system

Abstract

The system according to this embodiment aims to enable users to communicate efficiently with multiple specialized departments and to quickly gather necessary information. [Solution] The system according to this embodiment comprises a reception unit, an inquiry unit, an integration unit, a provision unit, and a dialogue unit. The reception unit receives questions from users. The inquiry unit analyzes the questions received by the reception unit and makes inquiries to the relevant specialized departments. The integration unit integrates the responses collected by the inquiry unit. The provision unit provides the information integrated by the integration unit to the user. The dialogue unit communicates in natural language using a conversational AI.

Description

Technical Field

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to efficiently communicate with a plurality of specialized departments and difficult to quickly aggregate necessary information.

[0005] The system according to the embodiment aims to enable a user to efficiently communicate with a plurality of specialized departments and quickly aggregate necessary information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an inquiry unit, an integration unit, a provision unit, and a dialogue unit. The reception unit receives questions from users. The inquiry unit analyzes the questions received by the reception unit and makes inquiries to the relevant specialized departments. The integration unit integrates the responses collected by the inquiry unit. The provision unit provides the information integrated by the integration unit to the user. The dialogue unit communicates in natural language using a conversational AI. [Effects of the Invention]

[0007] The system according to this embodiment allows users to communicate efficiently with multiple specialized departments and quickly gather necessary information. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages 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 Project Assistant AI System according to an embodiment of the present invention is a system that utilizes generative AI to efficiently communicate with multiple specialized departments and quickly aggregate necessary information. This system provides accurate answers in real time to questions in each specialized field. The Project Assistant AI System automatically inquires with relevant departments and integrates the necessary information. The Project Assistant AI System interacts with the user in a chatbot format and provides a user interface that allows for easy retrieval of information. The Project Assistant AI System enables natural language communication through conversational AI. For example, when a user inputs a question, the conversational AI understands the question through natural language communication and automatically inquires with the relevant specialized departments. Next, it collects responses from each specialized department and integrates the necessary information. Finally, it provides information to the user in a chatbot format and provides a user interface that allows for easy retrieval. This mechanism allows for automatic collaboration with various departments with internal expertise (legal, security, creative checks, accounting, budget, etc.) and enables quick and accurate answers to questions from project managers. This streamlines the process of confirming information with each specialized department and improves the speed of the entire planning process. For example, if a user asks, "Please tell me about the budget for the new project," the conversational AI understands the question and automatically inquires with the accounting department. The project assistant AI system collects responses from the accounting department, integrates the necessary information, and answers the user with a statement such as, "The budget for the new project is ¥XX." Targeting personnel in any company, this project assistant AI system reduces the time and effort required to coordinate with multiple specialized departments and prevents information delays and omissions. By using generative AI, it enables the creation of expertise-based data, autonomous communication, and a conversational interface, strengthening collaboration across the entire company and eliminating information delays and omissions. This allows the project assistant AI system to efficiently receive, analyze, inquire about, integrate, and deliver user questions.

[0029] The project assistant AI system according to this embodiment comprises a reception unit, an inquiry unit, an integration unit, a provision unit, and a dialogue unit. The reception unit receives questions from users. Questions from users include, but are not limited to, text format, voice format, and image format. The reception unit directly receives questions in text format, for example. The reception unit can also convert voice format questions into text format using speech recognition technology and accept them. Furthermore, the reception unit can analyze image format questions using image recognition technology, convert them into text format, and accept them. For example, the reception unit analyzes an image taken by the user with a smartphone and extracts the content of the question. The inquiry unit analyzes the questions received by the reception unit and makes inquiries to the relevant specialized departments. The inquiry unit analyzes the content of the questions using, for example, natural language processing technology and identifies the relevant specialized departments. Furthermore, the inquiry unit can analyze the content of the questions using machine learning algorithms and identify the relevant specialized departments. Furthermore, the inquiry unit automatically makes inquiries to the relevant specialized departments based on the content of the questions. For example, the inquiry unit analyzes the user's question and makes an inquiry to the legal department. The Integration Unit integrates the responses collected by the Inquiry Unit. The Integration Unit integrates responses using, for example, a data merging method. The Integration Unit can also integrate responses using a method for handling duplicate data. Furthermore, the Integration Unit integrates responses while considering their relevance. For example, the Integration Unit integrates responses from multiple specialized departments and eliminates duplicate information. The Delivery Unit provides the information integrated by the Integration Unit to the user. The Delivery Unit provides information in, for example, text format. The Delivery Unit can also provide information in audio format. Furthermore, the Delivery Unit can also provide information using visual display methods. For example, the Delivery Unit displays the integrated information as charts or graphs. The Dialogue Unit communicates in natural language using conversational AI. The Dialogue Unit interacts with users using, for example, a chatbot. The Dialogue Unit can also interact with users using a voice assistant. Furthermore, the Dialogue Unit communicates in natural language using text generation AI (e.g., LLM).For example, the dialogue unit uses generative AI to generate answers in natural language to user questions. This enables the project assistant AI system according to the embodiment to efficiently receive, analyze, query, integrate, and provide user questions.

[0030] The reception desk receives questions from users. These questions may include, but are not limited to, questions in text, audio, or image formats. For example, the reception desk can directly accept questions in text format. It can also convert audio questions into text format using speech recognition technology. Specifically, a deep learning-based speech recognition model is used. This model achieves high-precision speech recognition by learning from a large amount of audio data. For example, when a user inputs a question via voice through a smartphone or microphone, the speech recognition model analyzes the audio and converts it into text format. Furthermore, the reception desk can analyze questions in image format using image recognition technology and convert them into text format. A convolutional neural network (CNN) is used as the image recognition technology. For example, when a user uploads an image taken with a smartphone, the CNN analyzes the image and recognizes text and objects within it. This allows the question content to be extracted from the image and converted into text format. By combining these technologies, the reception desk can efficiently receive questions in various formats from users. Furthermore, the reception desk provides an environment that makes it easy for users to input questions through its user interface. For example, it includes text input fields, voice input buttons, and image upload buttons to allow users to operate it intuitively. This enables the reception desk to receive questions from users quickly and accurately.

