system

Abstract

The system according to this embodiment aims to enable users who are not comfortable with digital operations to easily obtain information and perform operations. [Solution] The system according to the embodiment comprises a reception unit, a display unit, a proposal unit, a reading unit, a provision unit, an execution unit, and an analysis unit. The reception unit receives voice input. The display unit displays the information received by the reception unit on the screen. The proposal unit makes the best proposal based on the information received by the reception unit. The reading unit reads the necessary files. The provision unit organizes and provides the information read by the reading unit. The execution unit executes a program as needed. The analysis unit handles complex questions.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for users who are not good at digital operations to easily obtain information and perform operations.

[0005] The system according to the embodiment aims to enable users who are not good at digital operations to easily obtain information and perform operations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a display unit, a proposal unit, a reading unit, a provision unit, an execution unit, and an analysis unit. The reception unit receives voice input. The display unit displays the information received by the reception unit on the screen. The proposal unit makes optimal suggestions based on the information received by the reception unit. The reading unit reads the necessary files. The provision unit organizes and provides the information read by the reading unit. The execution unit executes programs as needed. The analysis unit handles complex questions. [Effects of the Invention]

[0007] The system according to this embodiment allows even users who are not comfortable with digital operations to easily obtain information and perform operations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The generative AI agent service according to an embodiment of the present invention is a system designed to support people who are not comfortable with digital operations. This generative AI agent service provides agent functions that utilize voice and video, and is designed to allow users to easily obtain information and perform operations. The generative AI agent service allows users to input information by voice, and the agent displays the content on the screen. Next, the generative AI agent service makes optimal suggestions to the user based on product specifications, web information, and thought processes. Furthermore, the generative AI agent service reads necessary files, organizes the information, and provides it. If necessary, the generative AI agent service executes a program and generates results that meet the user's requests. Finally, the generative AI agent service utilizes a large-scale language model and can handle complex questions. As a result, users can easily obtain information and perform operations. For example, if a user inputs "Tell me the weather forecast" by voice, the generative AI agent service will display the content on the screen. Also, if a user inputs "I'm looking for a new smartphone," the generative AI agent service will suggest the most suitable smartphone based on product specifications and web information. Furthermore, if a user inputs "Read this PDF file and tell me its contents," the generative AI agent service will read the PDF file, organize the contents, and provide them. When a user inputs "Analyze this data and create a graph," the generative AI agent service executes its program and generates the graph. Finally, when the user inputs "What trends can be seen from this data?", the generative AI agent service uses a large-scale language model to analyze the trends and provide the results. The generative AI agent service is particularly useful in situations where many users are not comfortable with digital operations, such as banks, hospitals, and municipal service counters. By utilizing generative AI, it provides an environment that users can operate intuitively and supports them in adapting to digitalization. This allows the generative AI agent service to acquire information and perform operations through voice input.

[0029] The generation AI agent service according to the embodiment comprises a reception unit, a display unit, a proposal unit, a reading unit, a provision unit, an execution unit, and an analysis unit. The reception unit receives voice input. The reception unit can receive voice input using, for example, a microphone. The reception unit can also receive voice input using the voice recognition function of a smartphone. Furthermore, the reception unit can convert the content of the voice input into text data. For example, the reception unit converts voice input into text data using voice recognition technology. The display unit displays the information received by the reception unit on the screen. For example, the display unit can display the voice input information on the screen so that it can be visually confirmed. The display unit can also adjust the display layout according to the type of information. For example, the display unit highlights and displays important information. The proposal unit makes optimal suggestions based on the information received by the reception unit. The proposal unit can make optimal suggestions based on, for example, product specifications, web information, and thought processes. The proposal unit can also make suggestions based on the user's past behavior history and current situation. For example, the proposal unit suggests new products based on products the user has purchased in the past. The reading unit reads the necessary files. The reading unit can read necessary files such as text files, image files, and audio files. The reading unit can also adjust the reading method depending on the file type. For example, the reading unit can read a PDF file and analyze its contents. The providing unit organizes and provides the information read by the reading unit. For example, the providing unit can classify and provide the information. The providing unit can also adjust the order in which the information is provided. For example, the providing unit can prioritize the provision of important information. The execution unit executes programs as needed. For example, the execution unit can execute programs such as data analysis and graph creation. The execution unit can also execute programs when specific conditions are met. For example, the execution unit executes programs in response to user requests. The analysis unit uses large-scale language models to handle complex questions. For example, the analysis unit can handle complex questions such as technical questions and legal questions.Furthermore, the analysis unit can also provide appropriate answers to user questions. For example, the analysis unit generates answers to user questions using a large-scale language model. This allows the generation AI agent service according to the embodiment to allow users to acquire information and perform operations through voice input.

[0030] The reception desk accepts voice input. For example, the reception desk can accept voice input using a microphone. Specifically, the microphone is highly sensitive and equipped with noise-canceling capabilities to eliminate ambient noise. This allows for clear capture of the user's voice. The reception desk can also accept voice input using the voice recognition function of a smartphone. The smartphone's voice recognition function uses the latest voice recognition algorithms and can recognize user speech with high accuracy. Furthermore, the reception desk can convert the content of the voice input into text data. For example, the reception desk converts voice input into text data using voice recognition technology. This voice recognition technology utilizes a deep learning-based model to analyze voice features with high accuracy and convert them into text. This ensures that the user's voice input is accurately recorded as text data. The reception desk can convert the content of the voice input into text in real time and provide it quickly to other departments. This allows for immediate processing of user voice input and improves the overall system response speed. Furthermore, the reception desk supports multiple languages, and can appropriately recognize and convert voice input in different languages. This allows for flexible responses to a global user base.

[0031] The display unit shows information received by the reception unit on the screen. For example, the display unit can display voice-input information so that it can be visually confirmed. Specifically, the display unit is equipped with a high-resolution display, allowing for clear display of text and images. Furthermore, the display unit can adjust the display layout according to the type of information. For example, the display unit can highlight important information. Important information is displayed visually to stand out by increasing the font size or changing the color. In addition, the display unit is customizable according to user preferences, allowing changes to the display layout and theme. This allows users to configure the display unit to their liking and create a user-friendly environment. The display unit has touchscreen functionality, allowing users to directly operate the screen. This enables users to intuitively manipulate information and quickly access the information they need. Furthermore, the display unit can display not only voice-input information but also information from the proposal and provision units. This allows users to view all information on a single screen and manage information efficiently.

[0032] The proposal department makes optimal suggestions based on the information received by the reception department. For example, the proposal department can make optimal suggestions based on product specifications, web information, and thought processes. Specifically, the proposal department analyzes the user's voice input and searches for relevant information based on its content. For example, if a user asks about a specific product, the proposal department searches for the product's specifications and reviews and makes the optimal suggestion. The proposal department can also make suggestions based on the user's past behavior history and current situation. For example, the proposal department can suggest new products based on products the user has purchased in the past. This enables personalized suggestions tailored to the user's preferences and needs. The proposal department uses AI to generate suggestions. The AI ​​analyzes the user's input, extracts relevant information, and makes optimal suggestions. For example, it uses natural language processing technology to understand the user's question and generate an appropriate answer. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy of its suggestions. This allows the proposal department to always provide users with the best possible suggestions and improve their satisfaction.

[0033] The reading unit reads the necessary files. The reading unit can read various files, such as text files, image files, and audio files. Specifically, the reading unit selects the appropriate reading method depending on the file type. For example, in the case of text files, it analyzes the file content and reads it as text data. In the case of image files, it uses image recognition technology to analyze the image content and extract the necessary information. In the case of audio files, it uses speech recognition technology to convert the audio data into text. Furthermore, the reading unit can also read PDF files and analyze their content. It extracts the content of PDF files as text data and obtains the necessary information. The reading unit utilizes parallel processing technology to optimize file reading speed. This allows it to read large numbers of files quickly. The reading unit can also detect file reading errors and perform appropriate error handling. This enables the reading unit to achieve reliable file reading and improve the overall system stability.

