System

Abstract

An object of a system according to an embodiment is to quickly provide appropriate information in response to a question from a user.SOLUTION: A system according to an embodiment includes a question analysis unit, an information acquisition unit, and a response management unit. The question analyzer analyzes the question from the user using the generated AI and provides an appropriate answer. The information acquisition unit acquires necessary information from internal and external information sources on the basis of the question analyzed by the question analysis unit. The response management unit manages a response item from the user based on the information acquired by the information acquisition unit, and performs an appropriate response.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult to quickly provide appropriate information in response to a user's question.

[0005] The system according to the embodiment aims to quickly provide appropriate information in response to a question from a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a question analysis unit, an information acquisition unit, and a response management unit. The question analysis unit uses a generation AI to analyze questions from users and provide appropriate answers. The information acquisition unit acquires necessary information from internal and external information sources based on the questions analyzed by the question analysis unit. The response management unit manages response items from users based on the information acquired by the information acquisition unit and takes appropriate responses. [Effects of the Invention]

[0007] The system according to the embodiment can quickly provide appropriate information in response to a question from a user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The universal chatbot according to an embodiment of the present invention is a system that can answer all questions regarding internal and external confirmations and responses. This system uses a generation AI to analyze questions from users and provide appropriate answers. This allows the universal chatbot to quickly and accurately respond to internal and external confirmations and responses.

[0029] A universal chatbot according to an embodiment includes a question analysis unit, an information acquisition unit, and a response management unit. The question analysis unit analyzes questions from users using a generation AI and provides appropriate answers. For example, the generation AI analyzes questions and generates answers using a text generation AI (e.g., GPT-3). The generation AI can also use natural language processing technology to understand the intent of the question and provide appropriate answers. The generation AI can also use keyword extraction technology to analyze important parts of the question and generate answers. The information acquisition unit acquires necessary information from internal and external sources based on the question analyzed by the question analysis unit. For example, the information acquisition unit accesses an internal database to acquire employee contact information. The information acquisition unit can also acquire the latest industry news using an external API. The information acquisition unit can also acquire contact information for business partners using web scraping technology. The response management unit manages responses from users based on the information acquired by the information acquisition unit and takes appropriate actions. For example, the response management unit replies to emails based on instructions from users. The response management unit can also use a task management system to manage the progress of response items and report it to the user. The response management unit can also use a reminder function to notify the user of deadlines for important tasks. This allows the universal chatbot according to the embodiment to quickly and accurately respond to confirmation items and response items both inside and outside the company. For example, since the user can quickly obtain the necessary information, work efficiency is improved. In addition, smooth communication with business partners can be achieved, which is expected to lead to smooth business progress. Furthermore, the response item management and notification functions allow important tasks to be completed without being overlooked.

[0030] The question analysis unit can generate more personalized answers by referencing the user's past question history. For example, the question analysis unit uses a generation AI to retrieve the user's past question history from a database and generate new answers by referring to past answers to similar questions. For example, the question analysis unit provides the latest schedule information based on the answer to a previous question the user asked, "What is the meeting schedule this week?" The question analysis unit also analyzes the user's past question history and provides more detailed information for frequently asked questions. For example, if a user repeatedly asks, "What is Mr. / Ms. XX's contact information?", the question analysis unit provides related project information in addition to contact information. The question analysis unit also learns the user's preferences and interests based on the question history and generates answers accordingly. For example, if a user frequently asks questions about a specific project, the latest information related to that project is prioritized. This allows the system to provide more personalized answers by referencing the user's past question history.

[0031] The question analysis unit can generate answers taking into account the user's current situation. For example, the generation AI in the question analysis unit obtains the user's location information and provides information related to that location. For example, if the user is outside the office, it can provide information about nearby cafes and conference rooms. The question analysis unit also generates appropriate answers taking into account the user's time of day. For example, if the question is asked at night, "What is the meeting schedule?", it will prioritize providing the next day's schedule. The question analysis unit also analyzes the user's current situation and generates answers accordingly. For example, if it detects that the user is on a business trip, it will provide weather and traffic information for the business trip destination. This allows for more appropriate answers to be provided by taking the user's current situation into account.

[0032] The question analysis unit can respond to voice input, analyze the question in real time using voice recognition technology, and generate an answer. For example, the question analysis unit uses voice recognition technology to convert the user's voice input into text, analyze the question based on that text, and generate an answer. For example, if a user asks by voice, "What is the schedule for meetings this week?", the question analysis unit converts the voice into text and provides an answer. To respond to voice input, the question analysis unit also uses voice recognition technology to analyze the user's pronunciation and accent and perform accurate text conversion. For example, this makes it possible to respond to different dialects and accents. The question analysis unit also builds a system that analyzes voice input in real time and generates answers instantly. For example, if a user asks a question by voice during a meeting, the answer is provided on the spot. This allows users to ask questions and receive answers in a more natural way by supporting voice input.

[0033] The question analysis unit can support different languages, making it usable by international users. For example, the generation AI uses multilingual natural language processing technology to analyze questions in different languages ​​and generate answers in the corresponding language. For example, it supports multiple languages ​​such as English, French, and Chinese. Furthermore, when a user inputs a question in a different language, the generation AI automatically detects the language and provides an answer in the appropriate language. For example, if a user asks a question in Spanish, it generates an answer in Spanish. Furthermore, the generation AI builds a multilingual database to enable use by international users, providing information corresponding to each language. For example, it provides contact information for business partners in multiple languages. This allows support for different languages, making it usable by international users.

[0034] The information acquisition unit can evaluate the importance of the information when providing internal information and provide important information with priority. For example, the information acquisition unit uses an algorithm in which the generation AI evaluates the importance of internal information and provides important information with priority. For example, it prioritizes displaying highly urgent contact information and project progress status. The information acquisition unit also evaluates the importance of internal information and provides the information most relevant to a user's question. For example, if a user asks, "What are the important meetings this week?", it prioritizes providing information about meetings with high importance. The information acquisition unit also builds a system in which the generation AI evaluates the importance of information in real time and provides important information immediately. For example, it prioritizes displaying urgent contact information and important project progress information. In this way, important information can be provided with priority by evaluating the importance of internal information.