[0031] The inquiry department analyzes questions received by the reception department and contacts the relevant specialized departments. For example, the inquiry department uses natural language processing (NLP) techniques to analyze the question content and identify the relevant specialized department. Specifically, topic modeling and text classification algorithms are used as NLP techniques. For instance, if a user's question concerns legal matters, topic modeling identifies the question as legal and contacts the legal department. The inquiry department can also use machine learning algorithms to analyze the question content and identify the relevant specialized department. For example, algorithms such as support vector machines (SVM) and random forests are used to classify the question content and identify the appropriate specialized department. Furthermore, the inquiry department automatically contacts the relevant specialized departments based on the question content. For example, the inquiry department analyzes a user's question and contacts the legal department. The inquiry department provides an interface to the specialized departments to appropriately communicate the question content and obtain a prompt response. For example, it provides an API to send the question content in text format to the specialized department and receive a response. This allows the inquiry department to efficiently analyze user questions and contact the appropriate specialized departments.

[0032] The integration unit integrates the responses collected by the inquiry unit. The integration unit integrates responses using, for example, data merging methods. Specifically, it integrates multiple responses into a single dataset using database join operations or data frame merge operations. The integration unit can also integrate responses using methods for handling duplicate data. For example, it uses algorithms to detect and eliminate duplicate information. Furthermore, the integration unit considers the relevance of the responses during integration. For example, it analyzes the content of the responses and prioritizes integrating the most relevant information. Using these techniques, the integration unit efficiently integrates responses from multiple specialized departments to generate consistent information for the user. For example, it integrates responses from the legal and technical departments to provide a comprehensive answer to the user. This allows the integration unit to provide users with consistent information and comprehensive answers to their questions.

[0033] The information provider unit provides the user with information integrated by the information integration unit. The information provider unit provides information in, for example, text format. Specifically, it displays the integrated information to the user as text. The information provider unit can also provide information in audio format. For example, it can use speech synthesis technology to generate the integrated information as audio and provide it to the user. Furthermore, the information provider unit can provide information using visual display methods. For example, it can display the integrated information as charts or graphs, providing it in a way that is easy for the user to understand visually. Using these technologies, the information provider unit provides information to the user in various formats, improving user convenience. For example, if the user prefers a text response, the information is provided as text; if the user prefers an audio response, the information is provided as audio. This allows the information provider unit to provide information flexibly according to the user's needs.

[0034] The dialogue unit communicates in natural language using conversational AI. For example, the dialogue unit interacts with users using a chatbot. Specifically, the chatbot analyzes user input using natural language processing technology and generates appropriate responses. The dialogue unit can also interact with users using a voice assistant. For example, it combines speech recognition and speech synthesis technologies to analyze user voice input and generate voice responses. Furthermore, the dialogue unit communicates in natural language using text generation AI (e.g., LLM). Specifically, text generation AI possesses advanced natural language generation capabilities by learning from large amounts of text data. For example, the text generation AI generates appropriate answers to user questions, enabling natural dialogue. Using these technologies, the dialogue unit can achieve natural communication with users and respond quickly and appropriately to user questions. For example, if a user asks about project progress, the dialogue unit uses the generation AI to generate detailed answers regarding the progress and provides them to the user. This enables smooth communication with users and supports the efficient progress of projects.

[0035] The reception desk can analyze a user's past question history and select the optimal reception method. For example, the reception desk can automatically display as suggestions the type of question the user has frequently asked in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest the type of question the user will use at a specific time of day based on their past question history. For example, the reception desk can analyze a user's past question history using data mining techniques to select the optimal reception method. This enables efficient question reception by selecting the optimal reception method based on the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0036] The reception desk can filter questions based on the user's current projects and areas of interest. For example, it can prioritize questions related to projects the user is currently working on. It can also filter and receive relevant questions based on the user's areas of interest. Furthermore, it can prioritize relevant questions based on areas the user has shown interest in in the past. For example, it can use data from the user's project management tool to filter questions related to the current project. This allows for the priority of receiving highly relevant questions by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0037] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving questions related to that region. The reception desk can also filter and receive relevant questions based on the user's current location. Furthermore, if the user is on the move, the reception desk can prioritize receiving the most relevant questions based on their current location. For example, the reception desk can use the user's GPS data to filter questions related to their current location. This allows the reception desk to prioritize receiving highly relevant questions by taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without using AI.

[0038] The reception desk can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can accept questions based on topics the user has shown interest in on social media. The reception desk can also filter and accept relevant questions from the user's social media activity. Furthermore, the reception desk can prioritize accepting questions based on topics the user frequently mentions on social media. For example, the reception desk can analyze the content of the user's social media posts to identify relevant questions. This allows for the prioritization of relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0039] The inquiry unit can adjust the level of detail of an inquiry based on the importance of the question. For example, the inquiry unit will make detailed inquiries for high-importance questions. It can also make concise inquiries for low-importance questions. Furthermore, the inquiry unit can dynamically adjust the level of detail of an inquiry according to the importance of the question. For example, the inquiry unit can assess the urgency of the user's question and make detailed inquiries for high-importance questions. This allows for efficient inquiries by adjusting the level of detail of the inquiry according to the importance of the question. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or without using AI.

[0040] The inquiry unit can apply different inquiry algorithms depending on the category of the question when an inquiry is made. For example, the inquiry unit can apply a legal inquiry algorithm to legal-related questions. It can also apply a security-specific inquiry algorithm to security-related questions. Furthermore, it can apply a creative-specific inquiry algorithm to creative-related questions. For example, the inquiry unit analyzes the category of the user's question and selects the most suitable inquiry algorithm. This enables efficient inquiries by applying the most suitable inquiry algorithm according to the category of the question. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or without using AI.

[0041] The inquiry department can determine the priority of inquiries based on when the question is submitted. For example, the inquiry department can determine the priority based on when the question was submitted. The inquiry department can also dynamically adjust the priority of inquiries according to when the question was submitted. Furthermore, the inquiry department can prioritize inquiries that require a quick response based on when the question was submitted. For example, the inquiry department can record the date and time when a user submits a question and set a priority based on the submission date. This enables a quick response by determining the priority of inquiries based on when the question was submitted. Some or all of the above processing in the inquiry department may be performed using AI, for example, or not using AI.

[0042] The inquiry unit can adjust the order of inquiries based on the relevance of the questions when an inquiry is made. For example, the inquiry unit determines the order of inquiries based on the relevance of the questions. The inquiry unit can also dynamically adjust the order of inquiries according to the relevance of the questions. Furthermore, the inquiry unit can prioritize important questions based on their relevance. For example, the inquiry unit evaluates the similarity of the user's questions and prioritizes questions with high relevance. This allows important questions to be prioritized by adjusting the order of inquiries based on their relevance. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or without using AI.

