system

Abstract

The system according to this embodiment aims to analyze user statements and visually convey important information. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives user statements. The analysis unit analyzes the statements received by the reception unit and extracts important information. The generation unit generates an infographic based on the information extracted by the analysis unit. The display unit displays the infographic generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in voice conversations, there is a risk that users may feel burdened when listening to long speeches.

[0005] The system according to the embodiment analyzes the user's speech and aims to visually convey important information.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives the user's speech. The analysis unit analyzes the speech received by the reception unit and extracts important information. The generation unit generates an infographic based on the information extracted by the analysis unit. The display unit displays the infographic generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze user statements and visually convey important information. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The voice dialogue AI system according to an embodiment of the present invention is a system that uses generative AI to visually convey the key points of voice dialogue. In this system, the generative AI engages in dialogue with the user, extracts the key points to avoid lengthy conversations, and displays them visually as an infographic. This allows the user to confirm the necessary information concisely and intuitively, realizing a smooth dialogue experience. Furthermore, it can improve the efficiency of information comprehension and maximize the value of the dialogue. For example, when the generative AI engages in dialogue with the user, it analyzes the user's statements and extracts important information. For example, if the user asks, "What's the weather like today?", the generative AI collects information about the weather and extracts the key points. Next, it visually displays the extracted key points as an infographic. The generative AI generates an infographic based on the extracted information and provides it to the user. For example, by visually displaying information about the weather, the user can grasp the weather situation at a glance. Through this mechanism, the user can confirm the necessary information concisely and intuitively. By the generative AI avoiding lengthy conversations and visually conveying the key points, the burden on the user is reduced, realizing a smooth dialogue experience. Furthermore, it can improve the efficiency of information comprehension and maximize the value of the dialogue. For example, if a user asks, "What are my plans for tomorrow?", the generative AI gathers information about the plans and extracts the key points. Next, it visually displays these extracted points as an infographic. This allows the user to grasp tomorrow's plans at a glance. Furthermore, the generative AI analyzes the user's statements and extracts important information, maximizing the value of the conversation. For example, if a user asks, "What's the latest news?", the generative AI gathers information about the news and extracts the key points. Next, it visually displays these extracted points as an infographic. This allows the user to grasp the latest news at a glance. In this way, by using generative AI to visually convey the key points of voice dialogue, users can confirm the necessary information concisely and intuitively, resulting in a smooth dialogue experience. It also improves the efficiency of information comprehension and maximizes the value of the dialogue. Thus, voice dialogue AI systems can efficiently analyze user statements and visually convey important information.

[0029] The voice dialogue AI system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives user utterances. User utterances include, but are not limited to, voice, text, and gestures. The reception unit receives user utterances using, for example, speech recognition technology. The reception unit can also receive text input. Furthermore, the reception unit can also receive user gestures using gesture recognition technology. For example, the reception unit converts user utterances into text data using speech recognition technology. Text input can be performed using a keyboard or touchscreen. Gesture recognition technology detects user movements using a camera or sensors and recognizes them as utterances. The analysis unit analyzes the utterances received by the reception unit and extracts important information. Important information is extracted based on, for example, keyword frequency and contextual importance, but is not limited to such examples. For example, the analysis unit analyzes user utterances using natural language processing technology and extracts important information. The analysis unit can also evaluate the importance of utterances using machine learning algorithms. Furthermore, the analysis unit can understand the meaning of statements using contextual analysis techniques and extract important information. For example, the analysis unit can extract keywords from user statements using natural language processing techniques and identify important information based on their frequency. Machine learning algorithms learn from large amounts of data and automatically evaluate the importance of statements. Contextual analysis techniques understand the meaning by considering the context of statements and extract important information. The generation unit generates infographics based on the information extracted by the analysis unit. Infographics are generated in the form of graphs, charts, diagrams, etc., but are not limited to these examples. For example, the generation unit generates infographics using text generation AI (e.g., LLM). The generation unit can also generate infographics that combine text and images using multimodal generation AI. Furthermore, the generation unit can visually represent the extracted information using data visualization techniques. For example, the generation unit uses text generation AI to summarize the extracted information into concise sentences and generate infographics based on those sentences.The multimodal generation AI combines text and images to generate visually easy-to-understand infographics. Data visualization technology visually represents the extracted information as graphs and charts. The display unit displays the infographics generated by the generation unit. The display unit displays the infographics using devices such as displays, projectors, and smartphones, but is not limited to these examples. For example, the display unit displays the infographics using a display. The display unit can also project the infographics onto a large screen using a projector. Furthermore, the display unit can display the infographics using a smartphone or tablet. For example, the display unit displays the infographics in high resolution using a display. A projector projects the infographics onto a large screen, enabling sharing among multiple people. Smartphones and tablets are easily portable, allowing users to view the infographics anytime, anywhere. As a result, the voice dialogue AI system according to the embodiment can efficiently analyze user statements and visually convey important information.

[0030] The reception unit receives user input. User input includes, but is not limited to, voice, text, and gestures. The reception unit can receive user input using, for example, speech recognition technology. Specifically, speech recognition technology converts user voice into text data in real time. Speech recognition technology includes a process of extracting voice features and breaking them down into phonemes and words. This allows for accurate transcription of user input into text. The reception unit can also accept text input. Text input can be done using a keyboard or touchscreen, and the system accepts input by the user directly typing characters. Furthermore, the reception unit can accept user gestures using gesture recognition technology. Gesture recognition technology detects user movements using cameras and sensors and recognizes specific actions as input. For example, it can analyze hand movements and facial expressions to understand the user's intent. This allows the reception unit to support diverse input methods such as voice, text, and gestures, and flexibly accept user input. In addition, the reception unit plays a role in centrally managing this input data and transmitting it to the analysis unit. This allows the reception desk to efficiently receive user messages and ensure smooth processing of the entire system.

[0031] The analysis unit analyzes the utterances received by the reception unit and extracts important information. Important information is extracted based on, for example, keyword frequency or contextual importance, but is not limited to these examples. Specifically, the analysis unit uses natural language processing (NLP) techniques to analyze user utterances and extract important information. NLP techniques include morphological analysis, syntactic analysis, and semantic analysis, which are combined to understand the content of the utterances. For example, morphological analysis is used to break down the utterance into individual words, and syntactic analysis is used to analyze the sentence structure. Furthermore, semantic analysis is used to understand the context and identify important information. The analysis unit can also use machine learning algorithms to evaluate the importance of utterances. Machine learning algorithms learn from large amounts of data and automatically identify patterns and features of utterances. This allows the analysis unit to quickly and accurately extract important information from user utterances. Furthermore, the analysis unit can also use contextual analysis techniques to understand the meaning of utterances and extract important information. Contextual analysis techniques consider the context of utterances to understand meaning and identify important information. For example, if a user makes consecutive utterances, the analysis relates the preceding and succeeding utterances to understand the overall context. This allows the analysis unit to gain a deeper understanding of the user's statements and accurately extract important information.