[0035] The information acquisition unit can provide the latest information by taking into account the frequency of information updates when providing internal company information. For example, the generation AI of the information acquisition unit analyzes the frequency of internal company information updates and provides the latest information preferentially. For example, the progress of a frequently updated project is displayed in real time. The information acquisition unit also takes into account the frequency of information updates to build a system that provides the latest information in response to a user's question. For example, if an employee's contact information is updated, the latest contact information is provided. The information acquisition unit also monitors the frequency of information updates in real time and provides the latest information immediately. For example, if internal company regulations are changed, the latest regulation information is displayed preferentially. In this way, by taking into account the frequency of information updates, the latest information can always be provided.

[0036] The information acquisition unit can provide internal company information as visual data, making it easier to understand visually. For example, the information acquisition unit builds a system in which a generation AI provides internal company information as visual data. For example, it displays the progress of a project in graphs and charts. The information acquisition unit also provides answers to user questions using visual data. For example, it displays employee contact information as an organizational chart. The information acquisition unit also uses visual data to make internal company information easier to understand visually. For example, it shows changes to internal company regulations in a chart. In this way, the use of visual data makes internal company information easier to understand visually.

[0037] The information acquisition unit can also make the provision of internal company information compatible with mobile devices, allowing information to be accessed anywhere. For example, the information acquisition unit uses a generation AI to build an internal company information system compatible with mobile devices. For example, it allows employees' contact information and project progress to be checked on a smartphone or tablet. Furthermore, when a user inputs a question from a mobile device, the generation AI provides an appropriate answer. For example, it allows internal company regulations and contact information to be accessed even when the user is out and about. Furthermore, the information acquisition unit develops an internal company information system compatible with mobile devices, allowing information to be accessed anywhere. For example, checking the meeting schedule on a smartphone during a meeting. This allows information to be accessed anywhere by being compatible with mobile devices.

[0038] When providing external information, the information acquisition unit can evaluate the reliability of the information and provide more reliable information preferentially. For example, the generation AI uses an algorithm to evaluate the reliability of external information, and the information acquisition unit provides more reliable information preferentially. For example, information from official news sources or reliable databases is preferentially displayed. The information acquisition unit also evaluates the reliability of external information and provides the most reliable information in response to a user's question. For example, contact information for business partners is obtained from an official database and provided. The information acquisition unit also builds a system in which the generation AI evaluates the reliability of information in real time and provides more reliable information immediately. For example, the latest industry news from reliable sources is preferentially displayed. This allows the reliability of information to be evaluated and more reliable information to be provided preferentially.

[0039] The information acquisition unit can evaluate the relevance of external information when providing it and provide information related to the user's work. For example, the information acquisition unit uses an algorithm in which the generation AI evaluates the relevance of external information to provide the information most relevant to the user's work. For example, the latest news in industries of interest to the user and trends of competitors are displayed preferentially. The information acquisition unit also evaluates the relevance of external information and builds a system that provides the most relevant information to the user's questions. For example, it provides contact information for business partners along with project information related to the user's work. The information acquisition unit also uses the generation AI to evaluate the relevance of information in real time and instantly provide information related to the user's work. For example, it displays the latest research results related to topics of interest to the user. In this way, by evaluating the relevance of information, it is possible to provide information related to the user's work.

[0040] The information acquisition unit can provide external information in a news feed format, allowing the user to obtain the latest information in real time. The information acquisition unit, for example, builds a system in which the generation AI provides external information in a news feed format. For example, the latest industry news and competitor trends are displayed in real time. The information acquisition unit also provides answers to user questions in a news feed format. For example, contact information for business partners is incorporated into the news feed. The information acquisition unit also provides external information in a news feed format, allowing the user to obtain the latest information in real time. For example, the latest industry news and competitor trends are displayed in real time. As a result, by providing information in a news feed format, the latest information can be obtained in real time.

[0041] The information acquisition unit provides external information, including information from different industries, thereby broadening the user's perspective. The information acquisition unit, for example, builds a system in which a generation AI collects information from different industries and provides it to the user. For example, it provides the latest news and trend information from industries other than the one the user is interested in. The information acquisition unit also provides answers to user questions, including information from different industries. For example, it provides the latest news from that industry along with contact information for business partners. The information acquisition unit also provides information from different industries, broadening the user's perspective. For example, it provides the latest news and trend information from industries other than the one the user is interested in. This allows the user's perspective to be broadened by providing information from different industries.

[0042] The response management unit can automatically set task priorities in managing response items, enabling efficient task management. The response management unit performs efficient task management, for example, by using an algorithm in which the generation AI automatically sets the priority of response items. For example, the priority of tasks is set based on urgency and importance. The response management unit also builds a system in which the generation AI automatically sets task priorities based on user instructions. For example, if a user instructs, "Please reply to this email," the priority of that task is set. The response management unit also performs efficient task management by having the generation AI set task priorities in real time. For example, the priority of tasks is set based on urgency and importance. This enables efficient task management by automatically setting task priorities.

[0043] In managing response items, the response management unit can track the progress of tasks in real time and report it to the user. For example, the response management unit builds a system in which the generation AI tracks the progress of tasks in real time and reports it to the user. For example, the completion status and progress status of tasks are displayed in real time. The response management unit also tracks and reports the progress of tasks in real time based on user instructions. For example, if the user instructs, "Please reply to this email," the progress status of that task is reported. The response management unit also builds a system in which the generation AI tracks the progress of tasks in real time and reports it to the user. For example, the completion status and progress status of tasks are displayed in real time. This improves the efficiency of task management by tracking the progress of tasks in real time and reporting it to the user.

[0044] The response management unit can link the management of response items with a project management tool to manage tasks across the entire team. For example, the response management unit builds a system in which the generation AI links with the project management tool to manage tasks across the entire team. For example, it reflects the progress of tasks and the person in charge in the project management tool. Furthermore, the response management unit builds a system in which the generation AI links with the project management tool based on user instructions to manage tasks across the entire team. For example, if the user instructs, "Please assign this task to Mr. / Ms. XX," it reflects that task in the project management tool. Furthermore, the response management unit builds a system in which the generation AI links with the project management tool to manage tasks across the entire team. For example, it reflects the progress of tasks and the person in charge in the project management tool. In this way, by linking with the project management tool, task management across the entire team can be performed efficiently.