[0034] The information provider organizes and provides the information read by the information provider. For example, the information provider can classify and provide the information. Specifically, the information provider classifies the read information into categories so that users can quickly access the information they need. For example, it can classify information into different categories such as text information, image information, and audio information, and provide information for each category. The information provider can also adjust the order in which information is provided. For example, the information provider prioritizes providing important information. Important information is the information that users need most, and the information provider adjusts to display this first. Furthermore, the information provider can customize how information is provided. For example, information can be displayed in list format or grid format according to the user's preference. This allows users to browse information according to their preferences. The information provider can also adjust the frequency of information updates. For example, information that is updated in real time is always provided in its latest state. This allows users to always obtain the latest information. The information provider can collect user feedback and continuously improve the accuracy and effectiveness of the content provided. This allows the information provider to always provide users with the best possible information and improve satisfaction.

[0035] The execution unit executes programs as needed. For example, it can execute programs such as data analysis and graph creation. Specifically, the execution unit executes programs in response to user requests and generates the necessary results. For instance, if a user requests analysis of a specific dataset, the execution unit analyzes the dataset and displays the results as graphs or tables. The execution unit can also automatically execute programs when certain conditions are met. For example, it can automatically perform tasks such as periodic data backups and system maintenance. Furthermore, the execution unit can execute multiple programs in parallel. This allows for simultaneous processing of multiple tasks, improving the overall system efficiency. The execution unit can monitor program execution status and perform error handling as needed. This enables reliable program execution and improves the overall system stability. Additionally, the execution unit can collect user feedback and continuously improve program execution results. This allows the execution unit to consistently provide users with optimal results and improve satisfaction.

[0036] The analysis unit utilizes large-scale language models to handle complex questions. For example, it can handle complex questions such as technical or legal inquiries. Specifically, the analysis unit uses large-scale language models to analyze user questions and generate appropriate answers. These large-scale language models are trained on vast datasets and possess advanced natural language processing capabilities. This allows the analysis unit to accurately understand user questions and provide appropriate answers. For example, for technical questions, it searches relevant technical literature and databases to generate the best answer. For legal questions, it searches relevant legal documents and precedents to provide appropriate answers. Furthermore, the analysis unit can generate multiple answers to user questions and select the most appropriate one. This allows the analysis unit to provide highly accurate answers to users and improve satisfaction. The analysis unit can collect user feedback and continuously improve the accuracy of its answers. This ensures that the analysis unit always provides the best possible answers to users and improves satisfaction. Additionally, the analysis unit can analyze user trends and needs based on past question and answer history to provide more personalized answers. This allows the analysis unit to provide users with more appropriate answers and improve their satisfaction.

[0037] The reception desk accepts voice input. For example, the reception desk can accept voice input using a microphone. The reception desk can also accept voice input using the voice recognition function of a smartphone. The reception desk can convert the content of the voice input into text data. For example, the reception desk converts voice input into text data using voice recognition technology. This allows the reception desk to be operated intuitively by the user.

[0038] The display unit displays information received by the reception unit on the screen. For example, the display unit can display voice-input information on the screen so that it can be visually confirmed. The display unit can also adjust the display layout according to the type of information. For example, the display unit can highlight important information. This allows the display unit to visually confirm voice-input information.

[0039] The Proposal Department makes optimal proposals based on product specifications, web information, and thought processes. For example, the Proposal Department can make optimal proposals based on product specifications, web information, and thought processes. The Proposal Department can also make proposals based on the user's past behavior history and current situation. For example, the Proposal Department can propose new products based on products the user has purchased in the past. This allows the Proposal Department to make optimal proposals to users.

[0040] The reading unit reads the necessary files. The reading unit can read various files, such as text files, image files, and audio files. The reading unit can also adjust its reading method depending on the file type. For example, the reading unit can read a PDF file and analyze its contents. This allows the reading unit to read the necessary files.

[0041] The providing unit organizes and provides the information read by the reading unit. For example, the providing unit can classify and provide the information. The providing unit can also adjust the order in which the information is provided. For example, the providing unit can prioritize the provision of important information. This allows the providing unit to organize and provide the read information.

[0042] The execution unit executes programs as needed. For example, the execution unit can execute programs such as data analysis or graph creation. The execution unit can also execute programs when specific conditions are met. For example, the execution unit executes programs in response to user requests. This allows the execution unit to execute programs as needed.

[0043] The analysis unit utilizes large-scale language models to handle complex questions. For example, the analysis unit can handle complex questions such as technical or legal inquiries. The analysis unit can also provide appropriate answers to user questions. For instance, the analysis unit generates answers to user questions using large-scale language models, enabling it to handle complex questions.

[0044] The reception unit analyzes the user's past voice input history and selects the optimal voice recognition method. For example, the reception unit prioritizes recognizing voice commands that the user has frequently used in the past. The reception unit can also learn specific accents and pronunciation characteristics from the user's voice input history to improve recognition accuracy. Based on the user's past voice input history, the reception unit can also predict and recognize voice commands used during specific time periods. In this way, by analyzing the user's past voice input history, the reception unit can select the optimal voice recognition method and improve recognition accuracy.

[0045] The reception unit filters out the user's current ambient noise during voice input to remove noise. For example, if the user is in a noisy environment, the reception unit filters out ambient noise to remove noise from the voice input. If the user is in a quiet environment, the reception unit can also remove even subtle noise to improve the accuracy of the voice input. If the user is on the move when making a voice input, the reception unit can also filter out wind noise and car noise to remove noise. In this way, the reception unit can improve the accuracy of voice input by removing noise during voice input.

[0046] The reception desk prioritizes receiving highly relevant information when a user inputs voice data, taking into account their geographical location. For example, if the user is in a specific location, the reception desk will prioritize information related to that location. If the user is traveling, the reception desk can also prioritize information related to their travel destination. If the user is at home, the reception desk can also prioritize information related to their home. In this way, the reception desk can prioritize receiving highly relevant information by taking the user's geographical location into consideration.

[0047] The reception desk analyzes the user's social media activity during voice input and receives relevant information. For example, the reception desk prioritizes receiving information related to topics that the user frequently mentions on social media. The reception desk can also analyze the content of the user's social media posts and receive relevant information. The reception desk can also receive relevant information by referring to the activities of the user's social media followers and friends. In this way, the reception desk can prioritize receiving relevant information by analyzing the user's social media activity.

[0048] The display unit adjusts the level of detail based on the importance of the information when displaying it. For example, the display unit can display important information in detail so that the user can understand it immediately. The display unit can also display general information concisely so that the user can obtain only the information they need. The display unit can also highlight information that is highly urgent to attract the user's attention. In this way, the display unit can quickly obtain the information the user needs by adjusting the level of detail based on the importance of the information.

[0049] The display unit applies different display algorithms depending on the category of information during display. For example, the display unit can display news information in a timeline format, prioritizing the display of the latest information. The display unit can also display product information in a grid format to make it easier for users to compare items. The display unit can also display weather information in a map format to make it easier for users to understand visually. In this way, the display unit makes it easier for users to understand information by applying different display algorithms depending on the category of information.

[0050] The display unit determines the display priority based on when the information was submitted. For example, the display unit may prioritize displaying the latest information so that users can access it immediately. The display unit may also postpone displaying older information so that users can obtain only the information they need. The display unit may also highlight information of high urgency to attract the user's attention. In this way, the display unit can prioritize displaying the latest information by determining the display priority based on when the information was submitted.