[0032] The generation unit generates infographics based on the information extracted by the analysis unit. Infographics are generated in various forms, such as graphs, charts, and diagrams, but are not limited to these examples. Specifically, the generation unit uses text generation AI (e.g., LLM) to generate infographics. The text generation AI generates natural-sounding text based on the extracted information and visually represents it. For example, it summarizes keywords and important information extracted from user statements into concise sentences and creates an infographic based on them. The generation unit can also use multimodal generation AI to generate infographics that combine text and images. The multimodal generation AI integrates text and image data to create visually easy-to-understand infographics. For example, it displays user statements as text and combines them with related images and icons for visual representation. Furthermore, the generation unit can use data visualization technology to visually represent the extracted information. Data visualization technology visually represents the extracted information as graphs and charts, making it easy for users to understand. For example, it displays user statements as bar graphs or pie charts, visually indicating the importance and frequency of the information. This allows the generation unit to effectively visualize the extracted information and communicate it clearly to the user.

[0033] The display unit displays the infographic generated by the generation unit. The display unit displays the infographic using devices such as displays, projectors, and smartphones, but is not limited to these examples. Specifically, the display unit displays the infographic in high resolution using a display. Displays include desktop monitors and large screens, which can clearly display detailed information. The display unit can also project the infographic onto a large screen using a projector. Projectors are used in conference rooms and classrooms, enabling information sharing among multiple people. Furthermore, the display unit can display the infographic using smartphones and tablets. Smartphones and tablets are easily portable, allowing users to view the infographic anytime, anywhere. For example, the infographic can be displayed on a smartphone or tablet so that users can view it while on the go. This allows the display unit to display generated infographics on a variety of devices, effectively conveying information to users. Furthermore, the display unit has interactive functions, allowing users to manipulate the infographic. For example, users can zoom in and out of the infographic or display detailed information using a touchscreen. This allows the display unit to provide users with flexible information display and facilitate understanding of the information.

[0034] The analysis unit can analyze user utterances and extract important information. For example, the analysis unit can use natural language processing techniques to analyze user utterances and extract important information. For example, the analysis unit can identify important information based on keyword frequency and contextual importance. The analysis unit can also use machine learning algorithms to evaluate the importance of utterances. For example, the analysis unit can learn from large amounts of data and automatically evaluate the importance of utterances. Furthermore, the analysis unit can use contextual analysis techniques to understand the meaning of utterances and extract important information. For example, the analysis unit can understand the meaning by considering the context of the utterance and extract important information. This enables efficient information transmission by extracting important information from user utterances. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user utterances into a generating AI and have the generating AI perform the extraction of important information.

[0035] The generation unit can generate infographics based on the extracted information. The generation unit can generate infographics using, for example, a text generation AI (e.g., LLM). For example, the generation unit can summarize the extracted information into concise sentences and generate an infographic based on them. The generation unit can also generate infographics that combine text and images using a multimodal generation AI. For example, the generation unit can combine text and images to generate a visually easy-to-understand infographic. Furthermore, the generation unit can visually represent the extracted information using data visualization technology. For example, the generation unit can visually represent the extracted information as graphs or charts. This helps users understand the extracted information by displaying it visually. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the extracted information into a generation AI and have the generation AI perform the generation of infographics.

[0036] The display unit can provide the generated infographic to the user. The display unit can display the infographic using a display, for example. For example, the display unit can display the infographic in high resolution. The display unit can also project the infographic onto a large screen using a projector. For example, the display unit can project the infographic onto a large screen using a projector, enabling sharing among multiple people. Furthermore, the display unit can display the infographic using a smartphone or tablet. For example, the display unit can be easily carried out using a smartphone or tablet, allowing users to view the infographic anytime, anywhere. This reduces the burden on the user by providing information visually. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the generated infographic into a generating AI and have the generating AI optimize the display method.

[0037] The reception unit can analyze the user's past utterance history and select the optimal reception method. For example, the reception unit can prioritize receiving phrases that the user has frequently used in the past. For example, by prioritizing phrases that the user has frequently used in the past, the reception unit can quickly understand the user's intent. The reception unit can also analyze the user's past utterance patterns and suggest the optimal reception method. For example, by analyzing the user's past utterance patterns and suggesting the optimal reception method, the reception unit can accurately grasp the user's intent. Furthermore, the reception unit can prioritize receiving utterances related to specific topics from the user's past utterance history. For example, by prioritizing receiving utterances related to specific topics from the user's past utterance history, the reception unit can provide highly relevant information. In this way, by analyzing the past utterance history, the reception unit can provide the user with the optimal reception method. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past utterance history into a generating AI and have the generating AI select the optimal reception method.

[0038] The reception unit can filter messages based on the user's current situation and areas of interest when receiving them. For example, the reception unit can prioritize receiving messages related to the user's current situation. For example, by prioritizing messages related to the user's current situation, the reception unit can provide highly relevant information. The reception unit can also filter and receive relevant messages based on the user's areas of interest. For example, by filtering and receiving relevant messages based on the user's areas of interest, the reception unit can provide information tailored to the user's interests. Furthermore, the reception unit can also filter out unnecessary messages based on the user's current situation and areas of interest. For example, by filtering out unnecessary messages based on the user's current situation and areas of interest, the reception unit can reduce the user's burden. This allows for the provision of highly relevant information by filtering messages based on the user's situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering of messages.

[0039] The reception system can prioritize receiving messages that are highly relevant based on the user's geographical location. For example, if the user is in a specific location, the reception system will prioritize receiving messages related to that location. For example, by prioritizing messages related to a specific location when the user is in a specific location, the reception system can provide appropriate information. The reception system can also filter and receive messages that are highly relevant based on the user's geographical location. For example, by filtering and receiving messages that are highly relevant based on the user's geographical location, the reception system can provide information that meets the user's needs. Furthermore, if the user is on the move, the reception system can prioritize receiving messages related to their current location. For example, by prioritizing messages related to the user's current location when the user is on the move, the reception system can provide real-time information. In this way, by prioritizing the reception of highly relevant messages based on geographical location, the system can provide users with appropriate information. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into the generating AI and have the AI ​​perform filtering of the user's statements.

[0040] The reception unit can analyze the user's social media activity when receiving a message and accept relevant messages. For example, the reception unit can prioritize receiving messages related to topics of interest from the user's social media activity. For example, by prioritizing the reception unit to receive messages related to topics of interest from the user's social media activity, it can provide information tailored to the user's interests. The reception unit can also analyze the user's social media activity and filter the messages to accept relevant ones. For example, by analyzing the user's social media activity and filtering the messages to accept relevant ones, it can eliminate unnecessary information. Furthermore, the reception unit can filter out unnecessary messages based on the user's social media activity. For example, by filtering out unnecessary messages based on the user's social media activity, the reception unit can reduce the burden on the user. In this way, by analyzing social media activity, it is possible to accept messages based on the user's interests. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the filtering of messages.