[0045] The response management unit can centralize schedule management by linking the management of response items with a calendar app. For example, the response management unit builds a system in which the generation AI links with a calendar app to centralize schedule management. For example, it reflects meeting schedules and task deadlines in the calendar app. Furthermore, the response management unit builds a system in which the generation AI links with a calendar app based on user instructions to centralize schedule management. For example, if a user instructs, "Please add this meeting to the calendar," it reflects that schedule in the calendar app. Furthermore, the response management unit builds a system in which the generation AI links with a calendar app to centralize schedule management. For example, it reflects meeting schedules and task deadlines in the calendar app. In this way, by linking with the calendar app, schedule management can be centralized.

[0046] The notification unit can evaluate the importance of notifications in an automated notification function and prioritize sending important notifications. For example, the generation AI uses an algorithm to evaluate the importance of notifications, and the notification unit prioritizes sending important notifications. For example, it prioritizes sending reminders for urgent meetings and notifications of important deadlines. The notification unit also builds a system in which the generation AI evaluates the importance of notifications based on user instructions and prioritizes sending important notifications. For example, if a user instructs the system to "please send this notification as a priority," the system sends that notification as a priority. The notification unit also builds a system in which the generation AI evaluates the importance of notifications in real time and immediately sends important notifications. For example, it prioritizes sending reminders for urgent tasks and notifications for important meetings. This allows important notifications to be sent as a priority by evaluating the importance of notifications.

[0047] The notification unit can optimize the timing of notifications in an automated notification function, thereby improving the work efficiency of users. For example, the generation AI uses an algorithm to optimize the timing of notifications to improve the work efficiency of users. For example, notifications are sent during times when the user is most focused. The notification unit also analyzes the user's schedule and work status to build a system that sends notifications at the optimal timing. For example, notifications are sent during times when the user is not in a meeting. The notification unit also optimizes the timing of notifications in real time using the generation AI to improve the work efficiency of users. For example, a notification for the next task is sent immediately after the user completes a task. In this way, the timing of notifications is optimized, thereby improving the work efficiency of users.

[0048] The notification unit can also make the automated notification function compatible with different devices. For example, the notification unit builds a notification system in which the generation AI is compatible with different devices. For example, it sends notifications to a smartwatch or smart speaker. The notification unit also builds a system that sends notifications to the optimal device depending on the user's device environment. For example, if the user is using a smartwatch, it sends notifications to that device. The notification unit also develops a notification system that is compatible with different devices, allowing the user to receive notifications on any device. For example, it sends notifications simultaneously to a smartphone, smartwatch, and smart speaker. This makes it possible to support different devices, allowing the user to receive notifications on any device.

[0049] The notification unit can make the automated notification function customizable, allowing users to set the content and timing of notifications. For example, the notification unit may build a customizable notification system for the generation AI, allowing users to set the content and timing of notifications. For example, the user may customize the frequency and content of notifications. The notification unit may also build a system in which the generation AI customizes the content and timing of notifications based on user instructions. For example, if a user instructs the generation AI to "send this notification every day at 9:00 AM," the system will set it accordingly. The notification unit may also develop a customizable notification system, allowing users to freely set the content and timing of notifications. For example, the system may allow users to set the content and timing of notifications by dragging and dropping. This allows the content and timing of notifications to be customized, allowing notifications to be delivered according to the user's needs.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The question analysis unit can refer to the user's past question history to generate more personalized answers. For example, the latest schedule information can be provided based on the user's previous answer to the question, "What is the meeting schedule this week?" The question analysis unit also analyzes the user's past question history to provide more detailed information for frequently asked questions. For example, if a user repeatedly asks, "What is Mr. / Ms. XX's contact information?", related project information can be provided in addition to contact information. The question analysis unit can also learn the user's preferences and interests based on the question history and generate answers accordingly. For example, if a user frequently asks questions about a specific project, the latest information related to that project can be provided preferentially. This allows the system to provide more personalized answers by referring to the user's past question history.

[0052] The question analysis unit can generate answers taking into account the user's current situation. For example, the generation AI obtains the user's location information and provides information related to that location. For example, if the user is outside the office, it can provide information about nearby cafes and conference rooms. The question analysis unit also generates appropriate answers taking into account the user's time of day. For example, if the question is asked at night, "What is the meeting schedule?", it will prioritize providing the next day's schedule. The question analysis unit also analyzes the user's current situation and generates answers accordingly. For example, if it detects that the user is on a business trip, it will provide weather and traffic information for the business trip destination. This allows the system to provide more appropriate answers by taking the user's current situation into account.

[0053] The question analysis unit can respond to voice input, analyze the question in real time using voice recognition technology, and generate an answer. For example, the generation AI uses voice recognition technology to convert the user's voice input into text, analyze the question based on that text, and generate an answer. For example, if a user asks by voice, "What is the schedule for meetings this week?", the voice is converted into text and an answer is provided. In addition, to respond to voice input, the question analysis unit uses voice recognition technology to analyze the user's pronunciation and accent and perform accurate text conversion. For example, this makes it possible to respond to different dialects and accents. The question analysis unit also builds a system that analyzes voice input in real time and generates answers instantly. For example, if a user asks a question by voice during a meeting, an answer is provided on the spot. This allows users to ask questions and receive answers in a more natural way by supporting voice input.

[0054] The question analysis unit can support different languages, making it usable by international users. For example, the generation AI uses multilingual natural language processing technology to analyze questions in different languages ​​and generate answers in the corresponding language. For example, it supports multiple languages ​​such as English, French, and Chinese. Furthermore, when a user enters a question in a different language, the question analysis unit automatically detects the language and provides an answer in the appropriate language. For example, if a user asks a question in Spanish, it generates an answer in Spanish. Furthermore, the question analysis unit allows the generation AI to build a multilingual database and provide information corresponding to each language so that it can be used by international users. For example, it provides contact information for business partners in multiple languages. This makes it possible to support different languages ​​and make it usable by international users.

[0055] The information acquisition unit can evaluate the importance of internal information when providing it and provide important information on a priority basis. For example, the generation AI uses an algorithm to evaluate the importance of internal information and provide important information on a priority basis. For example, it prioritizes displaying highly urgent contact information and project progress status. The information acquisition unit also evaluates the importance of internal information and provides the information most relevant to a user's question. For example, if a user asks, "What are the important meetings this week?", it prioritizes providing information about meetings with high importance. The information acquisition unit also builds a system in which the generation AI evaluates the importance of information in real time and provides important information immediately. For example, it prioritizes displaying urgent contact information and important project progress information. In this way, important information can be provided on a priority basis by evaluating the importance of internal information.