[0043] The integration unit can improve the accuracy of the integration based on the interrelationships of responses from each specialized department during the integration process. For example, the integration unit can analyze the interrelationships of responses from each specialized department to improve the accuracy of the integration. The integration unit can also improve the accuracy of the integration by considering the relationships between responses from each specialized department. Furthermore, the integration unit can select the optimal integration method based on the interrelationships of responses from each specialized department. For example, the integration unit can analyze responses from multiple specialized departments and prioritize the integration of highly relevant information. This improves the accuracy of the integration by considering the interrelationships of responses from each specialized department. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without using AI.

[0044] The integration unit can perform integration while considering the attribute information of each specialized department. For example, the integration unit can select the optimal integration method based on the attribute information of each specialized department. The integration unit can also improve the accuracy of the integration by considering the attribute information of each specialized department. Furthermore, the integration unit can prioritize the integration of highly relevant information based on the attribute information of each specialized department. For example, the integration unit integrates information while considering the role and area of ​​expertise of each specialized department. This improves the accuracy of the integration by considering the attribute information of each specialized department. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0045] The integration unit can perform integration while considering the geographical distribution of each specialized department. For example, the integration unit can select the optimal integration method based on the geographical distribution of each specialized department. The integration unit can also improve the accuracy of the integration by considering the geographical distribution of each specialized department. Furthermore, the integration unit can prioritize the integration of highly relevant information based on the geographical distribution of each specialized department. For example, the integration unit uses the location information of each specialized department to integrate geographically relevant information. This improves the accuracy of the integration by considering the geographical distribution of each specialized department. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0046] The integration unit can improve the accuracy of the integration by referring to relevant literature during the integration process. For example, the integration unit can improve the accuracy of the integration by referring to relevant literature. The integration unit can also select the optimal integration method based on the information in the relevant literature. Furthermore, the integration unit can also prioritize the integration of highly relevant information by referring to relevant literature. For example, the integration unit can refer to academic papers and technical reports and evaluate the degree of information consistency. This improves the accuracy of the integration by referring to relevant literature. Some or all of the above processes in the integration unit may be performed using AI, for example, or without using AI.

[0047] The information delivery unit can select the optimal delivery method by referring to the user's past question history when providing information. For example, the delivery unit can select the optimal delivery method based on the content of questions the user has frequently asked in the past. The delivery unit can also prioritize providing relevant information based on the user's past question history. Furthermore, the delivery unit can analyze the user's past question history and select the most efficient delivery method. For example, the delivery unit can retrieve the user's past question history from a database and analyze the historical data. This enables efficient information delivery by selecting the optimal delivery method based on the user's past question history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0048] The information provider can customize the information provided based on the user's current projects and areas of interest. For example, the provider may prioritize providing information related to the user's current projects. It can also customize and provide relevant information based on the user's areas of interest. Furthermore, it can prioritize providing relevant information based on areas the user has shown interest in in the past. For example, the provider may use data from the user's project management tool to provide information related to the current project. This allows for the provision of highly relevant information by customizing it based on the user's current projects and areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or not.

[0049] The information provider can select the optimal method of providing information by considering the user's geographical location. For example, if the user is in a specific region, the provider will prioritize providing information related to that region. The provider can also customize and provide relevant information based on the user's current location. Furthermore, if the user is on the move, the provider can prioritize providing the most relevant information based on their current location. For example, the provider can use the user's GPS data to provide information related to their current location. This allows for the provision of highly relevant information by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI.

[0050] The information provider can analyze the user's social media activity and customize the content provided when delivering information. For example, the provider can provide information based on topics the user has shown interest in on social media. The provider can also customize and provide relevant information based on the user's social media activity. Furthermore, the provider can prioritize providing information based on topics the user frequently mentions on social media. For example, the provider can analyze the content of the user's social media posts and identify relevant information. This allows the provider to provide highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI.

[0051] The dialogue unit can select the optimal dialogue method during a conversation by referring to the user's past dialogue history. For example, the dialogue unit can select the optimal dialogue method based on the dialogue methods the user has frequently used in the past. The dialogue unit can also prioritize providing relevant dialogue methods based on the user's past dialogue history. Furthermore, the dialogue unit can analyze the user's past dialogue history and select the most efficient dialogue method. For example, the dialogue unit can retrieve the user's past dialogue history from a database and analyze the historical data. This enables efficient dialogue by selecting the optimal dialogue method based on the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI.

[0052] The dialogue unit can customize the content of conversations based on the user's current projects and areas of interest. For example, the dialogue unit can prioritize providing dialogue related to the user's current ongoing projects. It can also customize and provide relevant dialogue based on the user's areas of interest. Furthermore, it can prioritize providing relevant dialogue based on areas the user has shown interest in in the past. For example, the dialogue unit can use data from the user's project management tool to provide dialogue related to the current project. This allows for highly relevant conversations by customizing the dialogue based on the user's current projects and areas of interest. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI.

[0053] The dialogue unit can select the optimal dialogue method during a conversation, taking into account the user's geographical location. For example, if the user is in a specific region, the dialogue unit will prioritize providing dialogue content relevant to that region. The dialogue unit can also customize and provide relevant dialogue content based on the user's current location. Furthermore, if the user is on the move, the dialogue unit can prioritize providing the most appropriate dialogue content based on their current location. For example, the dialogue unit can use the user's GPS data to provide dialogue content relevant to their current location. This enables highly relevant dialogue by considering the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI.

[0054] The dialogue unit can analyze the user's social media activity during a conversation and customize the conversation content accordingly. For example, the dialogue unit can provide conversation content based on topics the user has shown interest in on social media. It can also customize and provide relevant conversation content based on the user's social media activity. Furthermore, the dialogue unit can prioritize providing conversation content based on topics the user frequently mentions on social media. For example, the dialogue unit can analyze the user's social media posts and identify relevant conversation content. This enables highly relevant conversations by analyzing the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The project assistant AI system can also be equipped with user schedule management capabilities. This feature retrieves the user's calendar information and optimizes the timing of question submissions and inquiries. For example, if a user is in a meeting, the system will accept questions after the meeting ends. Similarly, if a user is on vacation, less important questions can be accepted after their vacation. Furthermore, the schedule management feature can suggest the optimal timing for providing answers based on the user's schedule. This allows for flexible responses tailored to the user's schedule.

[0057] The project assistant AI system can analyze a user's past question history and automatically suggest answers to similar questions. For example, if a user previously asked about "the budget for a new project," the system will use that answer as a reference to suggest answers to new questions. It can also automatically generate related questions based on keywords the user has used in the past. Furthermore, it can prioritize displaying frequently asked questions based on the user's past question history. This enables efficient question handling by leveraging the user's past question history.