[0051] The display unit adjusts the display order based on the relevance of the information during display. For example, the display unit can prioritize displaying information that the user is interested in, making it easily accessible to the user. The display unit can also postpone displaying less relevant information, allowing the user to obtain only the information they need. The display unit can also prioritize displaying highly relevant information based on the user's past browsing history. In this way, the display unit can quickly obtain the information the user needs by adjusting the display order based on the relevance of the information.

[0052] The proposal department adjusts the level of detail in its proposals based on the importance of the products. For example, it provides detailed proposals for important products so that users can understand them immediately. It can also provide concise proposals for general products so that users can obtain only the information they need. It can also highlight urgent products to attract the user's attention. In this way, the proposal department can quickly obtain the information users need by adjusting the level of detail in its proposals based on the importance of the products.

[0053] The proposal department applies different proposal algorithms depending on the product category when making proposals. For example, for electronic products, the proposal department applies a proposal algorithm that emphasizes specification comparison. For fashion products, the proposal department can also apply a proposal algorithm that emphasizes design and trends. For food products, the proposal department can also apply a proposal algorithm that emphasizes nutritional value and expiration date. In this way, the proposal department makes it easier for users to understand products by applying different proposal algorithms depending on the product category.

[0054] The proposal department prioritizes proposals based on when the products are submitted. For example, the proposal department may prioritize the newest products so that users can access them immediately. The proposal department may also postpone proposing older products so that users can obtain only the information they need. The proposal department may also highlight urgent products to attract the user's attention. This allows the proposal department to prioritize the newest products by prioritizing proposals based on when they are submitted.

[0055] The suggestion department adjusts the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion department prioritizes suggesting products that the user is interested in, making them easily accessible to the user. The suggestion department can also postpone suggesting less relevant products, allowing the user to obtain only the information they need. The suggestion department can also prioritize suggesting highly relevant products based on the user's past purchase history. In this way, the suggestion department can quickly obtain the information the user needs by adjusting the order of suggestions based on the relevance of the products.

[0056] The reading unit analyzes the user's past file reading history and selects the optimal reading method. For example, the reading unit prioritizes reading file formats that the user has frequently read in the past. The reading unit can also learn the characteristics of specific file formats from the user's file reading history and improve reading accuracy. Based on the user's past file reading history, the reading unit can also predict and read file formats used during specific time periods. In this way, the reading unit can select the optimal reading method and improve reading accuracy by analyzing the user's past file reading history.

[0057] The reading unit filters out the user's current ambient noise to remove noise when reading files. For example, if the user is in a noisy environment, the reading unit filters out ambient noise to remove noise during file reading. If the user is in a quiet environment, the reading unit can also remove even minute noises to improve the accuracy of file reading. If the user is reading files while on the move, the reading unit can also filter out wind noise and car noise to remove noise. In this way, the reading unit can improve reading accuracy by removing noise during file reading.

[0058] The file reading unit prioritizes reading files that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reading unit will prioritize reading files related to that location. If the user is traveling, the reading unit can also prioritize reading files related to the travel destination. If the user is at home, the reading unit can also prioritize reading files related to home. In this way, the reading unit can prioritize reading highly relevant files by taking the user's geographical location into consideration.

[0059] The information provider adjusts the level of detail based on the importance of the information when providing it. For example, the provider provides detailed information on important information so that users can understand it immediately. The provider can also provide general information concisely so that users can obtain only the information they need. The provider can also highlight urgent information to attract the user's attention. In this way, the provider can quickly obtain the information users need by adjusting the level of detail based on the importance of the information.

[0060] The information provider selects the most appropriate method of information delivery by considering the user's geographical location. For example, if the user is in a specific location, the provider will prioritize providing information related to that location. If the user is traveling, the provider can also prioritize providing information related to their travel destination. If the user is at home, the provider can also prioritize providing information related to their home. In this way, the provider can select the most appropriate method of information delivery by considering the user's geographical location.

[0061] The execution unit analyzes the user's past execution history to select the optimal execution method when a program is executed. For example, the execution unit prioritizes the execution of programs that the user has frequently executed in the past. The execution unit can also learn the characteristics of specific programs from the user's execution history and improve execution accuracy. Based on the user's past execution history, the execution unit can also predict and execute programs to be used during specific time periods. In this way, the execution unit can select the optimal execution method and improve execution accuracy by analyzing the user's past execution history.

[0062] The execution unit selects the optimal execution method when executing a program, taking into account the user's geographical location. For example, if the user is in a specific location, the execution unit will prioritize executing programs related to that location. If the user is traveling, the execution unit can also prioritize executing programs related to the travel destination. If the user is at home, the execution unit can also prioritize executing programs related to home. In this way, the execution unit can select the optimal execution method by taking into account the user's geographical location.

[0063] The analysis unit optimizes the current analysis by referring to past analysis data during the analysis process. For example, the analysis unit improves the accuracy of the current analysis based on past analysis data. The analysis unit can also learn specific patterns from past analysis data and apply them to the current analysis. The analysis unit can also improve the efficiency of the current analysis by referring to past analysis data. In this way, the analysis unit can improve the accuracy of the current analysis by referring to past analysis data.

[0064] The analysis unit applies different analysis methods to each data category during analysis. For example, the analysis unit applies natural language processing to text data to improve analysis accuracy. The analysis unit can also apply image recognition technology to image data to improve analysis accuracy. The analysis unit can also apply speech recognition technology to audio data to improve analysis accuracy. In this way, the analysis unit can improve analysis accuracy by applying different analysis methods to each data category.

[0065] The analysis unit prioritizes analysis based on the data submission date. For example, the analysis unit prioritizes analyzing the most recent data so that users can access it immediately. The analysis unit can also postpone the analysis of older data so that users can obtain only the information they need. The analysis unit can also highlight and analyze data with high urgency to attract the user's attention. This allows the analysis unit to prioritize the analysis of the most recent data by prioritizing analysis based on the data submission date.

[0066] The analysis unit performs analysis by referencing relevant market data during the analysis process. For example, the analysis unit improves the accuracy of the current data analysis based on the relevant market data. The analysis unit can also learn specific patterns from the relevant market data and apply them to the current data. The analysis unit can also improve the efficiency of the current data by referencing the relevant market data. In this way, the analysis unit can improve the accuracy of the current data analysis by referencing the relevant market data.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The reception unit can analyze the user's past voice input history when receiving user voice input and select the optimal voice recognition method. For example, it can prioritize recognizing voice commands that the user has frequently used in the past. The reception unit can also learn specific accents and pronunciation characteristics from the user's voice input history to improve recognition accuracy. Furthermore, the reception unit can predict and recognize voice commands that the user will use at specific times of the day based on their past voice input history. In this way, the reception unit can select the optimal voice recognition method and improve recognition accuracy by analyzing the user's past voice input history.

[0069] The execution unit can analyze the user's past execution history to select the optimal execution method when a program is executed. For example, it can prioritize the execution of programs that the user has frequently run in the past. Furthermore, the execution unit can learn the characteristics of specific programs from the user's execution history to improve execution accuracy. In addition, the execution unit can predict and execute programs to be used during specific time periods based on the user's past execution history. This allows the execution unit to select the optimal execution method and improve execution accuracy by analyzing the user's past execution history.

[0070] The analysis unit can optimize the current analysis by referring to past analysis data during the analysis process. For example, it can improve the accuracy of the current analysis based on past analysis data. Furthermore, the analysis unit can learn specific patterns from past analysis data and apply them to the current analysis. In addition, the analysis unit can improve the efficiency of the current analysis by referring to past analysis data. Thus, the analysis unit can improve the accuracy of the current analysis by referring to past analysis data.

[0071] The reception unit can filter out ambient noise during voice input to remove unwanted sounds. For example, if the user is in a noisy environment, it can filter out ambient noise to remove unwanted sounds from the voice input. Conversely, if the user is in a quiet environment, it can also remove even subtle noises to improve the accuracy of the voice input. Furthermore, if the user is on the move when using voice input, it can filter out wind noise and vehicle noise to remove unwanted sounds. In this way, the reception unit can improve the accuracy of voice input by removing noise during the process.