[0041] The analysis unit can adjust the level of detail extracted based on the importance of the statements during analysis. For example, the analysis unit extracts detailed information for important statements. For example, by extracting detailed information for important statements, the analysis unit provides the user with the information they need. The analysis unit can also extract concise information for less important statements. For example, by extracting concise information for less important statements, the analysis unit reduces the burden on the user. Furthermore, the analysis unit can adjust the level of detail of the extracted information according to the importance of the statements. For example, by adjusting the level of detail of the extracted information according to the importance of the statements, the analysis unit can provide appropriate information. In this way, by adjusting the level of detail of the extraction according to the importance of the statements, appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input statement importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the extraction.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the statement during analysis. For example, the analysis unit can apply a news-specific analysis algorithm to statements related to news. For example, by applying a news-specific analysis algorithm to statements related to news, the analysis unit can extract information with high accuracy. The analysis unit can also apply a weather-specific analysis algorithm to statements related to weather. For example, by applying a weather-specific analysis algorithm to statements related to weather, the analysis unit can provide accurate information. Furthermore, the analysis unit can also apply a schedule-specific analysis algorithm to statements related to schedules. For example, by applying a schedule-specific analysis algorithm to statements related to schedules, the analysis unit can support the user's schedule management. This makes it possible to extract information with high accuracy by applying an analysis algorithm according to the category of the statement. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input statement category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0043] The analysis unit can determine the extraction priority based on the submission date of the statements during analysis. For example, the analysis unit may prioritize the extraction of the most recent statements. For example, by prioritizing the extraction of the most recent statements, the analysis unit can provide users with the latest information. The analysis unit can also determine the extraction priority for older statements according to their importance. For example, by determining the extraction priority for older statements according to their importance, the analysis unit can provide appropriate information. Furthermore, the analysis unit can adjust the priority of the information to be extracted based on the submission date of the statements. For example, by adjusting the priority of the information to be extracted based on the submission date of the statements, the analysis unit can provide users with the information they need. This ensures that the latest information is provided preferentially by determining the extraction priority based on the submission date of the statements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input statement submission date data into a generating AI and have the generating AI determine the extraction priority.

[0044] The analysis unit can adjust the extraction order based on the relevance of the statements during analysis. For example, the analysis unit can prioritize the extraction of highly relevant statements. For example, by prioritizing the extraction of highly relevant statements, the analysis unit can provide the user with highly relevant information. The analysis unit can also postpone the extraction of less relevant statements. For example, by postponing the extraction of less relevant statements, the analysis unit can prioritize important information. Furthermore, the analysis unit can adjust the order of information to be extracted based on the relevance of the statements. For example, by adjusting the order of information to be extracted based on the relevance of the statements, the analysis unit can provide the user with the information they need. In this way, by adjusting the extraction order based on the relevance of the statements, highly relevant information can be provided preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input statement relevance data into a generating AI and have the generating AI perform the adjustment of the extraction order.

[0045] The generation unit can adjust the level of detail of the infographic generated based on the importance of the information. For example, the generation unit can generate a detailed infographic for important information. For example, by generating a detailed infographic for important information, the generation unit can provide the user with the information they need. The generation unit can also generate a concise infographic for less important information. For example, by generating a concise infographic for less important information, the generation unit can reduce the burden on the user. Furthermore, the generation unit can adjust the level of detail of the infographic it generates according to the importance of the information. For example, by adjusting the level of detail of the infographic it generates according to the importance of the information, the generation unit can provide appropriate information. This makes it possible to provide appropriate information by adjusting the level of detail of the infographic according to the importance of the information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input information importance data into the generation AI and have the generation AI perform the adjustment of the level of detail of the infographic.

[0046] The generation unit can apply different generation algorithms depending on the category of information when generating infographics. For example, the generation unit can apply a news-specific generation algorithm to information about news. For example, by applying a news-specific generation algorithm to information about news, the generation unit can generate highly accurate infographics. The generation unit can also apply a weather-specific generation algorithm to information about weather. For example, by applying a weather-specific generation algorithm to information about weather, the generation unit can provide accurate information. Furthermore, the generation unit can also apply a schedule-specific generation algorithm to information about appointments. For example, by applying a schedule-specific generation algorithm to information about appointments, the generation unit can support the user's schedule management. This makes it possible to generate highly accurate infographics by applying a generation algorithm according to the category of information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input information category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0047] The generation unit can determine the generation priority based on the information submission timing when generating infographics. For example, the generation unit can prioritize generating the latest information as infographics. For example, by prioritizing the generation of the latest information as infographics, the generation unit can provide users with the most up-to-date information. The generation unit can also determine the generation priority for older information according to its importance. For example, by determining the generation priority for older information according to its importance, the generation unit can provide appropriate information. Furthermore, the generation unit can adjust the priority of the infographics to be generated based on the information submission timing. For example, by adjusting the priority of the infographics to be generated based on the information submission timing, the generation unit can provide users with the information they need. This ensures that the latest information is provided preferentially by determining the generation priority based on the information submission timing. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input information submission timing data into the generation AI and have the generation AI determine the generation priority.

[0048] The generation unit can adjust the generation order based on the relevance of the information when generating infographics. For example, the generation unit can prioritize generating highly relevant information as infographics. For example, by prioritizing the generation of highly relevant information as infographics, the generation unit can provide users with highly relevant information. The generation unit can also postpone the generation of less relevant information. For example, by postponing the generation of less relevant information, the generation unit can prioritize important information. Furthermore, the generation unit can adjust the order of the infographics to be generated based on the relevance of the information. For example, by adjusting the order of the infographics to be generated based on the relevance of the information, the generation unit can provide users with the information they need. In this way, by adjusting the generation order based on the relevance of the information, highly relevant information can be provided preferentially. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0049] The display unit can select the optimal display method by referring to the user's past operation history when displaying infographics. For example, the display unit can prioritize providing display methods that the user has previously preferred. For example, by prioritizing display methods that the user has previously preferred, the display unit can provide information tailored to the user's preferences. The display unit can also suggest the optimal display method based on the user's past operation history. For example, by suggesting the optimal display method based on the user's past operation history, the display unit can improve user visibility. Furthermore, the display unit can analyze the user's past operation history and select a display method with high visibility. For example, by analyzing the user's past operation history and selecting a display method with high visibility, the display unit can reduce the user's burden. This allows the display unit to provide the user with the optimal display method by referring to past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI select the display method.

[0050] The display unit can select the optimal display method when displaying infographics, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the user is using a smartphone, the display unit can improve visibility by providing a display method that matches the screen size. The display unit can also provide a display method optimized for larger screens if the user is using a tablet. For example, if the user is using a tablet, the display unit can convey information clearly by providing a display method optimized for larger screens. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. For example, if the user is using a smartwatch, the display unit can reduce the burden on the user by providing a concise and highly visible display method. This allows the display unit to provide the user with appropriate information by providing the optimal display method based on device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the display method.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The analysis unit can extract important information by referring to the user's past utterance history when analyzing user utterances. For example, by prioritizing the extraction of keywords frequently used by the user in the past, the user's intent can be quickly understood. It can also analyze the user's past utterance patterns to identify important information. Furthermore, it can prioritize the extraction of information related to specific topics from the user's past utterance history. In this way, by utilizing past utterance history, the system can provide users with the most relevant information.

[0053] The generation unit can customize the information generated in the infographic based on the user's current situation and areas of interest. For example, it can provide highly relevant information by prioritizing the display of information related to the user's current situation. It can also incorporate relevant information into the infographic based on the user's areas of interest. Furthermore, it can exclude unnecessary information based on the user's current situation and areas of interest. This reduces the burden on the user by providing information tailored to their situation and areas of interest.