[0056] The information acquisition unit can provide the latest information when providing internal company information, taking into account how often the information is updated. For example, the generation AI analyzes how often internal company information is updated and provides the latest information as a priority. For example, the progress of a frequently updated project is displayed in real time. The information acquisition unit also takes into account how often the information is updated and builds a system that provides the latest information in response to user questions. For example, if an employee's contact information is updated, the latest contact information is provided. The information acquisition unit also monitors how often the information is updated in real time and provides the latest information immediately. For example, if internal company regulations are changed, the latest regulation information is displayed as a priority. This allows the latest information to be always provided by taking into account how often the information is updated.

[0057] The information acquisition unit can provide internal company information as visual data, making it easier to understand visually. For example, a system can be built in which a generative AI provides internal company information as visual data. For example, the progress of a project can be displayed in graphs and charts. The information acquisition unit can also provide answers to user questions using visual data. For example, employee contact information can be displayed as an organizational chart. The information acquisition unit can also use visual data to make internal company information easier to understand visually. For example, changes to internal company regulations can be displayed in a chart. In this way, the use of visual data makes internal company information easier to understand visually.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The question analysis unit uses the generation AI to analyze the question from the user and provide an appropriate answer. For example, the generation AI may use a text generation AI (e.g., GPT-3) to analyze the question and generate an answer. The generation AI may also use natural language processing technology to understand the intent of the question and provide an appropriate answer. Furthermore, the generation AI may use keyword extraction technology to analyze the important parts of the question and generate an answer. Step 2: The information acquisition unit acquires the necessary information from internal and external sources based on the questions analyzed by the question analysis unit. For example, the information acquisition unit may access an internal database to acquire employee contact information. The information acquisition unit may also use an external API to acquire the latest industry news. Furthermore, the information acquisition unit may use web scraping technology to acquire contact information for business partners. Step 3: The response management unit manages responses from users based on the information acquired by the information acquisition unit and takes appropriate action. For example, the response management unit replies to emails based on instructions from users. The response management unit can also use a task management system to manage the progress of responses and report it to users. Furthermore, the response management unit can use a reminder function to notify users of deadlines for important tasks.

[0060] (Example 2) The universal chatbot according to an embodiment of the present invention is a system that can answer all questions regarding internal and external confirmations and responses. This system uses a generation AI to analyze questions from users and provide appropriate answers. This allows the universal chatbot to quickly and accurately respond to internal and external confirmations and responses.

[0061] A universal chatbot according to an embodiment includes a question analysis unit, an information acquisition unit, and a response management unit. The question analysis unit analyzes questions from users using a generation AI and provides appropriate answers. For example, the generation AI analyzes questions and generates answers using a text generation AI (e.g., GPT-3). The generation AI can also use natural language processing technology to understand the intent of the question and provide appropriate answers. The generation AI can also use keyword extraction technology to analyze important parts of the question and generate answers. The information acquisition unit acquires necessary information from internal and external sources based on the question analyzed by the question analysis unit. For example, the information acquisition unit accesses an internal database to acquire employee contact information. The information acquisition unit can also acquire the latest industry news using an external API. The information acquisition unit can also acquire contact information for business partners using web scraping technology. The response management unit manages responses from users based on the information acquired by the information acquisition unit and takes appropriate actions. For example, the response management unit replies to emails based on instructions from users. The response management unit can also use a task management system to manage the progress of response items and report it to the user. The response management unit can also use a reminder function to notify the user of deadlines for important tasks. This allows the universal chatbot according to the embodiment to quickly and accurately respond to confirmation items and response items both inside and outside the company. For example, since the user can quickly obtain the necessary information, work efficiency is improved. In addition, smooth communication with business partners can be achieved, which is expected to lead to smooth business progress. Furthermore, the response item management and notification functions allow important tasks to be completed without being overlooked.

[0062] The question analysis unit can generate more personalized answers by referencing the user's past question history. For example, the question analysis unit uses a generation AI to retrieve the user's past question history from a database and generate new answers by referring to past answers to similar questions. For example, the question analysis unit provides the latest schedule information based on the answer to a previous question the user asked, "What is the meeting schedule this week?" The question analysis unit also analyzes the user's past question history and provides more detailed information for frequently asked questions. For example, if a user repeatedly asks, "What is Mr. / Ms. XX's contact information?", the question analysis unit provides related project information in addition to contact information. The question analysis unit also learns the user's preferences and interests based on the question history and generates answers accordingly. For example, if a user frequently asks questions about a specific project, the latest information related to that project is prioritized. This allows the system to provide more personalized answers by referencing the user's past question history.

[0063] The question analysis unit can generate answers taking into account the user's current situation. For example, the generation AI in the question analysis unit obtains the user's location information and provides information related to that location. For example, if the user is outside the office, it can provide information about nearby cafes and conference rooms. The question analysis unit also generates appropriate answers taking into account the user's time of day. For example, if the question is asked at night, "What is the meeting schedule?", it will prioritize providing the next day's schedule. The question analysis unit also analyzes the user's current situation and generates answers accordingly. For example, if it detects that the user is on a business trip, it will provide weather and traffic information for the business trip destination. This allows for more appropriate answers to be provided by taking the user's current situation into account.

[0064] The question analysis unit can use the emotion estimation function to analyze the user's emotional state and generate an answer in a tone that corresponds to the emotion. For example, the question analysis unit uses a generation AI to analyze emotions from the user's input text and generate an answer in a tone that corresponds to the emotion. For example, if the user is feeling stressed, the question analysis unit answers in a gentle tone that relaxes the user. The question analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and generate an answer that elicits positive emotions. For example, if the user is feeling anxious, the question analysis unit provides an answer that includes words of encouragement. The question analysis unit also adjusts the content of the answer depending on the user's emotional state. For example, if the user is feeling angry, the question analysis unit answers in a calm and polite tone and makes specific suggestions for solving the problem. In this way, by providing answers in a tone that corresponds to the user's emotional state, user satisfaction is improved.