[0072] The display unit can adjust the level of detail based on the importance of the information being displayed. For example, important information can be displayed in detail so that the user can understand it immediately. General information can be displayed concisely so that the user can obtain only the information they need. Furthermore, information of high urgency can be highlighted to attract the user's attention. In this way, the display unit can adjust the level of detail based on the importance of the information, allowing the user to quickly obtain the information they need.

[0073] The proposal department can apply different proposal algorithms depending on the product category. For example, electronic products can be proposed using an algorithm that emphasizes specification comparisons. Fashion products can be proposed using an algorithm that emphasizes design and trends. Furthermore, food products can be proposed using an algorithm that emphasizes nutritional value and expiration date. By applying different proposal algorithms depending on the product category, the proposal department makes it easier for users to understand the products.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The reception desk accepts voice input. For example, it can accept voice input using a microphone or the voice recognition function of a smartphone. It can also convert the content of the voice input into text data using voice recognition technology. Step 2: The display unit displays the information received by the reception unit on the screen. For example, it can display voice-input information on the screen for visual confirmation, and the display layout can be adjusted according to the type of information. Step 3: The proposal department makes the best proposal based on the information received by the reception department. For example, they can make the best proposal based on product specifications, web information, and thought processes, or they can make proposals based on the user's past behavior history and current situation. Step 4: The loading section loads the necessary files. For example, it loads necessary files such as text files, image files, and audio files, and can adjust the loading method according to the file type. Step 5: The providing unit organizes and provides the information read by the reading unit. For example, it can classify and provide the information, and adjust the order in which the information is provided. Step 6: The execution unit runs the program as needed. For example, it can run programs such as data analysis or graph creation, or execute a program when certain conditions are met. Step 7: The analysis unit uses a large-scale language model to handle complex questions. For example, it can handle complex questions such as technical questions or legal questions and provide appropriate answers to user inquiries.

[0076] (Example of form 2) The generative AI agent service according to an embodiment of the present invention is a system designed to support people who are not comfortable with digital operations. This generative AI agent service provides agent functions that utilize voice and video, and is designed to allow users to easily obtain information and perform operations. The generative AI agent service allows users to input information by voice, and the agent displays the content on the screen. Next, the generative AI agent service makes optimal suggestions to the user based on product specifications, web information, and thought processes. Furthermore, the generative AI agent service reads necessary files, organizes the information, and provides it. If necessary, the generative AI agent service executes a program and generates results that meet the user's requests. Finally, the generative AI agent service utilizes a large-scale language model and can handle complex questions. As a result, users can easily obtain information and perform operations. For example, if a user inputs "Tell me the weather forecast" by voice, the generative AI agent service will display the content on the screen. Also, if a user inputs "I'm looking for a new smartphone," the generative AI agent service will suggest the most suitable smartphone based on product specifications and web information. Furthermore, if a user inputs "Read this PDF file and tell me its contents," the generative AI agent service will read the PDF file, organize the contents, and provide them. When a user inputs "Analyze this data and create a graph," the generative AI agent service executes its program and generates the graph. Finally, when the user inputs "What trends can be seen from this data?", the generative AI agent service uses a large-scale language model to analyze the trends and provide the results. The generative AI agent service is particularly useful in situations where many users are not comfortable with digital operations, such as banks, hospitals, and municipal service counters. By utilizing generative AI, it provides an environment that users can operate intuitively and supports them in adapting to digitalization. This allows the generative AI agent service to acquire information and perform operations through voice input.

[0077] The generation AI agent service according to the embodiment comprises a reception unit, a display unit, a proposal unit, a reading unit, a provision unit, an execution unit, and an analysis unit. The reception unit receives voice input. The reception unit can receive voice input using, for example, a microphone. The reception unit can also receive voice input using the voice recognition function of a smartphone. Furthermore, the reception unit can convert the content of the voice input into text data. For example, the reception unit converts voice input into text data using voice recognition technology. The display unit displays the information received by the reception unit on the screen. For example, the display unit can display the voice input information on the screen so that it can be visually confirmed. The display unit can also adjust the display layout according to the type of information. For example, the display unit highlights and displays important information. The proposal unit makes optimal suggestions based on the information received by the reception unit. The proposal unit can make optimal suggestions based on, for example, product specifications, web information, and thought processes. The proposal unit can also make suggestions based on the user's past behavior history and current situation. For example, the proposal unit suggests new products based on products the user has purchased in the past. The reading unit reads the necessary files. The reading unit can read necessary files such as text files, image files, and audio files. The reading unit can also adjust the reading method depending on the file type. For example, the reading unit can read a PDF file and analyze its contents. The providing unit organizes and provides the information read by the reading unit. For example, the providing unit can classify and provide the information. The providing unit can also adjust the order in which the information is provided. For example, the providing unit can prioritize the provision of important information. The execution unit executes programs as needed. For example, the execution unit can execute programs such as data analysis and graph creation. The execution unit can also execute programs when specific conditions are met. For example, the execution unit executes programs in response to user requests. The analysis unit uses large-scale language models to handle complex questions. For example, the analysis unit can handle complex questions such as technical questions and legal questions.Furthermore, the analysis unit can also provide appropriate answers to user questions. For example, the analysis unit generates answers to user questions using a large-scale language model. This allows the generation AI agent service according to the embodiment to allow users to acquire information and perform operations through voice input.

[0078] The reception desk accepts voice input. For example, the reception desk can accept voice input using a microphone. Specifically, the microphone is highly sensitive and equipped with noise-canceling capabilities to eliminate ambient noise. This allows for clear capture of the user's voice. The reception desk can also accept voice input using the voice recognition function of a smartphone. The smartphone's voice recognition function uses the latest voice recognition algorithms and can recognize user speech with high accuracy. Furthermore, the reception desk can convert the content of the voice input into text data. For example, the reception desk converts voice input into text data using voice recognition technology. This voice recognition technology utilizes a deep learning-based model to analyze voice features with high accuracy and convert them into text. This ensures that the user's voice input is accurately recorded as text data. The reception desk can convert the content of the voice input into text in real time and provide it quickly to other departments. This allows for immediate processing of user voice input and improves the overall system response speed. Furthermore, the reception desk supports multiple languages, and can appropriately recognize and convert voice input in different languages. This allows for flexible responses to a global user base.

[0079] The display unit shows information received by the reception unit on the screen. For example, the display unit can display voice-input information so that it can be visually confirmed. Specifically, the display unit is equipped with a high-resolution display, allowing for clear display of text and images. Furthermore, the display unit can adjust the display layout according to the type of information. For example, the display unit can highlight important information. Important information is displayed visually to stand out by increasing the font size or changing the color. In addition, the display unit is customizable according to user preferences, allowing changes to the display layout and theme. This allows users to configure the display unit to their liking and create a user-friendly environment. The display unit has touchscreen functionality, allowing users to directly operate the screen. This enables users to intuitively manipulate information and quickly access the information they need. Furthermore, the display unit can display not only voice-input information but also information from the proposal and provision units. This allows users to view all information on a single screen and manage information efficiently.

[0080] The proposal department makes optimal suggestions based on the information received by the reception department. For example, the proposal department can make optimal suggestions based on product specifications, web information, and thought processes. Specifically, the proposal department analyzes the user's voice input and searches for relevant information based on its content. For example, if a user asks about a specific product, the proposal department searches for the product's specifications and reviews and makes the optimal suggestion. The proposal department can also make suggestions based on the user's past behavior history and current situation. For example, the proposal department can suggest new products based on products the user has purchased in the past. This enables personalized suggestions tailored to the user's preferences and needs. The proposal department uses AI to generate suggestions. The AI ​​analyzes the user's input, extracts relevant information, and makes optimal suggestions. For example, it uses natural language processing technology to understand the user's question and generate an appropriate answer. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy of its suggestions. This allows the proposal department to always provide users with the best possible suggestions and improve their satisfaction.