[0054] The display unit can select the optimal display method when displaying generated infographics, taking into account the user's geographical location. For example, if the user is in a specific location, information related to that location can be prioritized, providing appropriate information. It can also filter and display highly relevant information based on the user's geographical location. Furthermore, if the user is on the move, information related to their current location can be prioritized. This allows for the provision of relevant information based on geographical location, thereby providing users with appropriate information.

[0055] The analysis unit can adjust the level of detail extracted based on the importance of the user's statements when analyzing them. For example, for important statements, detailed information can be extracted to provide the user with the information they need. Conversely, for less important statements, concise information can be extracted to reduce the user's burden. Furthermore, the level of detail of the extracted information can be adjusted according to the importance of the statement. This allows for the provision of appropriate information by adjusting the level of detail of the extraction according to the importance of the statement.

[0056] The generation unit can apply different generation algorithms depending on the category of information when generating infographics. For example, by applying a generation algorithm specifically for news information, highly accurate infographics can be generated. Similarly, by applying a generation algorithm specifically for weather information, accurate information can be provided. Furthermore, by applying a generation algorithm specifically for schedule information, it is possible to support users' schedule management. In this way, applying a generation algorithm appropriate to the category of information enables the generation of highly accurate infographics.

[0057] The display unit can select the optimal display method when displaying infographics, taking into account the user's device information. For example, if the user is using a smartphone, visibility can be improved by providing a display method that matches the screen size. If the user is using a tablet, information can be conveyed more clearly by providing a display method optimized for the larger screen. Furthermore, if the user is using a smartwatch, the burden on the user can be reduced by providing a concise and highly visible display method. In this way, by providing the optimal display method based on device information, the system can provide users with appropriate information.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives the user's statements. User statements can include voice, text, and gestures. The reception desk can use voice recognition technology to convert the user's statements into text data. Text input can be done using a keyboard or touchscreen, and gesture recognition technology can be used to detect the user's movements and recognize them as statements. Step 2: The analysis unit analyzes the statements received by the reception unit and extracts important information. The analysis unit uses natural language processing technology to analyze the user's statements and extracts important information based on keyword frequency and contextual importance. Furthermore, it can also evaluate the importance of statements and understand their meaning using machine learning algorithms and contextual analysis techniques. Step 3: The generation unit generates an infographic based on the information extracted by the analysis unit. The generation unit uses text generation AI and multimodal generation AI to generate an infographic that combines text and images. It can also use data visualization technology to visually represent the extracted information as graphs and charts. Step 4: The display unit displays the infographic generated by the generation unit. The display unit displays the infographic using a device such as a display, projector, or smartphone. Displays provide high resolution, projectors project onto a large screen, and allow for sharing among multiple people. Smartphones and tablets are easily portable, allowing users to view the infographic anytime, anywhere.

[0060] (Example of form 2) The voice dialogue AI system according to an embodiment of the present invention is a system that uses generative AI to visually convey the key points of voice dialogue. In this system, the generative AI engages in dialogue with the user, extracts the key points to avoid lengthy conversations, and displays them visually as an infographic. This allows the user to confirm the necessary information concisely and intuitively, realizing a smooth dialogue experience. Furthermore, it can improve the efficiency of information comprehension and maximize the value of the dialogue. For example, when the generative AI engages in dialogue with the user, it analyzes the user's statements and extracts important information. For example, if the user asks, "What's the weather like today?", the generative AI collects information about the weather and extracts the key points. Next, it visually displays the extracted key points as an infographic. The generative AI generates an infographic based on the extracted information and provides it to the user. For example, by visually displaying information about the weather, the user can grasp the weather situation at a glance. Through this mechanism, the user can confirm the necessary information concisely and intuitively. By the generative AI avoiding lengthy conversations and visually conveying the key points, the burden on the user is reduced, realizing a smooth dialogue experience. Furthermore, it can improve the efficiency of information comprehension and maximize the value of the dialogue. For example, if a user asks, "What are my plans for tomorrow?", the generative AI gathers information about the plans and extracts the key points. Next, it visually displays these extracted points as an infographic. This allows the user to grasp tomorrow's plans at a glance. Furthermore, the generative AI analyzes the user's statements and extracts important information, maximizing the value of the conversation. For example, if a user asks, "What's the latest news?", the generative AI gathers information about the news and extracts the key points. Next, it visually displays these extracted points as an infographic. This allows the user to grasp the latest news at a glance. In this way, by using generative AI to visually convey the key points of voice dialogue, users can confirm the necessary information concisely and intuitively, resulting in a smooth dialogue experience. It also improves the efficiency of information comprehension and maximizes the value of the dialogue. Thus, voice dialogue AI systems can efficiently analyze user statements and visually convey important information.

[0061] The voice dialogue AI system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives user utterances. User utterances include, but are not limited to, voice, text, and gestures. The reception unit receives user utterances using, for example, speech recognition technology. The reception unit can also receive text input. Furthermore, the reception unit can also receive user gestures using gesture recognition technology. For example, the reception unit converts user utterances into text data using speech recognition technology. Text input can be performed using a keyboard or touchscreen. Gesture recognition technology detects user movements using a camera or sensors and recognizes them as utterances. The analysis unit analyzes the utterances received by the reception unit and extracts important information. Important information is extracted based on, for example, keyword frequency and contextual importance, but is not limited to such examples. For example, the analysis unit analyzes user utterances using natural language processing technology and extracts important information. The analysis unit can also evaluate the importance of utterances using machine learning algorithms. Furthermore, the analysis unit can understand the meaning of statements using contextual analysis techniques and extract important information. For example, the analysis unit can extract keywords from user statements using natural language processing techniques and identify important information based on their frequency. Machine learning algorithms learn from large amounts of data and automatically evaluate the importance of statements. Contextual analysis techniques understand the meaning by considering the context of statements and extract important information. The generation unit generates infographics based on the information extracted by the analysis unit. Infographics are generated in the form of graphs, charts, diagrams, etc., but are not limited to these examples. For example, the generation unit generates infographics using text generation AI (e.g., LLM). The generation unit can also generate infographics that combine text and images using multimodal generation AI. Furthermore, the generation unit can visually represent the extracted information using data visualization techniques. For example, the generation unit uses text generation AI to summarize the extracted information into concise sentences and generate infographics based on those sentences.The multimodal generation AI combines text and images to generate visually easy-to-understand infographics. Data visualization technology visually represents the extracted information as graphs and charts. The display unit displays the infographics generated by the generation unit. The display unit displays the infographics using devices such as displays, projectors, and smartphones, but is not limited to these examples. For example, the display unit displays the infographics using a display. The display unit can also project the infographics onto a large screen using a projector. Furthermore, the display unit can display the infographics using a smartphone or tablet. For example, the display unit displays the infographics in high resolution using a display. A projector projects the infographics onto a large screen, enabling sharing among multiple people. Smartphones and tablets are easily portable, allowing users to view the infographics anytime, anywhere. As a result, the voice dialogue AI system according to the embodiment can efficiently analyze user statements and visually convey important information.