[0065] The question analysis unit can respond to voice input, analyze the question in real time using voice recognition technology, and generate an answer. For example, the question analysis unit uses voice recognition technology to convert the user's voice input into text, analyze the question based on that text, and generate an answer. For example, if a user asks by voice, "What is the schedule for meetings this week?", the question analysis unit converts the voice into text and provides an answer. To respond to voice input, the question analysis unit also uses voice recognition technology to analyze the user's pronunciation and accent and perform accurate text conversion. For example, this makes it possible to respond to different dialects and accents. The question analysis unit also builds a system that analyzes voice input in real time and generates answers instantly. For example, if a user asks a question by voice during a meeting, the answer is provided on the spot. This allows users to ask questions and receive answers in a more natural way by supporting voice input.

[0066] The question analysis unit can support different languages, making it usable by international users. For example, the generation AI uses multilingual natural language processing technology to analyze questions in different languages ​​and generate answers in the corresponding language. For example, it supports multiple languages ​​such as English, French, and Chinese. Furthermore, when a user inputs a question in a different language, the generation AI automatically detects the language and provides an answer in the appropriate language. For example, if a user asks a question in Spanish, it generates an answer in Spanish. Furthermore, the generation AI builds a multilingual database to enable use by international users, providing information corresponding to each language. For example, it provides contact information for business partners in multiple languages. This allows support for different languages, making it usable by international users.

[0067] The question analysis unit uses the emotion estimation function to restructure questions according to the user's emotions, thereby providing more appropriate answers. For example, the question analysis unit uses a generation AI to analyze the user's emotional state and restructure questions according to the emotions. For example, if the user is feeling anxious, the question is restructured to be more specific and reassuring. The question analysis unit also uses the emotion estimation function to restructure questions according to the user's emotions, thereby improving the accuracy of answers. For example, if the user is confused, the question is restructured to be concise and clear. The question analysis unit also restructures questions based on the user's emotional state to provide more appropriate answers. For example, if the user is feeling angry, the question is restructured to be calm and specific. In this way, by restructuring questions according to the user's emotions, more appropriate answers can be provided.

[0068] The information acquisition unit can evaluate the importance of the information when providing internal information and provide important information with priority. For example, the information acquisition unit uses an algorithm in which the generation AI evaluates the importance of internal information and provides important information with priority. For example, it prioritizes displaying highly urgent contact information and project progress status. The information acquisition unit also evaluates the importance of internal information and provides the information most relevant to a user's question. For example, if a user asks, "What are the important meetings this week?", it prioritizes providing information about meetings with high importance. The information acquisition unit also builds a system in which the generation AI evaluates the importance of information in real time and provides important information immediately. For example, it prioritizes displaying urgent contact information and important project progress information. In this way, important information can be provided with priority by evaluating the importance of internal information.

[0069] The information acquisition unit can provide the latest information by taking into account the frequency of information updates when providing internal company information. For example, the generation AI of the information acquisition unit analyzes the frequency of internal company information updates and provides the latest information preferentially. For example, the progress of a frequently updated project is displayed in real time. The information acquisition unit also takes into account the frequency of information updates to build a system that provides the latest information in response to a user's question. For example, if an employee's contact information is updated, the latest contact information is provided. The information acquisition unit also monitors the frequency of information updates in real time and provides the latest information immediately. For example, if internal company regulations are changed, the latest regulation information is displayed preferentially. In this way, by taking into account the frequency of information updates, the latest information can always be provided.

[0070] The information acquisition unit can provide internal company information as visual data, making it easier to understand visually. For example, the information acquisition unit builds a system in which a generation AI provides internal company information as visual data. For example, it displays the progress of a project in graphs and charts. The information acquisition unit also provides answers to user questions using visual data. For example, it displays employee contact information as an organizational chart. The information acquisition unit also uses visual data to make internal company information easier to understand visually. For example, it shows changes to internal company regulations in a chart. In this way, the use of visual data makes internal company information easier to understand visually.

[0071] The information acquisition unit can also make the provision of internal company information compatible with mobile devices, allowing information to be accessed anywhere. For example, the information acquisition unit uses a generation AI to build an internal company information system compatible with mobile devices. For example, it allows employees' contact information and project progress to be checked on a smartphone or tablet. Furthermore, when a user inputs a question from a mobile device, the generation AI provides an appropriate answer. For example, it allows internal company regulations and contact information to be accessed even when the user is out and about. Furthermore, the information acquisition unit develops an internal company information system compatible with mobile devices, allowing information to be accessed anywhere. For example, checking the meeting schedule on a smartphone during a meeting. This allows information to be accessed anywhere by being compatible with mobile devices.

[0072] The information acquisition unit uses the emotion estimation function to change the order in which information is presented according to the user's emotions, thereby reducing stress. For example, the information acquisition unit uses a generation AI to analyze the user's emotional state and change the order in which information is presented. For example, if the user is feeling stressed, important information is displayed first. The information acquisition unit also uses the emotion estimation function to build a system that changes the order in which information is presented according to the user's emotions in real time. For example, if the user is feeling anxious, information with a high level of urgency is displayed first. The information acquisition unit also changes the order in which information is presented based on the user's emotional state to reduce stress. For example, if the user is feeling anxious, information that gives a sense of security is displayed first. In this way, stress can be reduced by changing the order in which information is presented according to the user's emotions.

[0073] When providing external information, the information acquisition unit can evaluate the reliability of the information and provide more reliable information preferentially. For example, the generation AI uses an algorithm to evaluate the reliability of external information, and the information acquisition unit provides more reliable information preferentially. For example, information from official news sources or reliable databases is preferentially displayed. The information acquisition unit also evaluates the reliability of external information and provides the most reliable information in response to a user's question. For example, contact information for business partners is obtained from an official database and provided. The information acquisition unit also builds a system in which the generation AI evaluates the reliability of information in real time and provides more reliable information immediately. For example, the latest industry news from reliable sources is preferentially displayed. This allows the reliability of information to be evaluated and more reliable information to be provided preferentially.

[0074] The information acquisition unit can evaluate the relevance of external information when providing it and provide information related to the user's work. For example, the information acquisition unit uses an algorithm in which the generation AI evaluates the relevance of external information to provide the information most relevant to the user's work. For example, the latest news in industries of interest to the user and trends of competitors are displayed preferentially. The information acquisition unit also evaluates the relevance of external information and builds a system that provides the most relevant information to the user's questions. For example, it provides contact information for business partners along with project information related to the user's work. The information acquisition unit also uses the generation AI to evaluate the relevance of information in real time and instantly provide information related to the user's work. For example, it displays the latest research results related to topics of interest to the user. In this way, by evaluating the relevance of information, it is possible to provide information related to the user's work.