[0081] The reading unit reads the necessary files. The reading unit can read various files, such as text files, image files, and audio files. Specifically, the reading unit selects the appropriate reading method depending on the file type. For example, in the case of text files, it analyzes the file content and reads it as text data. In the case of image files, it uses image recognition technology to analyze the image content and extract the necessary information. In the case of audio files, it uses speech recognition technology to convert the audio data into text. Furthermore, the reading unit can also read PDF files and analyze their content. It extracts the content of PDF files as text data and obtains the necessary information. The reading unit utilizes parallel processing technology to optimize file reading speed. This allows it to read large numbers of files quickly. The reading unit can also detect file reading errors and perform appropriate error handling. This enables the reading unit to achieve reliable file reading and improve the overall system stability.

[0082] The information provider organizes and provides the information read by the information provider. For example, the information provider can classify and provide the information. Specifically, the information provider classifies the read information into categories so that users can quickly access the information they need. For example, it can classify information into different categories such as text information, image information, and audio information, and provide information for each category. The information provider can also adjust the order in which information is provided. For example, the information provider prioritizes providing important information. Important information is the information that users need most, and the information provider adjusts to display this first. Furthermore, the information provider can customize how information is provided. For example, information can be displayed in list format or grid format according to the user's preference. This allows users to browse information according to their preferences. The information provider can also adjust the frequency of information updates. For example, information that is updated in real time is always provided in its latest state. This allows users to always obtain the latest information. The information provider can collect user feedback and continuously improve the accuracy and effectiveness of the content provided. This allows the information provider to always provide users with the best possible information and improve satisfaction.

[0083] The execution unit executes programs as needed. For example, it can execute programs such as data analysis and graph creation. Specifically, the execution unit executes programs in response to user requests and generates the necessary results. For instance, if a user requests analysis of a specific dataset, the execution unit analyzes the dataset and displays the results as graphs or tables. The execution unit can also automatically execute programs when certain conditions are met. For example, it can automatically perform tasks such as periodic data backups and system maintenance. Furthermore, the execution unit can execute multiple programs in parallel. This allows for simultaneous processing of multiple tasks, improving the overall system efficiency. The execution unit can monitor program execution status and perform error handling as needed. This enables reliable program execution and improves the overall system stability. Additionally, the execution unit can collect user feedback and continuously improve program execution results. This allows the execution unit to consistently provide users with optimal results and improve satisfaction.

[0084] The analysis unit utilizes large-scale language models to handle complex questions. For example, it can handle complex questions such as technical or legal inquiries. Specifically, the analysis unit uses large-scale language models to analyze user questions and generate appropriate answers. These large-scale language models are trained on vast datasets and possess advanced natural language processing capabilities. This allows the analysis unit to accurately understand user questions and provide appropriate answers. For example, for technical questions, it searches relevant technical literature and databases to generate the best answer. For legal questions, it searches relevant legal documents and precedents to provide appropriate answers. Furthermore, the analysis unit can generate multiple answers to user questions and select the most appropriate one. This allows the analysis unit to provide highly accurate answers to users and improve satisfaction. The analysis unit can collect user feedback and continuously improve the accuracy of its answers. This ensures that the analysis unit always provides the best possible answers to users and improves satisfaction. Additionally, the analysis unit can analyze user trends and needs based on past question and answer history to provide more personalized answers. This allows the analysis unit to provide users with more appropriate answers and improve their satisfaction.

[0085] The reception desk accepts voice input. For example, the reception desk can accept voice input using a microphone. The reception desk can also accept voice input using the voice recognition function of a smartphone. The reception desk can convert the content of the voice input into text data. For example, the reception desk converts voice input into text data using voice recognition technology. This allows the reception desk to be operated intuitively by the user.

[0086] The display unit displays information received by the reception unit on the screen. For example, the display unit can display voice-input information on the screen so that it can be visually confirmed. The display unit can also adjust the display layout according to the type of information. For example, the display unit can highlight important information. This allows the display unit to visually confirm voice-input information.

[0087] The Proposal Department makes optimal proposals based on product specifications, web information, and thought processes. For example, the Proposal Department can make optimal proposals based on product specifications, web information, and thought processes. The Proposal Department can also make proposals based on the user's past behavior history and current situation. For example, the Proposal Department can propose new products based on products the user has purchased in the past. This allows the Proposal Department to make optimal proposals to users.

[0088] The reading unit reads the necessary files. The reading unit can read various files, such as text files, image files, and audio files. The reading unit can also adjust its reading method depending on the file type. For example, the reading unit can read a PDF file and analyze its contents. This allows the reading unit to read the necessary files.

[0089] The providing unit organizes and provides the information read by the reading unit. For example, the providing unit can classify and provide the information. The providing unit can also adjust the order in which the information is provided. For example, the providing unit can prioritize the provision of important information. This allows the providing unit to organize and provide the read information.

[0090] The execution unit executes programs as needed. For example, the execution unit can execute programs such as data analysis or graph creation. The execution unit can also execute programs when specific conditions are met. For example, the execution unit executes programs in response to user requests. This allows the execution unit to execute programs as needed.

[0091] The analysis unit utilizes large-scale language models to handle complex questions. For example, the analysis unit can handle complex questions such as technical or legal inquiries. The analysis unit can also provide appropriate answers to user questions. For instance, the analysis unit generates answers to user questions using large-scale language models, enabling it to handle complex questions.

[0092] The reception system estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated emotions. For example, if the user is stressed, the reception system will delay the timing of voice input acceptance and wait until the user is relaxed. If the user is relaxed, the reception system can also speed up the timing of voice input acceptance to accept input smoothly. If the user is in a hurry, the reception system can also make the timing of voice input acceptance immediate to accept input quickly. In this way, the reception system can accept voice input at a more appropriate time by adjusting the timing of voice input acceptance 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.

[0093] The reception unit analyzes the user's past voice input history and selects the optimal voice recognition method. For example, the reception unit prioritizes recognizing voice commands that the user has frequently used in the past. The reception unit can also learn specific accents and pronunciation characteristics from the user's voice input history to improve recognition accuracy. Based on the user's past voice input history, the reception unit can also predict and recognize voice commands used during specific time periods. In this way, by analyzing the user's past voice input history, the reception unit can select the optimal voice recognition method and improve recognition accuracy.

[0094] The reception unit filters out the user's current ambient noise during voice input to remove noise. For example, if the user is in a noisy environment, the reception unit filters out ambient noise to remove noise from the voice input. If the user is in a quiet environment, the reception unit can also remove even subtle noise to improve the accuracy of the voice input. If the user is on the move when making a voice input, the reception unit can also filter out wind noise and car noise to remove noise. In this way, the reception unit can improve the accuracy of voice input by removing noise during voice input.

[0095] The reception system estimates the user's emotions and prioritizes voice input based on the estimated emotions. For example, if the user is nervous, the reception system will prioritize important voice input. If the user is relaxed, the reception system may also prioritize all voice input equally. If the user is in a hurry, the reception system may also prioritize urgent voice input. In this way, the reception system can prioritize important voice input by determining the priority of voice input 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.

[0096] The reception desk prioritizes receiving highly relevant information when a user inputs voice data, taking into account their geographical location. For example, if the user is in a specific location, the reception desk will prioritize information related to that location. If the user is traveling, the reception desk can also prioritize information related to their travel destination. If the user is at home, the reception desk can also prioritize information related to their home. In this way, the reception desk can prioritize receiving highly relevant information by taking the user's geographical location into consideration.