[0058] The project assistant AI system can prioritize providing relevant information based on the user's current project progress. For example, if the user is in the early stages of a project, the system will prioritize providing information on planning and budgeting. If the user is in the middle stages of the project, it can also provide information on progress management and resource allocation. Furthermore, if the user is in the final stages of the project, it can provide information on deliverable review and final reporting. This enables the provision of appropriate information according to the project's progress.

[0059] The project assistant AI system can provide region-specific information by taking into account the user's geographical location. For example, if the user is in a specific region, it will prioritize providing information on laws, regulations, and market conditions relevant to that region. If the user is on a business trip abroad, it can also provide information on local business etiquette and culture. Furthermore, if the user is on the move, it can provide optimal information based on their current location. This enables the provision of appropriate information that takes the user's geographical location into consideration.

[0060] The project assistant AI system can analyze a user's social media activity and provide relevant information. For example, it can provide information based on topics the user has shown interest in on social media. It can also automatically generate relevant questions from the user's social media activity. Furthermore, it can prioritize information based on topics the user frequently mentions on social media. This enables the provision of appropriate information by leveraging the user's social media activity.

[0061] The project assistant AI system can adjust the level of detail in its answers based on the importance of the user's questions. For example, it can provide detailed answers to high-priority questions and concise answers to low-priority questions. Furthermore, it can dynamically adjust the level of detail in its answers according to the importance of the questions. This enables efficient answer provision tailored to the importance of each question.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives questions from users. These questions can be in text, audio, or image formats. The reception desk can directly accept text questions, and can also convert audio questions into text using speech recognition technology. It can also analyze image questions using image recognition technology and convert them into text for acceptance. Step 2: The inquiry department analyzes the questions received by the reception department and contacts the relevant specialist departments. The inquiry department uses natural language processing technology and machine learning algorithms to analyze the content of the questions and identify the relevant specialist departments. Furthermore, it automatically contacts the relevant specialist departments based on the content of the questions. Step 3: The Integration Department integrates the responses collected by the Inquiry Department. The Integration Department integrates the responses using data merging methods and duplicate data handling methods, taking into account the relevance of the responses. For example, it integrates responses from multiple specialized departments and eliminates duplicate information. Step 4: The delivery unit provides the user with the information integrated by the integration unit. The delivery unit can provide the information using text format, audio format, or visual display methods (such as charts and graphs). Step 5: The dialogue unit communicates in natural language using conversational AI. The dialogue unit interacts with the user using chatbots, voice assistants, and text generation AI (e.g., LLM) to generate responses in natural language.

[0064] (Example of form 2) The Project Assistant AI System according to an embodiment of the present invention is a system that utilizes generative AI to efficiently communicate with multiple specialized departments and quickly aggregate necessary information. This system provides accurate answers in real time to questions in each specialized field. The Project Assistant AI System automatically inquires with relevant departments and integrates the necessary information. The Project Assistant AI System interacts with the user in a chatbot format and provides a user interface that allows for easy retrieval of information. The Project Assistant AI System enables natural language communication through conversational AI. For example, when a user inputs a question, the conversational AI understands the question through natural language communication and automatically inquires with the relevant specialized departments. Next, it collects responses from each specialized department and integrates the necessary information. Finally, it provides information to the user in a chatbot format and provides a user interface that allows for easy retrieval. This mechanism allows for automatic collaboration with various departments with internal expertise (legal, security, creative checks, accounting, budget, etc.) and enables quick and accurate answers to questions from project managers. This streamlines the process of confirming information with each specialized department and improves the speed of the entire planning process. For example, if a user asks, "Please tell me about the budget for the new project," the conversational AI understands the question and automatically inquires with the accounting department. The project assistant AI system collects responses from the accounting department, integrates the necessary information, and answers the user with a statement such as, "The budget for the new project is ¥XX." Targeting personnel in any company, this project assistant AI system reduces the time and effort required to coordinate with multiple specialized departments and prevents information delays and omissions. By using generative AI, it enables the creation of expertise-based data, autonomous communication, and a conversational interface, strengthening collaboration across the entire company and eliminating information delays and omissions. This allows the project assistant AI system to efficiently receive, analyze, inquire about, integrate, and deliver user questions.

[0065] The project assistant AI system according to this embodiment comprises a reception unit, an inquiry unit, an integration unit, a provision unit, and a dialogue unit. The reception unit receives questions from users. Questions from users include, but are not limited to, text format, voice format, and image format. The reception unit directly receives questions in text format, for example. The reception unit can also convert voice format questions into text format using speech recognition technology and accept them. Furthermore, the reception unit can analyze image format questions using image recognition technology, convert them into text format, and accept them. For example, the reception unit analyzes an image taken by the user with a smartphone and extracts the content of the question. The inquiry unit analyzes the questions received by the reception unit and makes inquiries to the relevant specialized departments. The inquiry unit analyzes the content of the questions using, for example, natural language processing technology and identifies the relevant specialized departments. Furthermore, the inquiry unit can analyze the content of the questions using machine learning algorithms and identify the relevant specialized departments. Furthermore, the inquiry unit automatically makes inquiries to the relevant specialized departments based on the content of the questions. For example, the inquiry unit analyzes the user's question and makes an inquiry to the legal department. The Integration Unit integrates the responses collected by the Inquiry Unit. The Integration Unit integrates responses using, for example, a data merging method. The Integration Unit can also integrate responses using a method for handling duplicate data. Furthermore, the Integration Unit integrates responses while considering their relevance. For example, the Integration Unit integrates responses from multiple specialized departments and eliminates duplicate information. The Delivery Unit provides the information integrated by the Integration Unit to the user. The Delivery Unit provides information in, for example, text format. The Delivery Unit can also provide information in audio format. Furthermore, the Delivery Unit can also provide information using visual display methods. For example, the Delivery Unit displays the integrated information as charts or graphs. The Dialogue Unit communicates in natural language using conversational AI. The Dialogue Unit interacts with users using, for example, a chatbot. The Dialogue Unit can also interact with users using a voice assistant. Furthermore, the Dialogue Unit communicates in natural language using text generation AI (e.g., LLM).For example, the dialogue unit uses generative AI to generate answers in natural language to user questions. This enables the project assistant AI system according to the embodiment to efficiently receive, analyze, query, integrate, and provide user questions.