[0075] The information acquisition unit can estimate the user's level of interest using an emotion estimation function and provide information of high interest with priority. For example, the information acquisition unit uses a generation AI to analyze the user's emotional state and estimate the level of interest. For example, information related to topics in which the user is interested is provided with priority. The information acquisition unit also uses the emotion estimation function to build a system that evaluates the user's level of interest in real time and provides information of high interest. For example, if the user is excited, the latest information related to that topic is displayed with priority. The information acquisition unit also provides information of high interest based on the user's emotional state. For example, the latest news in an industry in which the user is interested or trends of competitors is displayed with priority. In this way, by estimating the user's level of interest, information of high interest can be provided with priority.

[0076] The information acquisition unit can provide external information in a news feed format, allowing the user to obtain the latest information in real time. The information acquisition unit, for example, builds a system in which the generation AI provides external information in a news feed format. For example, the latest industry news and competitor trends are displayed in real time. The information acquisition unit also provides answers to user questions in a news feed format. For example, contact information for business partners is incorporated into the news feed. The information acquisition unit also provides external information in a news feed format, allowing the user to obtain the latest information in real time. For example, the latest industry news and competitor trends are displayed in real time. As a result, by providing information in a news feed format, the latest information can be obtained in real time.

[0077] The information acquisition unit provides external information, including information from different industries, thereby broadening the user's perspective. The information acquisition unit, for example, builds a system in which a generation AI collects information from different industries and provides it to the user. For example, it provides the latest news and trend information from industries other than the one the user is interested in. The information acquisition unit also provides answers to user questions, including information from different industries. For example, it provides the latest news from that industry along with contact information for business partners. The information acquisition unit also provides information from different industries, broadening the user's perspective. For example, it provides the latest news and trend information from industries other than the one the user is interested in. This allows the user's perspective to be broadened by providing information from different industries.

[0078] The information acquisition unit uses the emotion estimation function to filter information according to the user's emotions and can provide positive information preferentially. For example, the information acquisition unit uses a generation AI to analyze the user's emotional state and provide positive information preferentially. For example, if the user is feeling stressed, positive news and success stories are preferentially displayed. The information acquisition unit also uses the emotion estimation function to build a system that filters information according to the user's emotions. For example, if the user is feeling anxious, information that gives a sense of security is preferentially provided. The information acquisition unit also preferentially provides positive information based on the user's emotional state. For example, if the user is excited, success stories and positive news related to that topic are preferentially displayed. In this way, positive information can be preferentially provided by filtering information according to the user's emotions.

[0079] The response management unit can automatically set task priorities in managing response items, enabling efficient task management. The response management unit performs efficient task management, for example, by using an algorithm in which the generation AI automatically sets the priority of response items. For example, the priority of tasks is set based on urgency and importance. The response management unit also builds a system in which the generation AI automatically sets task priorities based on user instructions. For example, if a user instructs, "Please reply to this email," the priority of that task is set. The response management unit also performs efficient task management by having the generation AI set task priorities in real time. For example, the priority of tasks is set based on urgency and importance. This enables efficient task management by automatically setting task priorities.

[0080] In managing response items, the response management unit can track the progress of tasks in real time and report it to the user. For example, the response management unit builds a system in which the generation AI tracks the progress of tasks in real time and reports it to the user. For example, the completion status and progress status of tasks are displayed in real time. The response management unit also tracks and reports the progress of tasks in real time based on user instructions. For example, if the user instructs, "Please reply to this email," the progress status of that task is reported. The response management unit also builds a system in which the generation AI tracks the progress of tasks in real time and reports it to the user. For example, the completion status and progress status of tasks are displayed in real time. This improves the efficiency of task management by tracking the progress of tasks in real time and reporting it to the user.

[0081] The response management unit can link the management of response items with a project management tool to manage tasks across the entire team. For example, the response management unit builds a system in which the generation AI links with the project management tool to manage tasks across the entire team. For example, it reflects the progress of tasks and the person in charge in the project management tool. Furthermore, the response management unit builds a system in which the generation AI links with the project management tool based on user instructions to manage tasks across the entire team. For example, if the user instructs, "Please assign this task to Mr. / Ms. XX," it reflects that task in the project management tool. Furthermore, the response management unit builds a system in which the generation AI links with the project management tool to manage tasks across the entire team. For example, it reflects the progress of tasks and the person in charge in the project management tool. In this way, by linking with the project management tool, task management across the entire team can be performed efficiently.

[0082] The response management unit can centralize schedule management by linking the management of response items with a calendar app. For example, the response management unit builds a system in which the generation AI links with a calendar app to centralize schedule management. For example, it reflects meeting schedules and task deadlines in the calendar app. Furthermore, the response management unit builds a system in which the generation AI links with a calendar app based on user instructions to centralize schedule management. For example, if a user instructs, "Please add this meeting to the calendar," it reflects that schedule in the calendar app. Furthermore, the response management unit builds a system in which the generation AI links with a calendar app to centralize schedule management. For example, it reflects meeting schedules and task deadlines in the calendar app. In this way, by linking with the calendar app, schedule management can be centralized.

[0083] The response management unit uses the emotion estimation function to send task reminders according to the user's emotions, thereby maintaining motivation. For example, the response management unit uses a generation AI to analyze the user's emotional state and send task reminders according to the emotions. For example, if the user is feeling stressed, the response management unit sends a reminder including an encouraging message. The response management unit also uses the emotion estimation function to build a system that sends task reminders according to the user's emotions in real time. For example, if the user is feeling impatient, the content of the reminder is adjusted and sent. The response management unit also sends task reminders according to the user's emotional state to maintain motivation. For example, if the user is feeling anxious, the response management unit sends a reminder that gives a sense of security. In this way, motivation can be maintained by sending reminders according to the user's emotions.