[0097] The reception desk analyzes the user's social media activity during voice input and receives relevant information. For example, the reception desk prioritizes receiving information related to topics that the user frequently mentions on social media. The reception desk can also analyze the content of the user's social media posts and receive relevant information. The reception desk can also receive relevant information by referring to the activities of the user's social media followers and friends. In this way, the reception desk can prioritize receiving relevant information by analyzing the user's social media activity.

[0098] The display unit estimates the user's emotions and adjusts the way the displayed content is presented based on the estimated emotions. For example, if the user is tense, the display unit provides a simple and highly visible display method. If the user is relaxed, the display unit can also provide a display method that includes detailed information. If the user is in a hurry, the display unit can also provide a display method that gets straight to the point. In this way, the display unit can provide a more appropriate display method by adjusting the way the displayed content is presented 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.

[0099] The display unit adjusts the level of detail based on the importance of the information when displaying it. For example, the display unit can display important information in detail so that the user can understand it immediately. The display unit can also display general information concisely so that the user can obtain only the information they need. The display unit can also highlight information that is highly urgent to attract the user's attention. In this way, the display unit can quickly obtain the information the user needs by adjusting the level of detail based on the importance of the information.

[0100] The display unit applies different display algorithms depending on the category of information during display. For example, the display unit can display news information in a timeline format, prioritizing the display of the latest information. The display unit can also display product information in a grid format to make it easier for users to compare items. The display unit can also display weather information in a map format to make it easier for users to understand visually. In this way, the display unit makes it easier for users to understand information by applying different display algorithms depending on the category of information.

[0101] The display unit estimates the user's emotions and adjusts the length of the displayed content based on the estimated emotions. For example, if the user is in a hurry, the display unit provides short, concise content. If the user is relaxed, the display unit can also provide longer content that includes detailed explanations. If the user is excited, the display unit can also provide content with visually stimulating effects. In this way, the display unit can provide more appropriate content by adjusting the length of the displayed content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The display unit determines the display priority based on when the information was submitted. For example, the display unit may prioritize displaying the latest information so that users can access it immediately. The display unit may also postpone displaying older information so that users can obtain only the information they need. The display unit may also highlight information of high urgency to attract the user's attention. In this way, the display unit can prioritize displaying the latest information by determining the display priority based on when the information was submitted.

[0103] The display unit adjusts the display order based on the relevance of the information during display. For example, the display unit can prioritize displaying information that the user is interested in, making it easily accessible to the user. The display unit can also postpone displaying less relevant information, allowing the user to obtain only the information they need. The display unit can also prioritize displaying highly relevant information based on the user's past browsing history. In this way, the display unit can quickly obtain the information the user needs by adjusting the display order based on the relevance of the information.

[0104] The suggestion function estimates the user's emotions and adjusts the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion function provides a simple and easily understandable suggestion. If the user is relaxed, the suggestion function may also provide a suggestion that includes detailed information. If the user is in a hurry, the suggestion function may also provide a concise suggestion. In this way, the suggestion function can provide more appropriate suggestions by adjusting the way suggestions are presented 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.

[0105] The proposal department adjusts the level of detail in its proposals based on the importance of the products. For example, it provides detailed proposals for important products so that users can understand them immediately. It can also provide concise proposals for general products so that users can obtain only the information they need. It can also highlight urgent products to attract the user's attention. In this way, the proposal department can quickly obtain the information users need by adjusting the level of detail in its proposals based on the importance of the products.

[0106] The proposal department applies different proposal algorithms depending on the product category when making proposals. For example, for electronic products, the proposal department applies a proposal algorithm that emphasizes specification comparison. For fashion products, the proposal department can also apply a proposal algorithm that emphasizes design and trends. For food products, the proposal department can also apply a proposal algorithm that emphasizes nutritional value and expiration date. In this way, the proposal department makes it easier for users to understand products by applying different proposal algorithms depending on the product category.

[0107] The suggestion function estimates the user's emotions and adjusts the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion function provides short, concise suggestions. If the user is relaxed, the suggestion function may provide longer suggestions with detailed explanations. If the user is excited, the suggestion function may also provide suggestions with visually stimulating effects. In this way, the suggestion function can provide more appropriate suggestions by adjusting the length of suggestions 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.

[0108] The proposal department prioritizes proposals based on when the products are submitted. For example, the proposal department may prioritize the newest products so that users can access them immediately. The proposal department may also postpone proposing older products so that users can obtain only the information they need. The proposal department may also highlight urgent products to attract the user's attention. This allows the proposal department to prioritize the newest products by prioritizing proposals based on when they are submitted.

[0109] The suggestion department adjusts the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion department prioritizes suggesting products that the user is interested in, making them easily accessible to the user. The suggestion department can also postpone suggesting less relevant products, allowing the user to obtain only the information they need. The suggestion department can also prioritize suggesting highly relevant products based on the user's past purchase history. In this way, the suggestion department can quickly obtain the information the user needs by adjusting the order of suggestions based on the relevance of the products.

[0110] The reading unit estimates the user's emotions and adjusts the file loading timing based on the estimated emotions. For example, if the user is stressed, the reading unit will delay the file loading time and wait until the user is relaxed. If the user is relaxed, the reading unit can speed up the file loading time for smoother loading. If the user is in a hurry, the reading unit can load the file immediately for faster loading. In this way, the reading unit can load files at a more appropriate time by adjusting the file loading timing 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.

[0111] The reading unit analyzes the user's past file reading history and selects the optimal reading method. For example, the reading unit prioritizes reading file formats that the user has frequently read in the past. The reading unit can also learn the characteristics of specific file formats from the user's file reading history and improve reading accuracy. Based on the user's past file reading history, the reading unit can also predict and read file formats used during specific time periods. In this way, the reading unit can select the optimal reading method and improve reading accuracy by analyzing the user's past file reading history.

[0112] The reading unit filters out the user's current ambient noise to remove noise when reading files. For example, if the user is in a noisy environment, the reading unit filters out ambient noise to remove noise during file reading. If the user is in a quiet environment, the reading unit can also remove even minute noises to improve the accuracy of file reading. If the user is reading files while on the move, the reading unit can also filter out wind noise and car noise to remove noise. In this way, the reading unit can improve reading accuracy by removing noise during file reading.

[0113] The reading unit estimates the user's emotions and determines the priority of files to read based on the estimated emotions. For example, if the user is stressed, the reading unit will prioritize reading important files. If the user is relaxed, the reading unit can also read all files equally. If the user is in a hurry, the reading unit can also prioritize reading files of high urgency. In this way, the reading unit can prioritize reading important files by determining the priority of files to read 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.

[0114] The file reading unit prioritizes reading files that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reading unit will prioritize reading files related to that location. If the user is traveling, the reading unit can also prioritize reading files related to the travel destination. If the user is at home, the reading unit can also prioritize reading files related to home. In this way, the reading unit can prioritize reading highly relevant files by taking the user's geographical location into consideration.

[0115] The information delivery system estimates the user's emotions and adjusts the method of information delivery based on the estimated emotions. For example, if the user is nervous, the system provides a simple and highly visible method of information delivery. If the user is relaxed, the system may also provide a method of information delivery that includes detailed information. If the user is in a hurry, the system may also provide a method of information delivery that gets straight to the point. In this way, the system can provide more appropriate information by adjusting the method of information delivery 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.

[0116] The information provider adjusts the level of detail based on the importance of the information when providing it. For example, the provider provides detailed information on important information so that users can understand it immediately. The provider can also provide general information concisely so that users can obtain only the information they need. The provider can also highlight urgent information to attract the user's attention. In this way, the provider can quickly obtain the information users need by adjusting the level of detail based on the importance of the information.

