit can also analyze the category of information and select an optimal generation algorithm. In this way, the generation unit can generate a more appropriate prompt by applying different generation algorithms depending on the category of information.

[0100] When generating a prompt, the generation unit can improve the accuracy of generation by referring to the user's past prompt results. The generation unit, for example, analyzes the user's past prompt results to improve the accuracy of generation. For example, the generation unit generates a similar prompt based on a prompt result that the user previously preferred. The generation unit can also analyze the user's past prompt results to generate an optimal prompt. The generation unit can also adjust the generation algorithm by referring to the user's past prompt results to improve accuracy. In this way, the generation unit can improve the accuracy of generation by referring to the user's past prompt results.

[0101] The generation unit can estimate the user's emotion and determine the length of the prompt to be generated based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the generation unit analyzes the user's facial expression to identify the emotion. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to identify the emotion. The generation unit can also estimate the user's emotion using text analysis technology. For example, the generation unit analyzes the content of the user's speech to identify the emotion. The generation unit determines the length of the prompt to be generated based on the estimated user's emotion. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point prompt. If the user is relaxed, the generation unit can generate a longer prompt with detailed explanations. If the user is anxious, the generation unit can generate a prompt of a length that gives a sense of security. In this way, the generation unit can generate more appropriate prompts by adjusting the length of the prompt according to the user's emotion.

[0102] When generating prompts, the generation unit can determine the order of prompts based on the time of submission of information. The generation unit, for example, analyzes the time of submission of information and determines the order of prompts. For example, the generation unit generates prompts with priority based on urgent information. The generation unit can also set a high priority based on information whose submission deadline is approaching. The generation unit can also analyze the submission time and generate prompts with an optimal priority. In this way, the generation unit can generate more appropriate prompts by determining the priority of prompts based on the time of submission of information.

[0103] When generating prompts, the generation unit can determine the order of prompts based on the relevance of information. The generation unit, for example, analyzes the relevance of information and determines the order of prompts. For example, the generation unit prioritizes the order of prompts based on highly relevant information. The generation unit can also postpone the order of prompts based on less relevant information. The generation unit can also analyze the relevance of information and generate prompts in an optimal order. In this way, the generation unit can generate more appropriate prompts by adjusting the order of prompts based on the relevance of information.

[0104] When generating a prompt, the generation unit can determine the use of technical terminology in the prompt depending on the user's level of expertise. The generation unit, for example, analyzes the user's level of expertise and determines the optimal use of technical terminology. For example, if the user has specialized knowledge, the generation unit generates a prompt that uses a lot of technical terminology. Alternatively, if the user has only general knowledge, the generation unit can generate a prompt using simple language. The generation unit can also analyze the user's level of expertise and adjust the optimal use of technical terminology. In this way, the generation unit can generate a more appropriate prompt by adjusting the use of technical terminology in the prompt depending on the user's level of expertise.

[0105] The presentation unit can estimate the user's emotion and determine a display method for the information to be presented based on the estimated user's emotion. The presentation unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the presentation unit can analyze the user's facial expression and identify the emotion. The presentation unit can also estimate the user's emotion using voice analysis technology. For example, the presentation unit can analyze the tone and speed of the user's voice and identify the emotion. The presentation unit can also estimate the user's emotion using text analysis technology. For example, the presentation unit can analyze the content of the user's speech and identify the emotion. The presentation unit determines a display method for the information to be presented based on the estimated user's emotion. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This allows the presentation unit to adjust the display method for information according to the user's emotion, thereby enabling more appropriate information presentation.

[0106] The presentation unit can select a display method by referring to the user's past operation history when presenting information. The presentation unit, for example, analyzes the user's past operation history and selects the optimal display method. For example, the presentation unit presents information in a similar manner based on a display method that the user has preferred in the past. The presentation unit can also prioritize a specific display method based on the user's past operation history. The presentation unit can also analyze the user's past operation history and suggest the most efficient display method. In this way, the presentation unit can select the optimal display method by referring to the user's past operation history.

[0107] The presentation unit can adjust the display content according to the user's current task when presenting the information. For example, the presentation unit analyzes the user's current task and adjusts the display content. For example, the presentation unit prioritizes displaying information related to a project the user is currently working on. The presentation unit can also customize the display content based on topics that the user has recently been interested in. The presentation unit can also display related information based on the user's current geographic location information. For example, the presentation unit provides information related to the user's current location. This allows the presentation unit to customize the display content according to the user's current task, thereby presenting more appropriate information.