[0066] The reception desk receives questions from users. These questions may include, but are not limited to, questions in text, audio, or image formats. For example, the reception desk can directly accept questions in text format. It can also convert audio questions into text format using speech recognition technology. Specifically, a deep learning-based speech recognition model is used. This model achieves high-precision speech recognition by learning from a large amount of audio data. For example, when a user inputs a question via voice through a smartphone or microphone, the speech recognition model analyzes the audio and converts it into text format. Furthermore, the reception desk can analyze questions in image format using image recognition technology and convert them into text format. A convolutional neural network (CNN) is used as the image recognition technology. For example, when a user uploads an image taken with a smartphone, the CNN analyzes the image and recognizes text and objects within it. This allows the question content to be extracted from the image and converted into text format. By combining these technologies, the reception desk can efficiently receive questions in various formats from users. Furthermore, the reception desk provides an environment that makes it easy for users to input questions through its user interface. For example, it includes text input fields, voice input buttons, and image upload buttons to allow users to operate it intuitively. This enables the reception desk to receive questions from users quickly and accurately.

[0067] The inquiry department analyzes questions received by the reception department and contacts the relevant specialized departments. For example, the inquiry department uses natural language processing (NLP) techniques to analyze the question content and identify the relevant specialized department. Specifically, topic modeling and text classification algorithms are used as NLP techniques. For instance, if a user's question concerns legal matters, topic modeling identifies the question as legal and contacts the legal department. The inquiry department can also use machine learning algorithms to analyze the question content and identify the relevant specialized department. For example, algorithms such as support vector machines (SVM) and random forests are used to classify the question content and identify the appropriate specialized department. Furthermore, the inquiry department automatically contacts the relevant specialized departments based on the question content. For example, the inquiry department analyzes a user's question and contacts the legal department. The inquiry department provides an interface to the specialized departments to appropriately communicate the question content and obtain a prompt response. For example, it provides an API to send the question content in text format to the specialized department and receive a response. This allows the inquiry department to efficiently analyze user questions and contact the appropriate specialized departments.

[0068] The integration unit integrates the responses collected by the inquiry unit. The integration unit integrates responses using, for example, data merging methods. Specifically, it integrates multiple responses into a single dataset using database join operations or data frame merge operations. The integration unit can also integrate responses using methods for handling duplicate data. For example, it uses algorithms to detect and eliminate duplicate information. Furthermore, the integration unit considers the relevance of the responses during integration. For example, it analyzes the content of the responses and prioritizes integrating the most relevant information. Using these techniques, the integration unit efficiently integrates responses from multiple specialized departments to generate consistent information for the user. For example, it integrates responses from the legal and technical departments to provide a comprehensive answer to the user. This allows the integration unit to provide users with consistent information and comprehensive answers to their questions.

[0069] The information provider unit provides the user with information integrated by the information integration unit. The information provider unit provides information in, for example, text format. Specifically, it displays the integrated information to the user as text. The information provider unit can also provide information in audio format. For example, it can use speech synthesis technology to generate the integrated information as audio and provide it to the user. Furthermore, the information provider unit can provide information using visual display methods. For example, it can display the integrated information as charts or graphs, providing it in a way that is easy for the user to understand visually. Using these technologies, the information provider unit provides information to the user in various formats, improving user convenience. For example, if the user prefers a text response, the information is provided as text; if the user prefers an audio response, the information is provided as audio. This allows the information provider unit to provide information flexibly according to the user's needs.

[0070] The dialogue unit communicates in natural language using conversational AI. For example, the dialogue unit interacts with users using a chatbot. Specifically, the chatbot analyzes user input using natural language processing technology and generates appropriate responses. The dialogue unit can also interact with users using a voice assistant. For example, it combines speech recognition and speech synthesis technologies to analyze user voice input and generate voice responses. Furthermore, the dialogue unit communicates in natural language using text generation AI (e.g., LLM). Specifically, text generation AI possesses advanced natural language generation capabilities by learning from large amounts of text data. For example, the text generation AI generates appropriate answers to user questions, enabling natural dialogue. Using these technologies, the dialogue unit can achieve natural communication with users and respond quickly and appropriately to user questions. For example, if a user asks about project progress, the dialogue unit uses the generation AI to generate detailed answers regarding the progress and provides them to the user. This enables smooth communication with users and supports the efficient progress of projects.

[0071] The reception system can estimate the user's emotions and adjust the way questions are answered based on those emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize the input steps. If the user is relaxed, the reception system can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to allow for quick question entry. For example, the reception system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate question answering by adjusting the way questions are answered according to the user's emotions.

[0072] The reception desk can analyze a user's past question history and select the optimal reception method. For example, the reception desk can automatically display as suggestions the type of question the user has frequently asked in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest the type of question the user will use at a specific time of day based on their past question history. For example, the reception desk can analyze a user's past question history using data mining techniques to select the optimal reception method. This enables efficient question reception by selecting the optimal reception method based on the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0073] The reception desk can filter questions based on the user's current projects and areas of interest. For example, it can prioritize questions related to projects the user is currently working on. It can also filter and receive relevant questions based on the user's areas of interest. Furthermore, it can prioritize relevant questions based on areas the user has shown interest in in the past. For example, it can use data from the user's project management tool to filter questions related to the current project. This allows for the priority of receiving highly relevant questions by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0074] The reception desk can estimate the user's emotions and prioritize the questions to be answered based on those emotions. For example, if the user is nervous, the reception desk will prioritize high-priority questions. If the user is relaxed, the reception desk may also prioritize detailed questions. Furthermore, if the user is in a hurry, the reception desk may prioritize questions that require a quick answer. For example, the reception desk may capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for prioritizing important questions according to the user's emotions.

[0075] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving questions related to that region. The reception desk can also filter and receive relevant questions based on the user's current location. Furthermore, if the user is on the move, the reception desk can prioritize receiving the most relevant questions based on their current location. For example, the reception desk can use the user's GPS data to filter questions related to their current location. This allows the reception desk to prioritize receiving highly relevant questions by taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without using AI.

[0076] The reception desk can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can accept questions based on topics the user has shown interest in on social media. The reception desk can also filter and accept relevant questions from the user's social media activity. Furthermore, the reception desk can prioritize accepting questions based on topics the user frequently mentions on social media. For example, the reception desk can analyze the content of the user's social media posts to identify relevant questions. This allows for the prioritization of relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0077] The inquiry unit can estimate the user's emotions and adjust the wording of the inquiry based on the estimated emotions. For example, if the user is nervous, the inquiry unit will use a concise and clear expression. If the user is relaxed, the inquiry unit may also use an expression that includes detailed explanations. Furthermore, if the user is in a hurry, the inquiry unit may use an expression that can be quickly understood. For example, the inquiry unit may capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate inquiries by adjusting the wording of the inquiry according to the user's emotions.