[0117] The information delivery system estimates the user's emotions and determines the priority of information delivery based on the estimated emotions. For example, if the user is stressed, the system will prioritize providing important information. If the user is relaxed, the system may also provide all information equally. If the user is in a hurry, the system may also prioritize providing information of high urgency. In this way, the system can prioritize providing important information by determining the priority of information delivery 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.

[0118] The information provider selects the most appropriate method of information delivery by considering the user's geographical location. For example, if the user is in a specific location, the provider will prioritize providing information related to that location. If the user is traveling, the provider can also prioritize providing information related to their travel destination. If the user is at home, the provider can also prioritize providing information related to their home. In this way, the provider can select the most appropriate method of information delivery by considering the user's geographical location.

[0119] The execution unit estimates the user's emotions and adjusts the timing of program execution based on the estimated emotions. For example, if the user is stressed, the execution unit will delay program execution and wait until the user is relaxed. If the user is relaxed, the execution unit can also speed up program execution for smoother execution. If the user is in a hurry, the execution unit can also execute the program immediately for rapid execution. In this way, the execution unit can execute the program at a more appropriate time by adjusting the timing of program execution 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.

[0120] The execution unit analyzes the user's past execution history to select the optimal execution method when a program is executed. For example, the execution unit prioritizes the execution of programs that the user has frequently executed in the past. The execution unit can also learn the characteristics of specific programs from the user's execution history and improve execution accuracy. Based on the user's past execution history, the execution unit can also predict and execute programs to be used during specific time periods. In this way, the execution unit can select the optimal execution method and improve execution accuracy by analyzing the user's past execution history.

[0121] The execution unit estimates the user's emotions and determines the priority of program execution based on the estimated emotions. For example, if the user is stressed, the execution unit will prioritize the execution of important programs. If the user is relaxed, the execution unit can also execute all programs equally. If the user is in a hurry, the execution unit can also prioritize the execution of programs with high urgency. In this way, the execution unit can prioritize the execution of important programs by determining the priority of program execution 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.

[0122] The execution unit selects the optimal execution method when executing a program, taking into account the user's geographical location. For example, if the user is in a specific location, the execution unit will prioritize executing programs related to that location. If the user is traveling, the execution unit can also prioritize executing programs related to the travel destination. If the user is at home, the execution unit can also prioritize executing programs related to home. In this way, the execution unit can select the optimal execution method by taking into account the user's geographical location.

[0123] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. In this way, the analysis unit can provide a more appropriate display method by adjusting the display method of the analysis results 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.

[0124] The analysis unit optimizes the current analysis by referring to past analysis data during the analysis process. For example, the analysis unit improves the accuracy of the current analysis based on past analysis data. The analysis unit can also learn specific patterns from past analysis data and apply them to the current analysis. The analysis unit can also improve the efficiency of the current analysis by referring to past analysis data. In this way, the analysis unit can improve the accuracy of the current analysis by referring to past analysis data.

[0125] The analysis unit applies different analysis methods to each data category during analysis. For example, the analysis unit applies natural language processing to text data to improve analysis accuracy. The analysis unit can also apply image recognition technology to image data to improve analysis accuracy. The analysis unit can also apply speech recognition technology to audio data to improve analysis accuracy. In this way, the analysis unit can improve analysis accuracy by applying different analysis methods to each data category.

[0126] The analysis unit estimates the user's emotions and adjusts the importance of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit will prioritize displaying important analysis results. If the user is relaxed, the analysis unit can also display all analysis results equally. If the user is in a hurry, the analysis unit can also prioritize displaying analysis results with high urgency. In this way, the analysis unit can prioritize displaying important analysis results by adjusting the importance of the analysis results 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.

[0127] The analysis unit prioritizes analysis based on the data submission date. For example, the analysis unit prioritizes analyzing the most recent data so that users can access it immediately. The analysis unit can also postpone the analysis of older data so that users can obtain only the information they need. The analysis unit can also highlight and analyze data with high urgency to attract the user's attention. This allows the analysis unit to prioritize the analysis of the most recent data by prioritizing analysis based on the data submission date.

[0128] The analysis unit performs analysis by referencing relevant market data during the analysis process. For example, the analysis unit improves the accuracy of the current data analysis based on the relevant market data. The analysis unit can also learn specific patterns from the relevant market data and apply them to the current data. The analysis unit can also improve the efficiency of the current data by referencing the relevant market data. In this way, the analysis unit can improve the accuracy of the current data analysis by referencing the relevant market data.

[0129] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0130] The reception unit can analyze the user's past voice input history when receiving user voice input and select the optimal voice recognition method. For example, it can prioritize recognizing voice commands that the user has frequently used in the past. The reception unit can also learn specific accents and pronunciation characteristics from the user's voice input history to improve recognition accuracy. Furthermore, the reception unit can predict and recognize voice commands that the user will use at specific times of the day based on their past voice input history. In this way, the reception unit can select the optimal voice recognition method and improve recognition accuracy by analyzing the user's past voice input history.

[0131] The display unit can estimate the user's emotions and adjust the way it presents the content based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. In this way, the display unit can provide a more appropriate display by adjusting the way it presents the content according to the user's emotions.

[0132] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible suggestion. If the user is relaxed, it can provide a suggestion that includes detailed information. Furthermore, if the user is in a hurry, it can provide a suggestion that gets straight to the point. In this way, the suggestion function can provide more appropriate suggestions by adjusting the way suggestions are presented according to the user's emotions.

[0133] The reading unit can estimate the user's emotions and adjust the file loading timing based on those emotions. For example, if the user is stressed, the file loading time can be delayed, waiting until the user is relaxed. If the user is relaxed, the file loading time can be sped up for smoother loading. Furthermore, if the user is in a hurry, the file loading time can be set to instantaneous for quick loading. In this way, the reading unit can load files at a more appropriate time by adjusting the file loading timing according to the user's emotions.

[0134] The information delivery unit can estimate the user's emotions and adjust the method of information delivery based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible method of information delivery. If the user is relaxed, it can provide a method of information delivery that includes detailed information. Furthermore, if the user is in a hurry, it can provide a method of information delivery that gets straight to the point. In this way, the information delivery unit can provide more appropriate information by adjusting the method of information delivery according to the user's emotions.

[0135] The execution unit can analyze the user's past execution history to select the optimal execution method when a program is executed. For example, it can prioritize the execution of programs that the user has frequently run in the past. Furthermore, the execution unit can learn the characteristics of specific programs from the user's execution history to improve execution accuracy. In addition, the execution unit can predict and execute programs to be used during specific time periods based on the user's past execution history. This allows the execution unit to select the optimal execution method and improve execution accuracy by analyzing the user's past execution history.

[0136] The analysis unit can optimize the current analysis by referring to past analysis data during the analysis process. For example, it can improve the accuracy of the current analysis based on past analysis data. Furthermore, the analysis unit can learn specific patterns from past analysis data and apply them to the current analysis. In addition, the analysis unit can improve the efficiency of the current analysis by referring to past analysis data. Thus, the analysis unit can improve the accuracy of the current analysis by referring to past analysis data.

[0137] The reception unit can filter out ambient noise during voice input to remove unwanted sounds. For example, if the user is in a noisy environment, it can filter out ambient noise to remove unwanted sounds from the voice input. Conversely, if the user is in a quiet environment, it can also remove even subtle noises to improve the accuracy of the voice input. Furthermore, if the user is on the move when using voice input, it can filter out wind noise and vehicle noise to remove unwanted sounds. In this way, the reception unit can improve the accuracy of voice input by removing noise during the process.

[0138] The display unit can adjust the level of detail based on the importance of the information being displayed. For example, important information can be displayed in detail so that the user can understand it immediately. General information can be displayed concisely so that the user can obtain only the information they need. Furthermore, information of high urgency can be highlighted to attract the user's attention. In this way, the display unit can adjust the level of detail based on the importance of the information, allowing the user to quickly obtain the information they need.