[0084] The notification unit can evaluate the importance of notifications in an automated notification function and prioritize sending important notifications. For example, the generation AI uses an algorithm to evaluate the importance of notifications, and the notification unit prioritizes sending important notifications. For example, it prioritizes sending reminders for urgent meetings and notifications of important deadlines. The notification unit also builds a system in which the generation AI evaluates the importance of notifications based on user instructions and prioritizes sending important notifications. For example, if a user instructs the system to "please send this notification as a priority," the system sends that notification as a priority. The notification unit also builds a system in which the generation AI evaluates the importance of notifications in real time and immediately sends important notifications. For example, it prioritizes sending reminders for urgent tasks and notifications for important meetings. This allows important notifications to be sent as a priority by evaluating the importance of notifications.

[0085] The notification unit can optimize the timing of notifications in an automated notification function, thereby improving the work efficiency of users. For example, the generation AI uses an algorithm to optimize the timing of notifications to improve the work efficiency of users. For example, notifications are sent during times when the user is most focused. The notification unit also analyzes the user's schedule and work status to build a system that sends notifications at the optimal timing. For example, notifications are sent during times when the user is not in a meeting. The notification unit also optimizes the timing of notifications in real time using the generation AI to improve the work efficiency of users. For example, a notification for the next task is sent immediately after the user completes a task. In this way, the timing of notifications is optimized, thereby improving the work efficiency of users.

[0086] The notification unit uses an emotion estimation function to provide notifications that take the user's emotional state into consideration, thereby reducing stress. For example, the notification unit uses a generation AI to analyze the user's emotional state and send a notification according to the emotion. For example, if the user is feeling stressed, the notification unit sends a notification that includes an encouraging message. The notification unit also uses the emotion estimation function to build a system that evaluates the user's emotional state in real time and sends notifications to reduce stress. For example, if the user is feeling impatient, the notification unit adjusts and sends the content of a reminder. The notification unit also sends notifications that correspond to the user's emotional state, thereby reducing stress. For example, if the user is feeling anxious, the notification unit sends a notification that gives a sense of security. In this way, stress can be reduced by providing notifications that take the user's emotional state into consideration.

[0087] The notification unit can also make the automated notification function compatible with different devices. For example, the notification unit builds a notification system in which the generation AI is compatible with different devices. For example, it sends notifications to a smartwatch or smart speaker. The notification unit also builds a system that sends notifications to the optimal device depending on the user's device environment. For example, if the user is using a smartwatch, it sends notifications to that device. The notification unit also develops a notification system that is compatible with different devices, allowing the user to receive notifications on any device. For example, it sends notifications simultaneously to a smartphone, smartwatch, and smart speaker. This makes it possible to support different devices, allowing the user to receive notifications on any device.

[0088] The notification unit can make the automated notification function customizable, allowing users to set the content and timing of notifications. For example, the notification unit may build a customizable notification system for the generation AI, allowing users to set the content and timing of notifications. For example, the user may customize the frequency and content of notifications. The notification unit may also build a system in which the generation AI customizes the content and timing of notifications based on user instructions. For example, if a user instructs the generation AI to "send this notification every day at 9:00 AM," the system will set it accordingly. The notification unit may also develop a customizable notification system, allowing users to freely set the content and timing of notifications. For example, the system may allow users to set the content and timing of notifications by dragging and dropping. This allows the content and timing of notifications to be customized, allowing notifications to be delivered according to the user's needs.

[0089] The notification unit uses the emotion estimation function to change the content of the notification according to the user's emotions, thereby eliciting positive emotions. For example, the generation AI in the notification unit analyzes the user's emotional state and changes the content of the notification according to the emotions. For example, if the user is feeling stressed, the notification unit sends a notification including an encouraging message. The notification unit also uses the emotion estimation function to build a system that evaluates the user's emotional state in real time and sends notifications to elicit positive emotions. For example, if the user is feeling impatient, the notification unit adjusts and sends the content of a reminder. The notification unit also changes the content of the notification according to the user's emotional state, thereby eliciting positive emotions. For example, if the user is feeling anxious, the notification unit sends a notification that gives a sense of security. In this way, positive emotions can be elicited by changing the content of the notification according to the user's emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The question analysis unit can refer to the user's past question history to generate more personalized answers. For example, the latest schedule information can be provided based on the user's previous answer to the question, "What is the meeting schedule this week?" The question analysis unit also analyzes the user's past question history to provide more detailed information for frequently asked questions. For example, if a user repeatedly asks, "What is Mr. / Ms. XX's contact information?", related project information can be provided in addition to contact information. The question analysis unit can also learn the user's preferences and interests based on the question history and generate answers accordingly. For example, if a user frequently asks questions about a specific project, the latest information related to that project can be provided preferentially. This allows the system to provide more personalized answers by referring to the user's past question history.

[0092] The question analysis unit can generate answers taking into account the user's current situation. For example, the generation AI obtains the user's location information and provides information related to that location. For example, if the user is outside the office, it can provide information about nearby cafes and conference rooms. The question analysis unit also generates appropriate answers taking into account the user's time of day. For example, if the question is asked at night, "What is the meeting schedule?", it will prioritize providing the next day's schedule. The question analysis unit also analyzes the user's current situation and generates answers accordingly. For example, if it detects that the user is on a business trip, it will provide weather and traffic information for the business trip destination. This allows the system to provide more appropriate answers by taking the user's current situation into account.

[0093] The question analysis unit can use the emotion estimation function to analyze the user's emotional state and generate an answer in a tone that corresponds to the emotion. For example, the generation AI analyzes emotions from the user's input text and generates an answer in a tone that corresponds to the emotion. For example, if the user is feeling stressed, the answer will be in a gentle tone that relaxes the user. The question analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and generate an answer that elicits positive emotions. For example, if the user is feeling anxious, the answer will include words of encouragement. The question analysis unit also adjusts the content of the answer depending on the user's emotional state. For example, if the user is feeling angry, the answer will be in a calm and polite tone and specific suggestions for solving the problem. In this way, by providing answers in a tone that corresponds to the user's emotional state, user satisfaction is improved.

[0094] The question analysis unit can respond to voice input, analyze the question in real time using voice recognition technology, and generate an answer. For example, the generation AI uses voice recognition technology to convert the user's voice input into text, analyze the question based on that text, and generate an answer. For example, if a user asks by voice, "What is the schedule for meetings this week?", the voice is converted into text and an answer is provided. In addition, to respond to voice input, the question analysis unit uses voice recognition technology to analyze the user's pronunciation and accent and perform accurate text conversion. For example, this makes it possible to respond to different dialects and accents. The question analysis unit also builds a system that analyzes voice input in real time and generates answers instantly. For example, if a user asks a question by voice during a meeting, an answer is provided on the spot. This allows users to ask questions and receive answers in a more natural way by supporting voice input.