[0062] The reception unit receives user input. User input includes, but is not limited to, voice, text, and gestures. The reception unit can receive user input using, for example, speech recognition technology. Specifically, speech recognition technology converts user voice into text data in real time. Speech recognition technology includes a process of extracting voice features and breaking them down into phonemes and words. This allows for accurate transcription of user input into text. The reception unit can also accept text input. Text input can be done using a keyboard or touchscreen, and the system accepts input by the user directly typing characters. Furthermore, the reception unit can accept user gestures using gesture recognition technology. Gesture recognition technology detects user movements using cameras and sensors and recognizes specific actions as input. For example, it can analyze hand movements and facial expressions to understand the user's intent. This allows the reception unit to support diverse input methods such as voice, text, and gestures, and flexibly accept user input. In addition, the reception unit plays a role in centrally managing this input data and transmitting it to the analysis unit. This allows the reception desk to efficiently receive user messages and ensure smooth processing of the entire system.

[0063] The analysis unit analyzes the utterances received by the reception unit and extracts important information. Important information is extracted based on, for example, keyword frequency or contextual importance, but is not limited to these examples. Specifically, the analysis unit uses natural language processing (NLP) techniques to analyze user utterances and extract important information. NLP techniques include morphological analysis, syntactic analysis, and semantic analysis, which are combined to understand the content of the utterances. For example, morphological analysis is used to break down the utterance into individual words, and syntactic analysis is used to analyze the sentence structure. Furthermore, semantic analysis is used to understand the context and identify important information. The analysis unit can also use machine learning algorithms to evaluate the importance of utterances. Machine learning algorithms learn from large amounts of data and automatically identify patterns and features of utterances. This allows the analysis unit to quickly and accurately extract important information from user utterances. Furthermore, the analysis unit can also use contextual analysis techniques to understand the meaning of utterances and extract important information. Contextual analysis techniques consider the context of utterances to understand meaning and identify important information. For example, if a user makes consecutive utterances, the analysis relates the preceding and succeeding utterances to understand the overall context. This allows the analysis unit to gain a deeper understanding of the user's statements and accurately extract important information.

[0064] The generation unit generates infographics based on the information extracted by the analysis unit. Infographics are generated in various forms, such as graphs, charts, and diagrams, but are not limited to these examples. Specifically, the generation unit uses text generation AI (e.g., LLM) to generate infographics. The text generation AI generates natural-sounding text based on the extracted information and visually represents it. For example, it summarizes keywords and important information extracted from user statements into concise sentences and creates an infographic based on them. The generation unit can also use multimodal generation AI to generate infographics that combine text and images. The multimodal generation AI integrates text and image data to create visually easy-to-understand infographics. For example, it displays user statements as text and combines them with related images and icons for visual representation. Furthermore, the generation unit can use data visualization technology to visually represent the extracted information. Data visualization technology visually represents the extracted information as graphs and charts, making it easy for users to understand. For example, it displays user statements as bar graphs or pie charts, visually indicating the importance and frequency of the information. This allows the generation unit to effectively visualize the extracted information and communicate it clearly to the user.

[0065] The display unit displays the infographic generated by the generation unit. The display unit displays the infographic using devices such as displays, projectors, and smartphones, but is not limited to these examples. Specifically, the display unit displays the infographic in high resolution using a display. Displays include desktop monitors and large screens, which can clearly display detailed information. The display unit can also project the infographic onto a large screen using a projector. Projectors are used in conference rooms and classrooms, enabling information sharing among multiple people. Furthermore, the display unit can display the infographic using smartphones and tablets. Smartphones and tablets are easily portable, allowing users to view the infographic anytime, anywhere. For example, the infographic can be displayed on a smartphone or tablet so that users can view it while on the go. This allows the display unit to display generated infographics on a variety of devices, effectively conveying information to users. Furthermore, the display unit has interactive functions, allowing users to manipulate the infographic. For example, users can zoom in and out of the infographic or display detailed information using a touchscreen. This allows the display unit to provide users with flexible information display and facilitate understanding of the information.

[0066] The analysis unit can analyze user utterances and extract important information. For example, the analysis unit can use natural language processing techniques to analyze user utterances and extract important information. For example, the analysis unit can identify important information based on keyword frequency and contextual importance. The analysis unit can also use machine learning algorithms to evaluate the importance of utterances. For example, the analysis unit can learn from large amounts of data and automatically evaluate the importance of utterances. Furthermore, the analysis unit can use contextual analysis techniques to understand the meaning of utterances and extract important information. For example, the analysis unit can understand the meaning by considering the context of the utterance and extract important information. This enables efficient information transmission by extracting important information from user utterances. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user utterances into a generating AI and have the generating AI perform the extraction of important information.

[0067] The generation unit can generate infographics based on the extracted information. The generation unit can generate infographics using, for example, a text generation AI (e.g., LLM). For example, the generation unit can summarize the extracted information into concise sentences and generate an infographic based on them. The generation unit can also generate infographics that combine text and images using a multimodal generation AI. For example, the generation unit can combine text and images to generate a visually easy-to-understand infographic. Furthermore, the generation unit can visually represent the extracted information using data visualization technology. For example, the generation unit can visually represent the extracted information as graphs or charts. This helps users understand the extracted information by displaying it visually. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the extracted information into a generation AI and have the generation AI perform the generation of infographics.

[0068] The display unit can provide the generated infographic to the user. The display unit can display the infographic using a display, for example. For example, the display unit can display the infographic in high resolution. The display unit can also project the infographic onto a large screen using a projector. For example, the display unit can project the infographic onto a large screen using a projector, enabling sharing among multiple people. Furthermore, the display unit can display the infographic using a smartphone or tablet. For example, the display unit can be easily carried out using a smartphone or tablet, allowing users to view the infographic anytime, anywhere. This reduces the burden on the user by providing information visually. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the generated infographic into a generating AI and have the generating AI optimize the display method.

[0069] The reception system can estimate the user's emotions and adjust the timing of receiving their statements based on the estimated emotions. For example, if the user is excited, the reception system can delay receiving their statements to give them time to calm down. If the user is calm, the reception system can immediately receive their statements to facilitate smooth conversation. Furthermore, if the user is tired, the reception system can adjust the timing of receiving their statements to reduce their burden. In this way, smooth conversation is achieved by adjusting the timing of receiving statements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user emotion data into a generating AI and have the generating AI adjust the timing of receiving statements.

[0070] The reception unit can analyze the user's past utterance history and select the optimal reception method. For example, the reception unit can prioritize receiving phrases that the user has frequently used in the past. For example, by prioritizing phrases that the user has frequently used in the past, the reception unit can quickly understand the user's intent. The reception unit can also analyze the user's past utterance patterns and suggest the optimal reception method. For example, by analyzing the user's past utterance patterns and suggesting the optimal reception method, the reception unit can accurately grasp the user's intent. Furthermore, the reception unit can prioritize receiving utterances related to specific topics from the user's past utterance history. For example, by prioritizing receiving utterances related to specific topics from the user's past utterance history, the reception unit can provide highly relevant information. In this way, by analyzing the past utterance history, the reception unit can provide the user with the optimal reception method. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past utterance history into a generating AI and have the generating AI select the optimal reception method.