[0108] The presentation unit can select a display means according to the user's input method when presenting information. The presentation unit, for example, analyzes the user's input method and selects the optimal display means. For example, if the user prefers voice input, the presentation unit can prioritize voice information presentation. Also, if the user prefers text input, the presentation unit can prioritize text information presentation. Also, if the user uses gesture input, the presentation unit can provide an information presentation means corresponding to the gesture. For example, the presentation unit analyzes the user's gesture, understands the intention, and presents information. This allows the presentation unit to select the optimal display means according to the user's input method, thereby enabling more appropriate information presentation.

[0109] The presentation unit can estimate the user's emotion and determine the order of information to be presented based on the estimated user's emotion. The presentation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the presentation unit analyzes the user's facial expression and identifies the emotion. The presentation unit can also estimate the user's emotion using voice analysis technology. For example, the presentation unit analyzes the tone and speed of the user's voice and identifies the emotion. The presentation unit can also estimate the user's emotion using text analysis technology. For example, the presentation unit analyzes the content of the user's speech and identifies the emotion. The presentation unit determines the order of information to be presented based on the estimated user's emotion. For example, if the user has an urgent problem, information related to the problem can be presented with the highest priority. If the user is relaxed, general information can be presented with the highest priority. If the user is feeling anxious, information to relieve the anxiety can be presented with the highest priority. In this way, the presentation unit can prioritize information according to the user's emotion, thereby enabling more appropriate information presentation.

[0110] When presenting information, the presentation unit can prioritize displaying highly relevant information by taking into account the user's geographical location information. The presentation unit, for example, analyzes the user's geographical location information and displays highly relevant information. For example, if the user is in a specific area, the presentation unit can prioritize displaying information related to that area. Furthermore, if the user is traveling, the presentation unit can also prioritize displaying information related to the travel destination. Furthermore, if the user is participating in a specific event, the presentation unit can also prioritize displaying information related to the event. For example, the presentation unit provides detailed information about the event the user is participating in. In this way, the presentation unit can prioritize displaying highly relevant information by taking into account the user's geographical location information.

[0111] At the time of presentation, the presentation unit can analyze the user's social media activity and display the information. The presentation unit, for example, analyzes the user's social media activity and displays related information. For example, the presentation unit displays information based on the content the user shared on social media. The presentation unit can also display information with reference to the activity of the user's friends on social media. The presentation unit can also analyze the content posted by the user on social media and display related information. In this way, the presentation unit can display related information by analyzing the user's social media activity.

[0112] The presentation unit can adjust the display method by reflecting the user's past feedback when presenting information. The presentation unit, for example, analyzes the user's past feedback and adjusts the display method. For example, the presentation unit presents information in a similar manner based on a display method that the user has previously preferred. The presentation unit can also adjust the display method based on feedback provided by the user in the past. The presentation unit can also analyze the user's past feedback and suggest an optimal display method. In this way, the presentation unit can customize the display method by reflecting the user's past feedback.

[0113] The learning unit can estimate the user's emotions and select training data based on the estimated user's emotions. The learning unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the learning unit analyzes the user's facial expressions and identifies the emotions. The learning unit can also estimate the user's emotions using voice analysis technology. For example, the learning unit analyzes the tone and speed of the user's voice and identifies the emotions. The learning unit can also estimate the user's emotions using text analysis technology. For example, the learning unit analyzes the content of the user's speech and identifies the emotions. The learning unit selects training data based on the estimated user's emotions. For example, if the user is relaxed, detailed training data can be selected. If the user is in a hurry, concise training data can be selected. If the user is feeling anxious, training data that gives a sense of security can be selected. In this way, the learning unit can select training data according to the user's emotions, enabling more appropriate learning.

[0114] During learning, the learning unit can adjust the learning algorithm by referring to past learning data. The learning unit, for example, analyzes past learning data and optimizes the learning algorithm. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also analyze past learning data and adjust the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. In this way, the learning unit can optimize the learning algorithm by referring to past learning data.

[0115] During learning, the learning unit can analyze fluctuations in the user's dialogue history and determine the update frequency of the learning data. The learning unit, for example, analyzes fluctuations in the user's dialogue history and adjusts the update frequency of the learning data. For example, the learning unit can increase the update frequency of the learning data if the user's dialogue history changes frequently. The learning unit can also decrease the update frequency of the learning data if the user's dialogue history is stable. The learning unit can also analyze fluctuations in the user's dialogue history and set an optimal update frequency. In this way, the learning unit can adjust the update frequency of the learning data by analyzing fluctuations in the user's dialogue history.