[0078] The inquiry unit can adjust the level of detail of an inquiry based on the importance of the question. For example, the inquiry unit will make detailed inquiries for high-importance questions. It can also make concise inquiries for low-importance questions. Furthermore, the inquiry unit can dynamically adjust the level of detail of an inquiry according to the importance of the question. For example, the inquiry unit can assess the urgency of the user's question and make detailed inquiries for high-importance questions. This allows for efficient inquiries by adjusting the level of detail of the inquiry according to the importance of the question. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or without using AI.

[0079] The inquiry unit can apply different inquiry algorithms depending on the category of the question when an inquiry is made. For example, the inquiry unit can apply a legal inquiry algorithm to legal-related questions. It can also apply a security-specific inquiry algorithm to security-related questions. Furthermore, it can apply a creative-specific inquiry algorithm to creative-related questions. For example, the inquiry unit analyzes the category of the user's question and selects the most suitable inquiry algorithm. This enables efficient inquiries by applying the most suitable inquiry algorithm according to the category of the question. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or without using AI.

[0080] The inquiry unit can estimate the user's emotions and adjust the length of the inquiry based on the estimated emotions. For example, if the user is nervous, the inquiry unit will make a short, to-the-point inquiry. If the user is relaxed, the inquiry unit can make a longer inquiry with detailed explanations. Furthermore, if the user is in a hurry, the inquiry unit can make a short inquiry that can be quickly understood. For example, the inquiry unit can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate inquiries by adjusting the length of the inquiry according to the user's emotions.

[0081] The inquiry department can determine the priority of inquiries based on when the question is submitted. For example, the inquiry department can determine the priority based on when the question was submitted. The inquiry department can also dynamically adjust the priority of inquiries according to when the question was submitted. Furthermore, the inquiry department can prioritize inquiries that require a quick response based on when the question was submitted. For example, the inquiry department can record the date and time when a user submits a question and set a priority based on the submission date. This enables a quick response by determining the priority of inquiries based on when the question was submitted. Some or all of the above processing in the inquiry department may be performed using AI, for example, or not using AI.

[0082] The inquiry unit can adjust the order of inquiries based on the relevance of the questions when an inquiry is made. For example, the inquiry unit determines the order of inquiries based on the relevance of the questions. The inquiry unit can also dynamically adjust the order of inquiries according to the relevance of the questions. Furthermore, the inquiry unit can prioritize important questions based on their relevance. For example, the inquiry unit evaluates the similarity of the user's questions and prioritizes questions with high relevance. This allows important questions to be prioritized by adjusting the order of inquiries based on their relevance. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or without using AI.

[0083] The integration unit can estimate the user's emotions and determine the priority of information to integrate based on the estimated emotions. For example, if the user is stressed, the integration unit will prioritize integrating information of high importance. If the user is relaxed, the integration unit can also prioritize integrating detailed information. Furthermore, if the user is in a hurry, the integration unit can prioritize integrating information that is needed quickly. For example, the integration unit can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the priority of information to be determined according to the user's emotions, thereby prioritizing the integration of important information.

[0084] The integration unit can improve the accuracy of the integration based on the interrelationships of responses from each specialized department during the integration process. For example, the integration unit can analyze the interrelationships of responses from each specialized department to improve the accuracy of the integration. The integration unit can also improve the accuracy of the integration by considering the relationships between responses from each specialized department. Furthermore, the integration unit can select the optimal integration method based on the interrelationships of responses from each specialized department. For example, the integration unit can analyze responses from multiple specialized departments and prioritize the integration of highly relevant information. This improves the accuracy of the integration by considering the interrelationships of responses from each specialized department. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without using AI.

[0085] The integration unit can perform integration while considering the attribute information of each specialized department. For example, the integration unit can select the optimal integration method based on the attribute information of each specialized department. The integration unit can also improve the accuracy of the integration by considering the attribute information of each specialized department. Furthermore, the integration unit can prioritize the integration of highly relevant information based on the attribute information of each specialized department. For example, the integration unit integrates information while considering the role and area of ​​expertise of each specialized department. This improves the accuracy of the integration by considering the attribute information of each specialized department. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0086] The integration unit can estimate the user's emotions and adjust the display method of the integrated information based on the estimated user emotions. For example, if the user is tense, the integration unit can provide a simple and highly visible display method. If the user is relaxed, the integration unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the integration unit can provide a concise display method. For example, the integration unit can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for highly visible displays by adjusting the display method of information according to the user's emotions.

[0087] The integration unit can perform integration while considering the geographical distribution of each specialized department. For example, the integration unit can select the optimal integration method based on the geographical distribution of each specialized department. The integration unit can also improve the accuracy of the integration by considering the geographical distribution of each specialized department. Furthermore, the integration unit can prioritize the integration of highly relevant information based on the geographical distribution of each specialized department. For example, the integration unit uses the location information of each specialized department to integrate geographically relevant information. This improves the accuracy of the integration by considering the geographical distribution of each specialized department. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0088] The integration unit can improve the accuracy of the integration by referring to relevant literature during the integration process. For example, the integration unit can improve the accuracy of the integration by referring to relevant literature. The integration unit can also select the optimal integration method based on the information in the relevant literature. Furthermore, the integration unit can also prioritize the integration of highly relevant information by referring to relevant literature. For example, the integration unit can refer to academic papers and technical reports and evaluate the degree of information consistency. This improves the accuracy of the integration by referring to relevant literature. Some or all of the above processes in the integration unit may be performed using AI, for example, or without using AI.

[0089] The information provider can estimate the user's emotions and adjust the way information is delivered based on those emotions. For example, if the user is nervous, the provider can provide a simple and highly visible presentation. If the user is relaxed, the provider can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the provider can provide a presentation that gets straight to the point. For example, the provider can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate information delivery by adjusting the way information is delivered according to the user's emotions.

[0090] The information delivery unit can select the optimal delivery method by referring to the user's past question history when providing information. For example, the delivery unit can select the optimal delivery method based on the content of questions the user has frequently asked in the past. The delivery unit can also prioritize providing relevant information based on the user's past question history. Furthermore, the delivery unit can analyze the user's past question history and select the most efficient delivery method. For example, the delivery unit can retrieve the user's past question history from a database and analyze the historical data. This enables efficient information delivery by selecting the optimal delivery method based on the user's past question history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0091] The information provider can customize the information provided based on the user's current projects and areas of interest. For example, the provider may prioritize providing information related to the user's current projects. It can also customize and provide relevant information based on the user's areas of interest. Furthermore, it can prioritize providing relevant information based on areas the user has shown interest in in the past. For example, the provider may use data from the user's project management tool to provide information related to the current project. This allows for the provision of highly relevant information by customizing it based on the user's current projects and areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or not.