[0071] The reception unit can filter messages based on the user's current situation and areas of interest when receiving them. For example, the reception unit can prioritize receiving messages related to the user's current situation. For example, by prioritizing messages related to the user's current situation, the reception unit can provide highly relevant information. The reception unit can also filter and receive relevant messages based on the user's areas of interest. For example, by filtering and receiving relevant messages based on the user's areas of interest, the reception unit can provide information tailored to the user's interests. Furthermore, the reception unit can also filter out unnecessary messages based on the user's current situation and areas of interest. For example, by filtering out unnecessary messages based on the user's current situation and areas of interest, the reception unit can reduce the user's burden. This allows for the provision of highly relevant information by filtering messages based on the user's situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering of messages.

[0072] The reception desk can estimate the user's emotions and determine the priority of messages to receive based on the estimated emotions. For example, if the user is agitated, the reception desk will prioritize receiving important messages. For example, if the user is agitated, the reception desk will respond quickly by prioritizing important messages. The reception desk can also accept all messages equally if the user is calm. For example, if the user is calm, the reception desk will respond fairly by accepting all messages equally. Furthermore, if the user is tired, the reception desk can prioritize receiving concise messages. For example, if the user is tired, the reception desk will reduce the user's burden by prioritizing concise messages. In this way, by determining the priority of messages according to the user's emotions, important information can be received preferentially. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user emotion data into a generating AI and have the generating AI determine the priority of statements.

[0073] The reception system can prioritize receiving messages that are highly relevant based on the user's geographical location. For example, if the user is in a specific location, the reception system will prioritize receiving messages related to that location. For example, by prioritizing messages related to a specific location when the user is in a specific location, the reception system can provide appropriate information. The reception system can also filter and receive messages that are highly relevant based on the user's geographical location. For example, by filtering and receiving messages that are highly relevant based on the user's geographical location, the reception system can provide information that meets the user's needs. Furthermore, if the user is on the move, the reception system can prioritize receiving messages related to their current location. For example, by prioritizing messages related to the user's current location when the user is on the move, the reception system can provide real-time information. In this way, by prioritizing the reception of highly relevant messages based on geographical location, the system can provide users with appropriate information. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into the generating AI and have the AI ​​perform filtering of the user's statements.

[0074] The reception unit can analyze the user's social media activity when receiving a message and accept relevant messages. For example, the reception unit can prioritize receiving messages related to topics of interest from the user's social media activity. For example, by prioritizing the reception unit to receive messages related to topics of interest from the user's social media activity, it can provide information tailored to the user's interests. The reception unit can also analyze the user's social media activity and filter the messages to accept relevant ones. For example, by analyzing the user's social media activity and filtering the messages to accept relevant ones, it can eliminate unnecessary information. Furthermore, the reception unit can filter out unnecessary messages based on the user's social media activity. For example, by filtering out unnecessary messages based on the user's social media activity, the reception unit can reduce the burden on the user. In this way, by analyzing social media activity, it is possible to accept messages based on the user's interests. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the filtering of messages.

[0075] The analysis unit can estimate the user's emotions and adjust the method of extracting important information based on the estimated user emotions. For example, if the user is excited, the analysis unit can highlight and extract important information. For example, if the user is excited, the analysis unit can attract the user's attention by highlighting and extracting important information. The analysis unit can also extract all information equally if the user is calm. For example, if the user is calm, the analysis unit can provide fair information by extracting all information equally. Furthermore, if the user is tired, the analysis unit can prioritize extracting concise information. For example, if the user is tired, the analysis unit reduces the user's burden by prioritizing the extraction of concise information. In this way, important information can be effectively extracted by adjusting the method of information extraction according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the method of extracting the information.

[0076] The analysis unit can adjust the level of detail extracted based on the importance of the statements during analysis. For example, the analysis unit extracts detailed information for important statements. For example, by extracting detailed information for important statements, the analysis unit provides the user with the information they need. The analysis unit can also extract concise information for less important statements. For example, by extracting concise information for less important statements, the analysis unit reduces the burden on the user. Furthermore, the analysis unit can adjust the level of detail of the extracted information according to the importance of the statements. For example, by adjusting the level of detail of the extracted information according to the importance of the statements, the analysis unit can provide appropriate information. In this way, by adjusting the level of detail of the extraction according to the importance of the statements, appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input statement importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the extraction.

[0077] The analysis unit can apply different analysis algorithms depending on the category of the statement during analysis. For example, the analysis unit can apply a news-specific analysis algorithm to statements related to news. For example, by applying a news-specific analysis algorithm to statements related to news, the analysis unit can extract information with high accuracy. The analysis unit can also apply a weather-specific analysis algorithm to statements related to weather. For example, by applying a weather-specific analysis algorithm to statements related to weather, the analysis unit can provide accurate information. Furthermore, the analysis unit can also apply a schedule-specific analysis algorithm to statements related to schedules. For example, by applying a schedule-specific analysis algorithm to statements related to schedules, the analysis unit can support the user's schedule management. This makes it possible to extract information with high accuracy by applying an analysis algorithm according to the category of the statement. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input statement category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0078] The analysis unit can estimate the user's emotions and determine the priority of information to extract based on the estimated emotions. For example, if the user is agitated, the analysis unit will prioritize extracting important information. For example, if the user is agitated, the analysis unit will respond quickly by prioritizing the extraction of important information. The analysis unit can also extract all information equally if the user is calm. For example, if the user is calm, the analysis unit will provide fair information by extracting all information equally. Furthermore, if the user is tired, the analysis unit can prioritize extracting concise information. For example, if the user is tired, the analysis unit will reduce the user's burden by prioritizing the extraction of concise information. In this way, important information can be prioritized by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI determine the priority of the information.

[0079] The analysis unit can determine the extraction priority based on the submission date of the statements during analysis. For example, the analysis unit may prioritize the extraction of the most recent statements. For example, by prioritizing the extraction of the most recent statements, the analysis unit can provide users with the latest information. The analysis unit can also determine the extraction priority for older statements according to their importance. For example, by determining the extraction priority for older statements according to their importance, the analysis unit can provide appropriate information. Furthermore, the analysis unit can adjust the priority of the information to be extracted based on the submission date of the statements. For example, by adjusting the priority of the information to be extracted based on the submission date of the statements, the analysis unit can provide users with the information they need. This ensures that the latest information is provided preferentially by determining the extraction priority based on the submission date of the statements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input statement submission date data into a generating AI and have the generating AI determine the extraction priority.

[0080] The analysis unit can adjust the extraction order based on the relevance of the statements during analysis. For example, the analysis unit can prioritize the extraction of highly relevant statements. For example, by prioritizing the extraction of highly relevant statements, the analysis unit can provide the user with highly relevant information. The analysis unit can also postpone the extraction of less relevant statements. For example, by postponing the extraction of less relevant statements, the analysis unit can prioritize important information. Furthermore, the analysis unit can adjust the order of information to be extracted based on the relevance of the statements. For example, by adjusting the order of information to be extracted based on the relevance of the statements, the analysis unit can provide the user with the information they need. In this way, by adjusting the extraction order based on the relevance of the statements, highly relevant information can be provided preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input statement relevance data into a generating AI and have the generating AI perform the adjustment of the extraction order.