[0116] During learning, the learning unit can revise the learning data by reflecting user feedback. The learning unit, for example, analyzes user feedback and updates the learning data. For example, the learning unit updates the learning data based on feedback provided by the user. The learning unit can also analyze user feedback and adjust the learning data. The learning unit can also improve the accuracy of the learning data by reflecting user feedback. In this way, the learning unit can update the learning data by reflecting user feedback.

[0117] The learning unit can estimate the user's emotions and determine the frequency of learning based on the estimated user's emotions. The learning unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the learning unit analyzes the user's facial expressions and identifies the emotions. The learning unit can also estimate the user's emotions using voice analysis technology. For example, the learning unit analyzes the tone and speed of the user's voice and identifies the emotions. The learning unit can also estimate the user's emotions using text analysis technology. For example, the learning unit analyzes the content of the user's speech and identifies the emotions. The learning unit determines the frequency of learning based on the estimated user's emotions. For example, if the user is relaxed, the learning frequency can be increased. If the user is in a hurry, the learning frequency can be decreased. If the user is feeling anxious, the learning can be performed at a frequency that gives the user a sense of security. In this way, the learning unit can adjust the learning frequency according to the user's emotions, enabling more appropriate learning.

[0118] During learning, the learning unit can weight the learning data based on the time when the dialogue history was submitted. The learning unit, for example, analyzes the time when the dialogue history was submitted and weights the learning data. For example, the learning unit weights the learning data based on recent dialogue history. The learning unit can also adjust the weighting of the learning data based on older dialogue history. The learning unit can also analyze the time when the dialogue history was submitted and set optimal weighting. In this way, the learning unit weights the learning data based on the time when the dialogue history was submitted, enabling more appropriate learning.

[0119] During learning, the learning unit can integrate information from different data sources to increase the training data. For example, the learning unit analyzes information from different data sources to expand the training data. For example, the learning unit integrates information from different data sources to increase the training data. The learning unit can also analyze different data sources, select optimal information, and add it to the training data. The learning unit can also integrate information from different data sources to improve the accuracy of the training data. In this way, the learning unit can expand the training data by integrating information from different data sources.

[0120] During learning, the learning unit can revise the learning algorithm by reflecting user feedback. The learning unit, for example, analyzes user feedback and adjusts the learning algorithm. For example, the learning unit adjusts the learning algorithm based on feedback provided by the user. The learning unit can also analyze user feedback and optimize the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm by reflecting user feedback. In this way, the learning unit can adjust the learning algorithm by reflecting user feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned dialogue unit, collection unit, generation unit, presentation unit, and learning unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the presentation unit is realized by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned interaction unit, collection unit, generation unit, presentation unit, and learning unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the interaction unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the presentation unit is realized by the output device 40 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned dialogue unit, collection unit, generation unit, presentation unit, and learning unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the presentation unit is realized by the output device 40 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned dialogue unit, collection unit, generation unit, presentation unit, and learning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the presentation unit is realized by the output device 40 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The dialogue unit can also estimate the user's emotions and adjust the tone of the dialogue based on the estimated emotions. For example, if the user is stressed, the dialogue unit can use a calm and reassuring tone. If the user is excited, the dialogue unit can use an energetic and lively tone. Furthermore, if the user is sad, the dialogue unit can use a gentle and comforting tone. This allows the dialogue unit to proceed with an appropriate tone according to the user's emotions.

[0123] The collection unit can also analyze the user's past search history and prioritize collection of highly relevant information. For example, information can be collected based on keywords that the user has frequently searched for in the past. Information can also be collected by referring to the content of websites that the user has visited in the past. Furthermore, information can be collected based on bookmarks that the user has saved in the past. This allows the collection unit to efficiently collect more relevant information based on the user's past behavior.

[0124] The generation unit can also estimate the user's emotions and adjust the content of the prompt based on the estimated emotions. For example, if the user is feeling anxious, the generation unit can generate a prompt with content that gives a sense of security. If the user is excited, the generation unit can generate a prompt with energetic and positive content. Furthermore, if the user is sad, the generation unit can generate a prompt with content that comforts. In this way, the generation unit can generate a prompt with appropriate content according to the user's emotions.

[0125] The presentation unit can also estimate the user's emotions and adjust the display format of information based on the estimated emotions. For example, if the user is relaxed, the presentation unit can use a display format that includes detailed information. If the user is in a hurry, the presentation unit can use a concise and to-the-point display format. Furthermore, if the user is feeling anxious, the presentation unit can use a display format that gives a sense of security. In this way, the presentation unit can present information in an appropriate display format according to the user's emotions.