[0139] The proposal department can apply different proposal algorithms depending on the product category. For example, electronic products can be proposed using an algorithm that emphasizes specification comparisons. Fashion products can be proposed using an algorithm that emphasizes design and trends. Furthermore, food products can be proposed using an algorithm that emphasizes nutritional value and expiration date. By applying different proposal algorithms depending on the product category, the proposal department makes it easier for users to understand the products.

[0140] The following briefly describes the processing flow for example form 2.

[0141] Step 1: The reception desk accepts voice input. For example, it can accept voice input using a microphone or the voice recognition function of a smartphone. It can also convert the content of the voice input into text data using voice recognition technology. Step 2: The display unit displays the information received by the reception unit on the screen. For example, it can display voice-input information on the screen for visual confirmation, and the display layout can be adjusted according to the type of information. Step 3: The proposal department makes the best proposal based on the information received by the reception department. For example, they can make the best proposal based on product specifications, web information, and thought processes, or they can make proposals based on the user's past behavior history and current situation. Step 4: The loading section loads the necessary files. For example, it loads necessary files such as text files, image files, and audio files, and can adjust the loading method according to the file type. Step 5: The providing unit organizes and provides the information read by the reading unit. For example, it can classify and provide the information, and adjust the order in which the information is provided. Step 6: The execution unit runs the program as needed. For example, it can run programs such as data analysis or graph creation, or execute a program when certain conditions are met. Step 7: The analysis unit uses a large-scale language model to handle complex questions. For example, it can handle complex questions such as technical questions or legal questions and provide appropriate answers to user inquiries.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the reception unit, display unit, proposal unit, reading unit, provision unit, execution unit, and analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 38B of the smart device 14 and converts the voice input into text data using the control unit 46A. The display unit visually displays the information using the display 40A of the smart device 14. The proposal unit makes optimal suggestions based on product specifications and web information using the specific processing unit 290 of the data processing unit 12. The reading unit reads the necessary files using the storage 50 of the smart device 14. The provision unit organizes and provides the information using the specific processing unit 290 of the data processing unit 12. The execution unit executes the program using the specific processing unit 290 of the data processing unit 12. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to handle complex questions using a large-scale language model. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0147] 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The 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.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] 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.

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

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

[0156] In 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.

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

[0158] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0160] The data processing system 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.

[0161] Each of the multiple elements described above, including the reception unit, display unit, proposal unit, reading unit, provision unit, execution unit, and analysis unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 238 of the smart glasses 214 and converts the voice input into text data using the control unit 46A. The display unit visually displays the information using the display of the smart glasses 214. The proposal unit makes optimal suggestions based on product specifications and web information using the specific processing unit 290 of the data processing unit 12. The reading unit reads the necessary files using the storage 50 of the smart glasses 214. The provision unit organizes and provides the information using the specific processing unit 290 of the data processing unit 12. The execution unit executes the program using the specific processing unit 290 of the data processing unit 12. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to handle complex questions using a large-scale language model. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the reception unit, display unit, proposal unit, reading unit, provision unit, execution unit, and analysis unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 238 of the headset terminal 314 and converts the voice input into text data by the control unit 46A. The display unit visually displays the information using the display 343 of the headset terminal 314. The proposal unit makes optimal suggestions based on product specifications and web information using the specific processing unit 290 of the data processing unit 12. The reading unit reads the necessary files using the storage 50 of the headset terminal 314. The provision unit organizes and provides the information using the specific processing unit 290 of the data processing unit 12. The execution unit executes the program using the specific processing unit 290 of the data processing unit 12. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to handle complex questions using a large-scale language model. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] Each of the multiple elements described above, including the reception unit, display unit, proposal unit, reading unit, provision unit, execution unit, and analysis unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 238 of the robot 414 and converts the voice input into text data by the control unit 46A. The display unit visually displays the information using the display of the robot 414. The proposal unit makes optimal suggestions based on product specifications and web information using the specific processing unit 290 of the data processing unit 12. The reading unit reads the necessary files using the storage 50 of the robot 414. The provision unit organizes and provides the information using the specific processing unit 290 of the data processing unit 12. The execution unit executes the program using the specific processing unit 290 of the data processing unit 12. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to respond to complex questions using a large-scale language model. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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."

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] (Note 1) A reception desk that accepts voice input, A display unit that displays the information received by the reception unit on the screen, A proposal department that makes the most suitable proposal based on the information received by the aforementioned reception department, A reading unit that reads the necessary files, A providing unit that organizes and provides the information read by the reading unit, An execution unit that runs the program as needed, It includes an analysis unit that can handle complex questions. A system characterized by the following features. (Note 2) The aforementioned reception unit is Accepts voice input The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is The information received by the reception unit is displayed on the screen. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We provide optimal proposals based on product specifications, web information, and thought processes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reading unit, Load the necessary files The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The information read by the aforementioned reading unit is organized and provided. The system described in Appendix 1, characterized by the features described herein. (Note 7) The execution unit is, Run the program as needed. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Using large-scale language models to handle complex questions The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system analyzes the user's past voice input history and selects the optimal voice recognition method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When using voice input, the system filters out the user's current ambient noise to remove unwanted sounds. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When using voice input, the system prioritizes accepting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is During voice input, the system analyzes the user's social media activity and accepts relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned display unit is It estimates the user's emotions and adjusts the way the displayed content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned display unit is When displaying information, 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 17) The aforementioned display unit is When displaying information, different display algorithms are applied depending on the category of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is It estimates the user's emotions and adjusts the length of the displayed content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is When displaying information, the display priority is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is When displaying information, adjust the display order based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the products are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reading unit, It estimates the user's emotions and adjusts the file loading timing based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reading unit, Analyze the user's past file loading history and select the optimal loading method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reading unit, When loading a file, the system filters out the user's current ambient noise to remove it. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reading unit, It estimates the user's emotions and determines the priority of files to load based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reading unit, When loading files, the system prioritizes loading files that are more relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing information, 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 34) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing information, the optimal method of information delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The execution unit is, It estimates the user's emotions and adjusts the timing of program execution based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The execution unit is, When a program is executed, the system analyzes the user's past execution history to select the optimal execution method. The system described in Appendix 1, characterized by the features described herein. (Note 38) The execution unit is, The program estimates the user's emotions and determines the priority of program execution based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The execution unit is, When the program is executed, the optimal execution method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned analysis unit, During analysis, past analysis data is referenced to optimize the current analysis. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned analysis unit, During analysis, different analytical methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned analysis unit, It estimates the user's emotions and adjusts the importance of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned analysis unit, During the analysis, the analysis is performed by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0214] 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 accepts voice input, A display unit that displays the information received by the reception unit on the screen, A proposal department that makes the most suitable proposal based on the information received by the aforementioned reception department, A reading unit that reads the necessary files, A providing unit that organizes and provides the information read by the reading unit, An execution unit that runs the program as needed, It includes an analysis unit that can handle complex questions. A system characterized by the following features.

2. The aforementioned reception unit is Accepts voice input The system according to feature 1.

3. The aforementioned display unit is The information received by the reception unit is displayed on the screen. The system according to feature 1.

4. The aforementioned proposal section is, We provide optimal proposals based on product specifications, web information, and thought processes. The system according to feature 1.

5. The aforementioned reading unit, Load the necessary files The system according to feature 1.

6. The aforementioned supply unit is, The information read by the aforementioned reading unit is organized and provided. The system according to feature 1.

7. The execution unit is, Run the program as needed. The system according to feature 1.

8. The aforementioned analysis unit, Using large-scale language models to handle complex questions The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is The system analyzes the user's past voice input history and selects the optimal voice recognition method. The system according to feature 1.

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