[0092] The information provider can estimate the user's emotions and adjust the order in which information is delivered based on those emotions. For example, if the user is nervous, the provider will prioritize providing information of high importance. Similarly, if the user is relaxed, the provider can prioritize providing detailed information. Furthermore, if the user is in a hurry, the provider can prioritize providing information that is needed quickly. For example, the provider can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the prioritization of important information by adjusting the order in which information is delivered according to the user's emotions.

[0093] The information provider can select the optimal method of providing information by considering the user's geographical location. For example, if the user is in a specific region, the provider will prioritize providing information related to that region. The provider can also customize and provide relevant information based on the user's current location. Furthermore, if the user is on the move, the provider can prioritize providing the most relevant information based on their current location. For example, the provider can use the user's GPS data to provide information related to their current location. This allows for the provision of highly relevant information by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI.

[0094] The information provider can analyze the user's social media activity and customize the content provided when delivering information. For example, the provider can provide information based on topics the user has shown interest in on social media. The provider can also customize and provide relevant information based on the user's social media activity. Furthermore, the provider can prioritize providing information based on topics the user frequently mentions on social media. For example, the provider can analyze the content of the user's social media posts and identify relevant information. This allows the provider to provide highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI.

[0095] The dialogue unit can estimate the user's emotions and adjust the way it expresses itself based on those emotions. For example, if the user is nervous, the dialogue unit will use a concise and clear style of expression. If the user is relaxed, the dialogue unit may also use a style that includes detailed explanations. Furthermore, if the user is in a hurry, the dialogue unit may use a style that can be quickly understood. For example, the dialogue unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate dialogue by adjusting the way it expresses itself according to the user's emotions.

[0096] The dialogue unit can select the optimal dialogue method during a conversation by referring to the user's past dialogue history. For example, the dialogue unit can select the optimal dialogue method based on the dialogue methods the user has frequently used in the past. The dialogue unit can also prioritize providing relevant dialogue methods based on the user's past dialogue history. Furthermore, the dialogue unit can analyze the user's past dialogue history and select the most efficient dialogue method. For example, the dialogue unit can retrieve the user's past dialogue history from a database and analyze the historical data. This enables efficient dialogue by selecting the optimal dialogue method based on the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI.

[0097] The dialogue unit can customize the content of conversations based on the user's current projects and areas of interest. For example, the dialogue unit can prioritize providing dialogue related to the user's current ongoing projects. It can also customize and provide relevant dialogue based on the user's areas of interest. Furthermore, it can prioritize providing relevant dialogue based on areas the user has shown interest in in the past. For example, the dialogue unit can use data from the user's project management tool to provide dialogue related to the current project. This allows for highly relevant conversations by customizing the dialogue based on the user's current projects and areas of interest. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI.

[0098] The dialogue unit can estimate the user's emotions and determine the priority of the conversation based on those emotions. For example, if the user is nervous, the dialogue unit will prioritize high-priority conversations. If the user is relaxed, the dialogue unit can also prioritize detailed conversations. Furthermore, if the user is in a hurry, the dialogue unit can prioritize conversations requiring a quick response. For example, the dialogue unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for prioritizing important conversations based on the user's emotions.

[0099] The dialogue unit can select the optimal dialogue method during a conversation, taking into account the user's geographical location. For example, if the user is in a specific region, the dialogue unit will prioritize providing dialogue content relevant to that region. The dialogue unit can also customize and provide relevant dialogue content based on the user's current location. Furthermore, if the user is on the move, the dialogue unit can prioritize providing the most appropriate dialogue content based on their current location. For example, the dialogue unit can use the user's GPS data to provide dialogue content relevant to their current location. This enables highly relevant dialogue by considering the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI.

[0100] The dialogue unit can analyze the user's social media activity during a conversation and customize the conversation content accordingly. For example, the dialogue unit can provide conversation content based on topics the user has shown interest in on social media. It can also customize and provide relevant conversation content based on the user's social media activity. Furthermore, the dialogue unit can prioritize providing conversation content based on topics the user frequently mentions on social media. For example, the dialogue unit can analyze the user's social media posts and identify relevant conversation content. This enables highly relevant conversations by analyzing the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The project assistant AI system can also be equipped with user schedule management capabilities. This feature retrieves the user's calendar information and optimizes the timing of question submissions and inquiries. For example, if a user is in a meeting, the system will accept questions after the meeting ends. Similarly, if a user is on vacation, less important questions can be accepted after their vacation. Furthermore, the schedule management feature can suggest the optimal timing for providing answers based on the user's schedule. This allows for flexible responses tailored to the user's schedule.

[0103] The project assistant AI system can estimate the user's emotions and adjust the tone of its responses based on those emotions. For example, if the user is stressed, the system will provide responses in a gentle tone. If the user is relaxed, it can provide responses in a casual tone. Furthermore, if the user is in a hurry, it can provide responses in a concise and direct tone. This enables appropriate communication tailored to the user's emotions.

[0104] The project assistant AI system can analyze a user's past question history and automatically suggest answers to similar questions. For example, if a user previously asked about "the budget for a new project," the system will use that answer as a reference to suggest answers to new questions. It can also automatically generate related questions based on keywords the user has used in the past. Furthermore, it can prioritize displaying frequently asked questions based on the user's past question history. This enables efficient question handling by leveraging the user's past question history.

[0105] The project assistant AI system can prioritize providing relevant information based on the user's current project progress. For example, if the user is in the early stages of a project, the system will prioritize providing information on planning and budgeting. If the user is in the middle stages of the project, it can also provide information on progress management and resource allocation. Furthermore, if the user is in the final stages of the project, it can provide information on deliverable review and final reporting. This enables the provision of appropriate information according to the project's progress.

[0106] The project assistant AI system can estimate the user's emotions and adjust the information display format based on those emotions. For example, if the user is stressed, the system provides a simple and highly visible display format. If the user is relaxed, it can provide a display format that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise display format that gets straight to the point. This enables the display of information appropriately according to the user's emotions.

[0107] The project assistant AI system can provide region-specific information by taking into account the user's geographical location. For example, if the user is in a specific region, it will prioritize providing information on laws, regulations, and market conditions relevant to that region. If the user is on a business trip abroad, it can also provide information on local business etiquette and culture. Furthermore, if the user is on the move, it can provide optimal information based on their current location. This enables the provision of appropriate information that takes the user's geographical location into consideration.