[0126] The learning unit can also estimate the user's emotions and adjust the criteria for selecting learning data based on the estimated emotions. For example, if the user is relaxed, the learning unit selects detailed data. If the user is in a hurry, the learning unit can select concise data. Furthermore, if the user is feeling anxious, the learning unit can select data that gives a sense of security. This allows the learning unit to select appropriate learning data according to the user's emotions.

[0127] The dialogue unit can also analyze the user's past dialogue history and customize the way the dialogue proceeds. For example, the dialogue unit can proceed in a similar style based on the dialogue style that the user has preferred in the past. Also, the dialogue unit can use related phrases based on phrases that the user has frequently used in the past. Furthermore, the dialogue unit can adjust the way the dialogue proceeds based on topics that the user has avoided in the past. This allows the dialogue unit to provide a more personalized dialogue based on the user's past dialogue history.

[0128] The collection unit may also prioritize collection of region-specific information by taking into account the user's current geographic location information. For example, if the user is in a particular city, news and event information related to that city may be collected. Also, if the user is traveling, tourist information and transportation information for the travel destination may be collected. Furthermore, if the user is attending a business meeting in a particular region, business-related information for that region may be collected. This allows the collection unit to provide more relevant information based on the user's current geographic location information.

[0129] The generator can also adjust the content of the prompt according to the user's level of expertise. For example, if the user has specialized knowledge, the generator can generate a prompt that uses a lot of technical terminology. If the user only has general knowledge, the generator can generate a prompt in simple language. Furthermore, if the user is knowledgeable in a particular field, the generator can generate a detailed prompt related to that field. This allows the generator to generate a prompt with appropriate content according to the user's level of expertise.

[0130] The presentation unit can also adjust the display order of information according to the user's current task. For example, it can prioritize displaying information related to a project the user is currently working on. It can also display information based on topics that the user has recently been interested in. Furthermore, if the user is pressed for a specific deadline, it can prioritize displaying information related to that deadline. This allows the presentation unit to display information appropriate to the user's current task.

[0131] The learning unit can also integrate information from different data sources to increase the training data. For example, the learning unit can collect and integrate information from different data sources, such as websites, social media, and academic papers. It can also analyze information from different data sources, select the most appropriate information, and add it to the training data. Furthermore, it can integrate information from different data sources to improve the accuracy of the training data. This allows the learning unit to expand the training data by integrating information from different data sources.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The dialogue unit accepts dialogue from the user. The dialogue unit can accept dialogue in the form of voice dialogue, text dialogue, gesture dialogue, etc., and understands the user's intention using natural language processing technology and intention recognition algorithms. Step 2: The collection unit collects information based on the dialogue received by the dialogue unit. The collection unit collects information by methods such as web scraping and data acquisition from APIs, and uses natural language processing technology and intent recognition algorithms to understand the user's intent and collect the necessary information. Step 3: The generator generates prompts based on the information collected by the collector. The generator generates prompts using methods such as natural language generation technology and template-based generation, and generates appropriate prompts using a generation AI. Step 4: The presentation unit presents the dialogue to the user based on the prompts generated by the generation unit. The presentation unit can present the dialogue by using voice output, text display, visual elements, or the like, and can present information visually using icons. Step 5: The learning unit learns the dialogue history received by the dialogue unit. The learning unit uses a machine learning algorithm to learn the dialogue history and generate more appropriate prompts for subsequent dialogues.

[0134] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0137] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 7, a 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.

[0172] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0178] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0180] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0183] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0184] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0186] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0187] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0189] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0190] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0191] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0195] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0196] 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.

[0197] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0198] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0199] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0200] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0202] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0203] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0204] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0205] [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a dialogue unit that accepts dialogue from a user; a collection unit that collects information based on the dialogue received by the dialogue unit; a generator that generates a prompt based on the information collected by the collector; a presentation unit that presents a dialogue to a user based on the prompt generated by the generation unit; a learning unit that learns the dialogue history received by the dialogue unit. A system characterized by:

2. The collecting unit Understand user intent and gather information 2. The system of claim 1.

3. The generation unit Generate prompts based on collected information 2. The system of claim 1.

4. The presentation unit Presents the generated prompt to the user 2. The system of claim 1.

5. The learning unit Learns dialogue history and generates prompts for subsequent dialogues 2. The system of claim 1.

6. The presentation unit Present information using icons 2. The system of claim 1.

7. The dialogue unit Estimate the user's emotions and adjust the speed of the dialogue based on the estimated user emotions.

2. The system of claim 1.

8. The dialogue unit Analyze the user's past interaction history and select the method for starting the interaction 2. The system of claim 1.

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