[0081] The generation unit can estimate the user's emotions and adjust the presentation of the infographic based on the estimated emotions. For example, if the user is excited, the generation unit can generate a visually stimulating infographic to attract the user's attention. The generation unit can also generate a simple and easy-to-read infographic if the user is calm. For example, if the user is calm, the generation unit can convey information clearly by generating a simple and easy-to-read infographic. Furthermore, if the user is tired, the generation unit can generate a concise and to-the-point infographic to reduce the user's burden. This allows for visually effective information delivery by adjusting the presentation of the infographic according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the infographic is presented.

[0082] The generation unit can adjust the level of detail of the infographic generated based on the importance of the information. For example, the generation unit can generate a detailed infographic for important information. For example, by generating a detailed infographic for important information, the generation unit can provide the user with the information they need. The generation unit can also generate a concise infographic for less important information. For example, by generating a concise infographic for less important information, the generation unit can reduce the burden on the user. Furthermore, the generation unit can adjust the level of detail of the infographic it generates according to the importance of the information. For example, by adjusting the level of detail of the infographic it generates according to the importance of the information, the generation unit can provide appropriate information. This makes it possible to provide appropriate information by adjusting the level of detail of the infographic according to the importance of the information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input information importance data into the generation AI and have the generation AI perform the adjustment of the level of detail of the infographic.

[0083] The generation unit can apply different generation algorithms depending on the category of information when generating infographics. For example, the generation unit can apply a news-specific generation algorithm to information about news. For example, by applying a news-specific generation algorithm to information about news, the generation unit can generate highly accurate infographics. The generation unit can also apply a weather-specific generation algorithm to information about weather. For example, by applying a weather-specific generation algorithm to information about weather, the generation unit can provide accurate information. Furthermore, the generation unit can also apply a schedule-specific generation algorithm to information about appointments. For example, by applying a schedule-specific generation algorithm to information about appointments, the generation unit can support the user's schedule management. This makes it possible to generate highly accurate infographics by applying a generation algorithm according to the category of information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input information category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0084] The generation unit can estimate the user's emotions and adjust the length of the infographic based on the estimated emotions. For example, if the user is excited, the generation unit can generate a short, concise infographic to capture the user's attention. The generation unit can also generate a longer infographic with detailed explanations if the user is calm. For example, if the user is calm, the generation unit can generate a longer infographic with detailed explanations to convey information clearly. Furthermore, if the user is tired, the generation unit can generate a concise and highly visual infographic to reduce the user's burden. This allows for visually effective information delivery by adjusting the length of the infographic according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the infographic.

[0085] The generation unit can determine the generation priority based on the information submission timing when generating infographics. For example, the generation unit can prioritize generating the latest information as infographics. For example, by prioritizing the generation of the latest information as infographics, the generation unit can provide users with the most up-to-date information. The generation unit can also determine the generation priority for older information according to its importance. For example, by determining the generation priority for older information according to its importance, the generation unit can provide appropriate information. Furthermore, the generation unit can adjust the priority of the infographics to be generated based on the information submission timing. For example, by adjusting the priority of the infographics to be generated based on the information submission timing, the generation unit can provide users with the information they need. This ensures that the latest information is provided preferentially by determining the generation priority based on the information submission timing. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input information submission timing data into the generation AI and have the generation AI determine the generation priority.

[0086] The generation unit can adjust the generation order based on the relevance of the information when generating infographics. For example, the generation unit can prioritize generating highly relevant information as infographics. For example, by prioritizing the generation of highly relevant information as infographics, the generation unit can provide users with highly relevant information. The generation unit can also postpone the generation of less relevant information. For example, by postponing the generation of less relevant information, the generation unit can prioritize important information. Furthermore, the generation unit can adjust the order of the infographics to be generated based on the relevance of the information. For example, by adjusting the order of the infographics to be generated based on the relevance of the information, the generation unit can provide users with the information they need. In this way, by adjusting the generation order based on the relevance of the information, highly relevant information can be provided preferentially. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0087] The display unit can estimate the user's emotions and adjust the way the infographic is displayed based on the estimated emotions. For example, if the user is excited, the display unit can provide a visually stimulating display to attract the user's attention. The display unit can also provide a simple and easy-to-read display when the user is calm. For example, if the user is calm, the display unit can convey information clearly by providing a simple and easy-to-read display. Furthermore, if the user is tired, the display unit can provide a concise and to-the-point display. For example, if the user is tired, the display unit can reduce the user's burden by providing a concise and to-the-point display. This makes it possible to provide visually effective information by adjusting the way the infographic is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generating AI and have the generating AI adjust the way the infographic is displayed.

[0088] The display unit can select the optimal display method by referring to the user's past operation history when displaying infographics. For example, the display unit can prioritize providing display methods that the user has previously preferred. For example, by prioritizing display methods that the user has previously preferred, the display unit can provide information tailored to the user's preferences. The display unit can also suggest the optimal display method based on the user's past operation history. For example, by suggesting the optimal display method based on the user's past operation history, the display unit can improve user visibility. Furthermore, the display unit can analyze the user's past operation history and select a display method with high visibility. For example, by analyzing the user's past operation history and selecting a display method with high visibility, the display unit can reduce the user's burden. This allows the display unit to provide the user with the optimal display method by referring to past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI select the display method.

[0089] The display unit can estimate the user's emotions and adjust the display order of the infographic based on the estimated emotions. For example, if the user is excited, the display unit can prioritize displaying important information. For example, if the user is excited, the display unit can attract the user's attention by prioritizing the display of important information. The display unit can also display all information equally if the user is calm. For example, if the user is calm, the display unit can provide fair information by displaying all information equally. Furthermore, if the user is tired, the display unit can prioritize displaying concise information. For example, if the user is tired, the display unit can reduce the user's burden by prioritizing the display of concise information. In this way, important information can be prioritized by adjusting the display order of the infographic according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generating AI and have the generating AI adjust the display order.

[0090] The display unit can select the optimal display method when displaying infographics, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the user is using a smartphone, the display unit can improve visibility by providing a display method that matches the screen size. The display unit can also provide a display method optimized for larger screens if the user is using a tablet. For example, if the user is using a tablet, the display unit can convey information clearly by providing a display method optimized for larger screens. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. For example, if the user is using a smartwatch, the display unit can reduce the burden on the user by providing a concise and highly visible display method. This allows the display unit to provide the user with appropriate information by providing the optimal display method based on device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the display method.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The reception system can analyze the tone and speed of the user's voice when receiving their statements, and estimate the user's emotional state. For example, if a user is speaking quickly, the reception system can estimate that the user is anxious and temporarily delay receiving their statement. Conversely, if a user is speaking slowly, the reception system can estimate that the user is relaxed and immediately receive their statement. Furthermore, if the user's voice tone is high, the reception system can estimate that the user is excited and adjust the reception of their statement accordingly. By adjusting the reception of statements according to the user's emotional state, smoother conversations can be achieved.

[0093] The analysis unit can extract important information by referring to the user's past utterance history when analyzing user utterances. For example, by prioritizing the extraction of keywords frequently used by the user in the past, the user's intent can be quickly understood. It can also analyze the user's past utterance patterns to identify important information. Furthermore, it can prioritize the extraction of information related to specific topics from the user's past utterance history. In this way, by utilizing past utterance history, the system can provide users with the most relevant information.