[0095] The question analysis unit can support different languages, making it usable by international users. For example, the generation AI uses multilingual natural language processing technology to analyze questions in different languages ​​and generate answers in the corresponding language. For example, it supports multiple languages ​​such as English, French, and Chinese. Furthermore, when a user enters a question in a different language, the question analysis unit automatically detects the language and provides an answer in the appropriate language. For example, if a user asks a question in Spanish, it generates an answer in Spanish. Furthermore, the question analysis unit allows the generation AI to build a multilingual database and provide information corresponding to each language so that it can be used by international users. For example, it provides contact information for business partners in multiple languages. This makes it possible to support different languages ​​and make it usable by international users.

[0096] The question analysis unit uses the emotion estimation function to restructure questions according to the user's emotions, thereby providing more appropriate answers. For example, the generation AI analyzes the user's emotional state and restructures questions according to the emotions. For example, if the user is feeling anxious, the question is restructured to be more specific and reassuring. The question analysis unit also uses the emotion estimation function to restructure questions according to the user's emotions, thereby improving the accuracy of answers. For example, if the user is confused, the question is restructured to be concise and clear. The question analysis unit also restructures questions based on the user's emotional state to provide more appropriate answers. For example, if the user is feeling angry, the question is restructured to be calm and specific. In this way, by restructuring questions according to the user's emotions, more appropriate answers can be provided.

[0097] The information acquisition unit can evaluate the importance of internal information when providing it and provide important information on a priority basis. For example, the generation AI uses an algorithm to evaluate the importance of internal information and provide important information on a priority basis. For example, it prioritizes displaying highly urgent contact information and project progress status. The information acquisition unit also evaluates the importance of internal information and provides the information most relevant to a user's question. For example, if a user asks, "What are the important meetings this week?", it prioritizes providing information about meetings with high importance. The information acquisition unit also builds a system in which the generation AI evaluates the importance of information in real time and provides important information immediately. For example, it prioritizes displaying urgent contact information and important project progress information. In this way, important information can be provided on a priority basis by evaluating the importance of internal information.

[0098] The information acquisition unit can provide the latest information when providing internal company information, taking into account how often the information is updated. For example, the generation AI analyzes how often internal company information is updated and provides the latest information as a priority. For example, the progress of a frequently updated project is displayed in real time. The information acquisition unit also takes into account how often the information is updated and builds a system that provides the latest information in response to user questions. For example, if an employee's contact information is updated, the latest contact information is provided. The information acquisition unit also monitors how often the information is updated in real time and provides the latest information immediately. For example, if internal company regulations are changed, the latest regulation information is displayed as a priority. This allows the latest information to be always provided by taking into account how often the information is updated.

[0099] The information acquisition unit can provide internal company information as visual data, making it easier to understand visually. For example, a system can be built in which a generative AI provides internal company information as visual data. For example, the progress of a project can be displayed in graphs and charts. The information acquisition unit can also provide answers to user questions using visual data. For example, employee contact information can be displayed as an organizational chart. The information acquisition unit can also use visual data to make internal company information easier to understand visually. For example, changes to internal company regulations can be displayed in a chart. In this way, the use of visual data makes internal company information easier to understand visually.

[0100] The information acquisition unit uses the emotion estimation function to change the order in which information is presented according to the user's emotions, thereby reducing stress. For example, the generation AI analyzes the user's emotional state and changes the order in which information is presented. For example, if the user is feeling stressed, important information is displayed first. The information acquisition unit also uses the emotion estimation function to build a system that changes the order in which information is presented according to the user's emotions in real time. For example, if the user is feeling anxious, information with a high level of urgency is displayed first. The information acquisition unit also changes the order in which information is presented based on the user's emotional state to reduce stress. For example, if the user is feeling anxious, information that gives a sense of security is displayed first. In this way, stress can be reduced by changing the order in which information is presented according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The question analysis unit uses the generation AI to analyze the question from the user and provide an appropriate answer. For example, the generation AI may use a text generation AI (e.g., GPT-3) to analyze the question and generate an answer. The generation AI may also use natural language processing technology to understand the intent of the question and provide an appropriate answer. Furthermore, the generation AI may use keyword extraction technology to analyze the important parts of the question and generate an answer. Step 2: The information acquisition unit acquires the necessary information from internal and external sources based on the questions analyzed by the question analysis unit. For example, the information acquisition unit may access an internal database to acquire employee contact information. The information acquisition unit may also use an external API to acquire the latest industry news. Furthermore, the information acquisition unit may use web scraping technology to acquire contact information for business partners. Step 3: The response management unit manages responses from users based on the information acquired by the information acquisition unit and takes appropriate action. For example, the response management unit replies to emails based on instructions from users. The response management unit can also use a task management system to manage the progress of responses and report it to users. Furthermore, the response management unit can use a reminder function to notify users of deadlines for important tasks.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A question analysis unit that uses generation AI to analyze questions from users and provide appropriate answers; an information acquisition unit that acquires necessary information from internal and external information sources based on the question analyzed by the question analysis unit; a response management unit that manages response items from users based on the information acquired by the information acquisition unit and takes appropriate responses. A system characterized by:

2. The question analysis unit Refer to the user's past question history to generate a more personalized answer 2. The system of claim 1.

3. The question analysis unit Responds to voice input and uses voice recognition technology to analyze the question in real time and generate the answer 2. The system of claim 1.

4. The information acquisition unit When providing internal company information, evaluate the importance of the information and provide the important information with priority.

2. The system of claim 1.

5. The response management unit In managing the above-mentioned correspondence matters, task priorities are automatically set and efficient task management is performed.

2. The system of claim 1.

6. The question analysis unit Analyzing the emotional state of the user and generating the response in a tone corresponding to the emotion.

2. The system of claim 1.

7. The information acquisition unit The urgency of the information desired by the user is estimated, and information is provided according to the urgency.

2. The system of claim 1.

8. The response management unit Estimate the stress level of the user and perform task management to reduce stress.

2. The system of claim 1.

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