[0108] The project assistant AI system can analyze a user's social media activity and provide relevant information. For example, it can provide information based on topics the user has shown interest in on social media. It can also automatically generate relevant questions from the user's social media activity. Furthermore, it can prioritize information based on topics the user frequently mentions on social media. This enables the provision of appropriate information by leveraging the user's social media activity.

[0109] The project assistant AI system can estimate the user's emotions and adjust the pace of the conversation based on those emotions. For example, if the user is nervous, the system will proceed at a slow pace. If the user is relaxed, it can proceed at a normal pace. Furthermore, if the user is in a hurry, it can proceed quickly. This enables appropriate conversational progression according to the user's emotions.

[0110] The project assistant AI system can adjust the level of detail in its answers based on the importance of the user's questions. For example, it can provide detailed answers to high-priority questions and concise answers to low-priority questions. Furthermore, it can dynamically adjust the level of detail in its answers according to the importance of the questions. This enables efficient answer provision tailored to the importance of each question.

[0111] The project assistant AI system can estimate the user's emotions and adjust the content of the conversation based on those emotions. For example, if the user is stressed, the system will provide content to help them relax. If the user is relaxed, it can also provide detailed information. Furthermore, if the user is in a hurry, it can quickly provide the necessary information. This enables the provision of appropriate conversation content tailored to the user's emotions.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk receives questions from users. These questions can be in text, audio, or image formats. The reception desk can directly accept text questions, and can also convert audio questions into text using speech recognition technology. It can also analyze image questions using image recognition technology and convert them into text for acceptance. Step 2: The inquiry department analyzes the questions received by the reception department and contacts the relevant specialist departments. The inquiry department uses natural language processing technology and machine learning algorithms to analyze the content of the questions and identify the relevant specialist departments. Furthermore, it automatically contacts the relevant specialist departments based on the content of the questions. Step 3: The Integration Department integrates the responses collected by the Inquiry Department. The Integration Department integrates the responses using data merging methods and duplicate data handling methods, taking into account the relevance of the responses. For example, it integrates responses from multiple specialized departments and eliminates duplicate information. Step 4: The delivery unit provides the user with the information integrated by the integration unit. The delivery unit can provide the information using text format, audio format, or visual display methods (such as charts and graphs). Step 5: The dialogue unit communicates in natural language using conversational AI. The dialogue unit interacts with the user using chatbots, voice assistants, and text generation AI (e.g., LLM) to generate responses in natural language.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the reception unit, inquiry unit, integration unit, provision unit, and dialogue unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives questions from the user. The inquiry unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the question and makes inquiries to the relevant specialized departments. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the collected responses. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the integrated information to the user. The dialogue unit is implemented by the control unit 46A of the smart device 14 and communicates in natural language using conversational AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the reception unit, inquiry unit, integration unit, provision unit, and dialogue unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives questions from the user. The inquiry unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the question and makes inquiries to the relevant specialized departments. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the collected responses. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the integrated information to the user. The dialogue unit is implemented by the control unit 46A of the smart glasses 214 and communicates in natural language using conversational AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the reception unit, inquiry unit, integration unit, provision unit, and dialogue 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 is implemented by the control unit 46A of the headset terminal 314 and receives questions from the user. The inquiry unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the content of the question and makes inquiries to the relevant specialized departments. The integration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and integrates the collected responses. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides the integrated information to the user. The dialogue unit is implemented by, for example, the control unit 46A of the headset terminal 314 and communicates in natural language using conversational AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the reception unit, inquiry unit, integration unit, provision unit, and dialogue unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives questions from the user. The inquiry unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the content of the question and makes inquiries to the relevant specialized departments. The integration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and integrates the collected responses. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the integrated information to the user. The dialogue unit is implemented by, for example, the control unit 46A of the robot 414 and communicates in natural language using conversational AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A reception desk that handles questions from users, The inquiry department analyzes the questions received by the aforementioned reception department and makes inquiries to the relevant specialized departments. An integration unit that integrates the responses collected by the aforementioned inquiry unit, A providing unit that provides the information integrated by the aforementioned integration unit to the user, It features a dialogue unit that uses conversational AI to communicate in natural language. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past question history and select the most appropriate method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When receiving questions, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving questions, the system prioritizes questions that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned inquiry section is, It estimates the user's emotions and adjusts the wording of inquiries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned inquiry section is, When you submit an inquiry, adjust the level of detail based on the importance of your question. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned inquiry section is, When an inquiry is made, a different query algorithm is applied depending on the category of the question. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned inquiry section is, It estimates the user's sentiment and adjusts the length of the inquiry based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned inquiry section is, When you submit an inquiry, we will prioritize it based on when you submitted it. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned inquiry section is, When you submit an inquiry, we will adjust the order of the inquiries based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned integration unit is It estimates the user's emotions and determines the priority of information to integrate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned integration unit is During integration, improve the accuracy of the integration based on the interrelationships of responses from each specialized department. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned integration unit is During integration, the integration will be carried out based on the attribute information of each specialized department. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned integration unit is It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned integration unit is During the integration process, the integration will be carried out based on the geographical distribution of each specialized department. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned integration unit is During integration, refer to relevant literature to improve the accuracy of the integration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, the system will refer to the user's past question history to select the most appropriate method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, the content will be customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts the order in which information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, the optimal method of 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 25) The aforementioned supply unit is, When providing information, we analyze the user's social media activity to customize the content provided. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dialogue unit, During a conversation, the system selects the optimal conversation method by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dialogue unit, During conversations, the dialogue content is customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned dialogue unit, During the interaction, the system selects the optimal interaction method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity to customize the conversation content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0186] 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 handles questions from users, The inquiry department analyzes the questions received by the aforementioned reception department and makes inquiries to the relevant specialized departments. An integration unit that integrates the responses collected by the aforementioned inquiry unit, A providing unit that provides the information integrated by the aforementioned integration unit to the user, It features a dialogue unit that uses conversational AI to communicate in natural language. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past question history and select the most appropriate method of handling inquiries. The system according to feature 1.

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

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

6. The aforementioned reception unit is When receiving questions, the system prioritizes questions that are highly relevant based on the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and selects relevant questions. The system according to feature 1.

8. The aforementioned inquiry section is, It estimates the user's emotions and adjusts the wording of inquiries based on those estimated emotions. The system according to feature 1.

Images