[0094] The generation unit can customize the information generated in the infographic based on the user's current situation and areas of interest. For example, it can provide highly relevant information by prioritizing the display of information related to the user's current situation. It can also incorporate relevant information into the infographic based on the user's areas of interest. Furthermore, it can exclude unnecessary information based on the user's current situation and areas of interest. This reduces the burden on the user by providing information tailored to their situation and areas of interest.

[0095] The display unit can select the optimal display method when displaying generated infographics, taking into account the user's geographical location. For example, if the user is in a specific location, information related to that location can be prioritized, providing appropriate information. It can also filter and display highly relevant information based on the user's geographical location. Furthermore, if the user is on the move, information related to their current location can be prioritized. This allows for the provision of relevant information based on geographical location, thereby providing users with appropriate information.

[0096] The reception system can estimate the user's emotions and adjust the timing of receiving their comments based on those emotions. For example, if the user is excited, the reception timing can be delayed to give them time to calm down. If the user is calm, their comments can be received immediately to facilitate smooth conversation. Furthermore, if the user is tired, the reception timing can be adjusted to reduce their burden. In this way, smooth conversations can be achieved by adjusting the timing of comments according to the user's emotions.

[0097] The analysis unit can adjust the level of detail extracted based on the importance of the user's statements when analyzing them. For example, for important statements, detailed information can be extracted to provide the user with the information they need. Conversely, for less important statements, concise information can be extracted to reduce the user's burden. Furthermore, the level of detail of the extracted information can be adjusted according to the importance of the statement. This allows for the provision of appropriate information by adjusting the level of detail of the extraction according to the importance of the statement.

[0098] The generation unit can apply different generation algorithms depending on the category of information when generating infographics. For example, by applying a generation algorithm specifically for news information, highly accurate infographics can be generated. Similarly, by applying a generation algorithm specifically for weather information, accurate information can be provided. Furthermore, by applying a generation algorithm specifically for schedule information, it is possible to support users' schedule management. In this way, applying a generation algorithm appropriate to the category of information enables the generation of highly accurate infographics.

[0099] The analysis unit can estimate the user's emotions and adjust the method of extracting important information based on the estimated emotions. For example, if the user is excited, the system can attract the user's attention by highlighting and extracting important information. If the user is calm, the system can provide fair information by extracting all information equally. Furthermore, if the user is tired, the system can reduce the user's burden by prioritizing the extraction of concise information. In this way, by adjusting the information extraction method according to the user's emotions, important information can be extracted effectively.

[0100] The generation unit can estimate the user's emotions and adjust the way the infographic is presented based on those emotions. For example, if the user is excited, a visually stimulating infographic can be generated to attract the user's attention. If the user is calm, a simple and highly visible infographic can be generated to convey information clearly. Furthermore, if the user is tired, a concise and to-the-point infographic can be generated to reduce the user's burden. In this way, by adjusting the way the infographic is presented according to the user's emotions, it becomes possible to provide visually effective information.

[0101] The display unit can select the optimal display method when displaying infographics, taking into account the user's device information. For example, if the user is using a smartphone, visibility can be improved by providing a display method that matches the screen size. If the user is using a tablet, information can be conveyed more clearly by providing a display method optimized for the larger screen. Furthermore, if the user is using a smartwatch, the burden on the user can be reduced by providing a concise and highly visible display method. In this way, by providing the optimal display method based on device information, the system can provide users with appropriate information.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The reception desk receives the user's statements. User statements can include voice, text, and gestures. The reception desk can use voice recognition technology to convert the user's statements into text data. Text input can be done using a keyboard or touchscreen, and gesture recognition technology can be used to detect the user's movements and recognize them as statements. Step 2: The analysis unit analyzes the statements received by the reception unit and extracts important information. The analysis unit uses natural language processing technology to analyze the user's statements and extracts important information based on keyword frequency and contextual importance. Furthermore, it can also evaluate the importance of statements and understand their meaning using machine learning algorithms and contextual analysis techniques. Step 3: The generation unit generates an infographic based on the information extracted by the analysis unit. The generation unit uses text generation AI and multimodal generation AI to generate an infographic that combines text and images. It can also use data visualization technology to visually represent the extracted information as graphs and charts. Step 4: The display unit displays the infographic generated by the generation unit. The display unit displays the infographic using a device such as a display, projector, or smartphone. Displays provide high resolution, projectors project onto a large screen, and allow for sharing among multiple people. Smartphones and tablets are easily portable, allowing users to view the infographic anytime, anywhere.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0106] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user statements using the microphone 38B or touch panel 38A of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's statements and extracts important information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an infographic based on the extracted information. The display unit displays the infographic using the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0114] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0115] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0116] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0117] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0118] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives the user's speech using the microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's speech and extracts important information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an infographic based on the extracted information. The display unit displays the infographic using the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives user statements using the microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's statements and extracts important information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an infographic based on the extracted information. The display unit displays the infographic using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0148] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives user statements using the microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's statements and extracts important information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an infographic based on the extracted information. The display unit displays the infographic using the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0157] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0159] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0160] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0161] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0165] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0166] 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.

[0167] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0168] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0169] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0170] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0172] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0173] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0174] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0175] (Note 1) A system characterized by comprising: a reception unit that receives user statements; an analysis unit that analyzes the statements received by the reception unit and extracts important information; a generation unit that generates an infographic based on the information extracted by the analysis unit; and a display unit that displays the infographic generated by the generation unit. (Note 2) The aforementioned analysis unit, Analyze user statements and extract important information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate an infographic based on the extracted information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is Provide the generated infographic to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving comments based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The reception unit is characterized by analyzing the user's past speech history and selecting an appropriate reception method, as described in Appendix 1. (Note 7) The aforementioned reception unit is When receiving a message, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and determines the priority of messages to accept based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving messages, the system prioritizes receiving messages that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving a comment, the system analyzes the user's social media activity and accepts relevant comments. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how important information is extracted based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the level of detail extracted is adjusted based on the importance of each statement. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the statement. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and determines the priority of information to extract based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the priority of extraction is determined based on when the statements were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of extraction is adjusted based on the relevance of the statements. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is We estimate the user's emotions and adjust the way the infographic is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating infographics, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating infographics, different generation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the length of the infographic based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating infographics, prioritize their creation based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating infographics, adjust the generation order based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is It estimates the user's emotions and adjusts how the infographic is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The system described in Appendix 1, characterized in that the display unit selects an appropriate display method by referring to the user's past operation history when displaying an infographic. (Note 25) The aforementioned display unit is It estimates the user's emotions and adjusts the display order of infographics based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The system described in Appendix 1, characterized in that the display unit selects an appropriate display method based on the user's device information when displaying an infographic. [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives user comments, An analysis unit analyzes the statements received by the reception unit and extracts important information, A generation unit that generates an infographic based on the information extracted by the analysis unit, The system includes a display unit that displays the infographic generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, It estimates the user's emotions and adjusts how important information is extracted based on the estimated user emotions. The system according to feature 1.

3. The generating unit is When generating infographics, adjust the level of detail based on the importance of the information. The system according to feature 1.

4. The aforementioned display unit is It estimates the user's emotions and adjusts the display order of infographics based on the estimated user emotions. The system according to feature 1.

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

6. The reception unit analyzes the user's past communication history and selects an appropriate reception method. The system according to feature 1.

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

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

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